# datoon - Doramagic AI Context Pack

> Positioning: a pre-install experience and judgment asset. It helps the host AI get off to a good start, but it does not mean the project has already been installed, run, or validated.

## Sufficiency Principle

- **Sufficiency over compression**: The AI Context Pack should be sufficient for the host AI to understand the project's value, capability boundaries, entrypoints, risks, and evidence sources before starting work; it may be layered, but it does not aim for the shortest possible summary.
- **Compression policy**: Compress only noise and duplication, never context that affects judgment or the quality of the work.

## How the Host AI Should Use This

You are reading the AI Context Pack that Doramagic compiled for datoon. Treat it as pre-work context: help the user understand who it fits, what it can do, how to start, what must be verified after install, and where the risks are. Do not claim that you have already installed, run, or executed the target project.

## Claim Consumption Rules

- **Fact source**: Repo Evidence + Claim/Evidence Graph; the Human Wiki only supplies salience, terminology, and narrative structure.
- **Minimum status for a fact**: `supported`
- `supported`: May be used as a project fact, but the answer must cite the claim_id and evidence path.
- `weak`: Usable only as a low-confidence lead; the user must be asked to keep verifying.
- `inferred`: Usable only for risk notes or open questions; must not be packaged as a project fact.
- `unverified`: Must not be used as fact; state clearly that evidence is insufficient.
- `contradicted`: Must show the conflicting sources and must not force a single version on the user's behalf.

## Who It Fits Best

- **Developers already using host AIs such as Claude/Codex/Cursor/Gemini**: The README or plugin config mentions multiple host AIs. Evidence: `README.md` Claim: `clm_0004` supported 0.86
- **Users who want to bring professional workflows into a host AI**: The repo contains Skill documents. Evidence: `datoon/SKILL.md`, `plugins/datoon/skills/datoon/SKILL.md`, `skills/datoon/SKILL.md` Claim: `clm_0005` supported 0.86

## What It Can Do

- **AI Skill / Agent Instruction Asset Library** (Previewable before install): The project contains Skill or Agent instruction files that a host AI can read, useful for bringing professional workflows into hosts like Claude, Codex, or Cursor. Evidence: `datoon/SKILL.md`, `plugins/datoon/skills/datoon/SKILL.md`, `skills/datoon/SKILL.md`, `SKILL.md` Claim: `clm_0001` supported 0.86
- **Multi-Host Install and Distribution** (Verify after install): The project contains plugin or marketplace configuration, indicating it targets install and distribution across one or more AI hosts. Evidence: `.agents/plugins/marketplace.json`, `.claude-plugin/marketplace.json`, `.claude-plugin/plugin.json`, `plugins/datoon/.codex-plugin/plugin.json` Claim: `clm_0002` supported 0.86
- **Command-Line Startup or Install Flow** (Verify after install): The project documentation contains runnable commands; real use requires running them in a local or host environment. Evidence: `INSTALL.md`, `README.md` Claim: `clm_0003` supported 0.86

## How to Start

- `git clone https://github.com/andrii-su/datoon.git` Evidence: `INSTALL.md` Claim: `clm_0006` supported 0.86
- `pip install datoon` Evidence: `INSTALL.md` Claim: `clm_0007` supported 0.86, `clm_0008` supported 0.86, `clm_0009` supported 0.86, `clm_0010` supported 0.86 et al.
- `pip install "datoon[tokens]"  # tiktoken token estimates` Evidence: `INSTALL.md` Claim: `clm_0008` supported 0.86
- `pip install "datoon[mcp]"     # MCP server dependencies` Evidence: `INSTALL.md` Claim: `clm_0009` supported 0.86
- `pip install "datoon[mcp]"` Evidence: `INSTALL.md` Claim: `clm_0009` supported 0.86, `clm_0010` supported 0.86
- `npx --version` Evidence: `INSTALL.md` Claim: `clm_0011` supported 0.86
- `pip install "datoon[yaml]"` Evidence: `README.md` Claim: `clm_0012` supported 0.86
- `pip install "datoon[excel]"` Evidence: `README.md` Claim: `clm_0013` supported 0.86
- `pip install "datoon[columnar]"` Evidence: `README.md` Claim: `clm_0014` supported 0.86
- `pip install "datoon[numbers]"` Evidence: `README.md` Claim: `clm_0015` supported 0.86

## Continue-or-Stop Decision Card

- **Current recommendation**: Sandbox trial only
- **Why**: The project has signals of install commands, host configuration, or local writes; do not go straight into your primary environment—trial it in isolation first.

### 30-Second Read

- **What to do now**: Sandbox trial only
- **Minimum safe next step**: Run Prompt Preview first; if you still want to install, trial only in an isolated environment
- **Do not trust yet**: Real output quality cannot be trusted before install.
- **Continuing will touch**: Command execution, Host AI configuration, Local environment or project files

### What You Can Trust Now

- **Target-audience signal: Developers already using host AIs such as Claude/Codex/Cursor/Gemini** (supported): Backed by a supported claim or project evidence, but that still is not the same as real install results. Evidence: `README.md` Claim: `clm_0004` supported 0.86
- **Target-audience signal: Users who want to bring professional workflows into a host AI** (supported): Backed by a supported claim or project evidence, but that still is not the same as real install results. Evidence: `datoon/SKILL.md`, `plugins/datoon/skills/datoon/SKILL.md`, `skills/datoon/SKILL.md` Claim: `clm_0005` supported 0.86
- **Capability exists: AI Skill / Agent Instruction Asset Library** (supported): You can trust that the project contains signals of this capability; whether it fits your specific task still needs trial or after-install verification. Evidence: `datoon/SKILL.md`, `plugins/datoon/skills/datoon/SKILL.md`, `skills/datoon/SKILL.md`, `SKILL.md` Claim: `clm_0001` supported 0.86
- **Capability exists: Multi-Host Install and Distribution** (supported): You can trust that the project contains signals of this capability; whether it fits your specific task still needs trial or after-install verification. Evidence: `.agents/plugins/marketplace.json`, `.claude-plugin/marketplace.json`, `.claude-plugin/plugin.json`, `plugins/datoon/.codex-plugin/plugin.json` Claim: `clm_0002` supported 0.86
- **Capability exists: Command-Line Startup or Install Flow** (supported): You can trust that the project contains signals of this capability; whether it fits your specific task still needs trial or after-install verification. Evidence: `INSTALL.md`, `README.md` Claim: `clm_0003` supported 0.86
- **There are Quick Start / install-command signals** (supported): You can trust that the docs mention a startup or install entrypoint; do not run it directly in your primary environment because of that. Evidence: `INSTALL.md` Claim: `clm_0006` supported 0.86

### What You Cannot Trust Yet

- **Real output quality cannot be trusted before install.** (unverified): Prompt Preview can only show how it guides you; it cannot prove result quality in the real project.
- **Host AI version compatibility cannot be trusted before install.** (unverified): Host loading rules and version differences across Claude, Cursor, Codex, Gemini, and others must be verified in a real environment.
- **That it will not pollute your existing host AI's behavior cannot be trusted directly.** (inferred): Skill, plugin, and AGENTS/CLAUDE/GEMINI instructions may change the host AI's default behavior. Evidence: `.agents/plugins/marketplace.json`, `.claude-plugin/marketplace.json`, `.claude-plugin/plugin.json`, `CLAUDE.md` et al.
- **Safe rollback cannot be assumed by default.** (unverified): Unless the project clearly provides uninstall and recovery instructions, verify in an isolated environment first.
- **After a real install, is it compatible with the user's current host AI version?** (unverified): Compatibility can only be verified in the actual host environment. Evidence: `.agents/plugins/marketplace.json`, `.claude-plugin/marketplace.json`, `.claude-plugin/plugin.json`, `plugins/datoon/.codex-plugin/plugin.json`
- **Does the project's output quality meet the user's specific task?** (unverified): The pre-install preview can only show flow and boundaries; it cannot replace real evaluation.
- **Do the install commands require network access, permissions, or global writes?** (unverified): This affects install risk in both enterprise and personal environments. Evidence: `INSTALL.md`

### What Continuing Will Touch

- **Command execution**: Package managers, network downloads, the local plugin directory, project config, or the user's home directory. Why: Running the very first command can already change your environment; decide whether it is worth running first. Evidence: `INSTALL.md`, `README.md`
- **Host AI configuration**: The plugin, Skill, or rule-loading config of hosts like Claude/Codex/Cursor/Gemini/OpenCode. Why: Host configuration changes how the AI works afterward and may conflict with the user's existing rules. Evidence: `.agents/plugins/marketplace.json`, `.claude-plugin/marketplace.json`, `.claude-plugin/plugin.json`, `CLAUDE.md` et al.
- **Local environment or project files**: Install results, plugin caches, project config, or local dependency directories. Why: The write scope and rollback path cannot be proven before install and need isolated verification. Evidence: `.agents/plugins/marketplace.json`, `.claude-plugin/marketplace.json`, `.claude-plugin/plugin.json`, `INSTALL.md` et al.
- **Host AI context**: The AI Context Pack, Prompt Preview, Skill routing, risk rules, and project facts. Why: Importing context affects the host AI's later judgment, so avoid packaging unverified items as facts.

### Minimum Safe Next Steps

- **Run Prompt Preview first**: Use a pre-install interactive trial to judge whether the way of working fits; it needs no authorization or environment change. (applies when: Applies to any project, especially when output quality is unknown.)
- **Trial-install only in an isolated directory or a test account**: Avoid letting install commands pollute your primary host AI, real projects, or home directory. (applies when: When there are signals of command execution, plugin config, or local writes.)
- **Back up your host AI configuration first**: Skill, plugin, and rule files may change the default behavior of Claude/Cursor/Codex. (applies when: When there is a plugin manifest, a Skill, or a host rule entrypoint.)
- **After install, verify just one minimal task**: Verify loading, compatibility, output quality, and rollback first, then decide whether to use it deeply. (applies when: When moving from a trial into a real workflow.)

### Exit Plan

- **Preserve the pre-install state**: Record the original host config and project state so you can later judge whether it is recoverable.
- **Be ready to remove the host plugin / Skill / rule entrypoint**: If behavior is off after the trial install, you can restore the host AI to its pre-trial state.
- **Record the install commands and written paths**: Without clear uninstall instructions, you at least need to know which directories or configs to clean up manually.
- **If there is no rollback path, do not enter your primary environment**: No rollback is a blocker before continuing; do not proceed on trust or luck.

## What Can Only Be Previewed

- Explain who the project fits and what it can do
- Demonstrate a typical conversation flow based on project docs
- Help the user decide whether it is worth installing or researching further

## What Must Be Verified After Install

- Actually installing the Skill, plugin, or CLI
- Running scripts, modifying local files, or accessing external services
- Verifying real output quality, performance, and compatibility

## Boundary & Risk Decision Card

- **Mistaking the pre-install preview for a real run**: The user may overestimate how much configuration, permission, and compatibility verification the project has already done. Mitigation: Clearly separate prompt_preview_can_do from runtime_required. Claim: `clm_0018` inferred 0.45
- **Host AI plugin or Skill rule conflicts**: New rules may change how the user's existing host AI behaves. Mitigation: Inspect the plugin manifest and Skill files before installing, and test in isolation if needed. Evidence: `.agents/plugins/marketplace.json`, `.claude-plugin/marketplace.json`, `.claude-plugin/plugin.json`, `plugins/datoon/.codex-plugin/plugin.json` Claim: `clm_0019` supported 0.86
- **Command execution will modify the local environment**: Install commands may write to the user's home directory, the host plugin directory, or project configuration. Mitigation: Run in an isolated environment or a test account first. Evidence: `INSTALL.md`, `README.md` Claim: `clm_0020` supported 0.86
- **To confirm**: After a real install, is it compatible with the user's current host AI version?. Why: Compatibility can only be verified in the actual host environment.
- **To confirm**: Does the project's output quality meet the user's specific task?. Why: The pre-install preview can only show flow and boundaries; it cannot replace real evaluation.
- **To confirm**: Do the install commands require network access, permissions, or global writes?. Why: This affects install risk in both enterprise and personal environments.

## Pre-Work Working Context

### Loading Order

- First read how_to_use.host_ai_instruction to establish the boundaries of this pre-install judgment asset.
- Read claim_graph_summary to confirm facts come from the Claim/Evidence Graph, not the Human Wiki narrative.
- Then read intended_users, capabilities, and quick_start_candidates to judge whether the user is a match.
- When you need to carry out a concrete task, check role_skill_index first, then evidence_index.
- For real install, file modification, network access, performance, or compatibility questions, turn to risk_card and boundaries.runtime_required.

### Task Routes

- **AI Skill / Agent Instruction Asset Library**: Use role_skill_index / evidence_index to help the user pick a usable role, Skill, or workflow first. Boundary: Can be experienced via a pre-install Prompt. Evidence: `datoon/SKILL.md`, `plugins/datoon/skills/datoon/SKILL.md`, `skills/datoon/SKILL.md`, `SKILL.md` Claim: `clm_0001` supported 0.86
- **Multi-Host Install and Distribution**: State that this is an after-install capability first, then give a pre-install checklist. Boundary: Must be verified after a real install or run. Evidence: `.agents/plugins/marketplace.json`, `.claude-plugin/marketplace.json`, `.claude-plugin/plugin.json`, `plugins/datoon/.codex-plugin/plugin.json` Claim: `clm_0002` supported 0.86
- **Command-Line Startup or Install Flow**: State that this is an after-install capability first, then give a pre-install checklist. Boundary: Must be verified after a real install or run. Evidence: `INSTALL.md`, `README.md` Claim: `clm_0003` supported 0.86

### Context Scale

- Total files: 92
- Important-file coverage: 40/92
- Evidence index entries: 75
- Role / Skill entries: 3

### Handling Insufficient Evidence

- **missing_evidence**: State that evidence is insufficient and ask the user for the target file, a README section, or after-install verification records; do not fill in facts.
- **out_of_scope_request**: State that the task is beyond the current AI Context Pack's evidence scope and suggest the user check the Human Manual or verify after a real install.
- **runtime_request**: Provide a pre-install checklist and command sources, but do not run commands for the user or claim they have been run.
- **source_conflict**: Show the conflicting sources side by side, mark them as unverified, and do not force a single version.

## Prompt Recipes

### Fit assessment

- Goal: Judge whether this project fits the user's current task.
- Expected output: A fit conclusion, key reasons, evidence citations, what can be previewed before install, what must be verified after install, and a next-step recommendation.

```text
Based on the AI Context Pack for datoon, ask me 3 necessary questions first, then judge whether it fits my task. The answer must cover: who it fits, what it can do, what it cannot do, whether it is worth installing, and where the evidence comes from. Every project fact must cite evidence_refs, source_paths, or a claim_id.
```

### Pre-install experience

- Goal: Let the user feel the core workflow before installing, while avoiding packaging the preview as real capability or a marketing promise.
- Expected output: An experience script with boundary labels, an after-install verification checklist, and a cautious recommendation; with no real-run promises or strong marketing language.

```text
Treat datoon as a pre-install experience asset, not an already-installed tool or a real runtime environment.

Output exactly four parts:
1. Ask me 3 necessary questions first.
2. Give an "experience script": use the three labels [Previewable before install], [Must verify after install], and [Insufficient evidence] to show how it might guide the workflow.
3. Give an after-install verification checklist: list which capabilities can only be confirmed after a real install, real host loading, and a real project run.
4. Give a cautious recommendation: only "worth researching/trialing further", "add information before deciding", or "not recommended to continue"; do not endorse the project.

Hard boundaries:
- Do not claim you have installed, run, executed tests, modified files, or produced real results.
- Do not write promise-like phrasing such as "auto-adapts", "guarantees passing", "perfect fit", or "strongly recommend installing".
- If you describe how it works after install, you must use a conditional such as "if installed successfully and the host loads the Skill correctly, it might...".
- The experience script may only be written as "example lines / hypothetical flow": use "might ask / might suggest / might show", not "has written, has generated, has passed, is running, is generating".
- Prompt Preview does not hand out install commands; if the user is ready to trial, only prompt them to read Quick Start and the Risk Card first and to verify in an isolated environment.
- Every project fact must come from a supported claim, evidence_refs, or source_paths; inferred/unverified items can only be risks or open questions.

```

### Role / Skill selection

- Goal: Pick the best-matching asset from the project's roles or Skills.
- Expected output: A list of candidate roles or Skills, each with an applicable scenario, evidence paths, risk boundary, and whether after-install verification is needed.

```text
Read role_skill_index and recommend 3-5 of the most relevant roles or Skills for my target task. For each recommendation, state the applicable scenario, likely output, risk boundary, and evidence_refs.
```

### Risk pre-check

- Goal: Identify environment, permission, rule-conflict, and quality risks before installing or adopting.
- Expected output: A checklist of environment, permission, dependency, license, host-conflict, quality risk, and unknown items.

```text
Based on risk_card, boundaries, and quick_start_candidates, give me a pre-install risk pre-check list. Do not run commands for me; only explain what I should check, why, and what impact a failure would have.
```

### Host AI kickoff instruction

- Goal: Turn the project context into a host AI instruction for the start of a conversation.
- Expected output: A pre-work instruction with clear boundaries and clear evidence citations, suitable to copy to a host AI.

```text
Based on the AI Context Pack for datoon, generate a pre-work instruction I can paste to my host AI. This instruction must obey not_runtime=true and must not claim the project has been installed, run, or produced real results.
```

## Role / Skill Index

- Indexed 3 role / Skill / project-doc entries.

- **datoon** (skill): Smart TOON conversion workflow for structured data in Claude Code. Converts JSON-like payloads to TOON only when structure is suitable and token savings are meaningful. Activation hint: When the user's task is highly relevant to the workflow described by “datoon”, use it for a pre-install experience first, then decide whether to install. Evidence: `datoon/SKILL.md`
- **datoon** (skill): Smart TOON conversion workflow for structured data in Claude Code. Converts JSON-like payloads to TOON only when structure is suitable and token savings are meaningful. Activation hint: When the user's task is highly relevant to the workflow described by “datoon”, use it for a pre-install experience first, then decide whether to install. Evidence: `plugins/datoon/skills/datoon/SKILL.md`
- **datoon** (skill): Smart TOON conversion workflow for structured data in Claude Code. Converts JSON-like payloads to TOON only when structure is suitable and token savings are meaningful. Activation hint: When the user's task is highly relevant to the workflow described by “datoon”, use it for a pre-install experience first, then decide whether to install. Evidence: `skills/datoon/SKILL.md`

## Evidence Index

- Indexed 75 evidence entries.

- **Before / After** (documentation): smart structured-data→TOON gateway — converts only when it actually saves tokens Evidence: `README.md`
- **datoon Skill** (documentation): Smart TOON conversion workflow for structured data in AI-agent sessions. Evidence: `skills/datoon/README.md`
- **Marketplace** (structured_config): { "$schema": "https://anthropic.com/claude-code/marketplace.schema.json", "name": "datoon", "description": "Smart JSON-to-TOON conversion with pragmatic auto-gating for LLM prompts.", "owner": { "name": "Andrii Suruhov", "url": "https://github.com/andrii-su" }, "plugins": { "name": "datoon", "description": "Auto-convert structured JSON to TOON only when token savings are meaningful.", "source": "./", "category": "productivity" } } Evidence: `.claude-plugin/marketplace.json`
- **Plugin** (structured_config): { "name": "datoon", "version": "1.4.1", "description": "Smart JSON-to-TOON conversion with pragmatic auto-gating for LLM prompts.", "author": { "name": "Andrii Suruhov", "url": "https://github.com/andrii-su" }, "homepage": "https://github.com/andrii-su/datoon", "repository": "https://github.com/andrii-su/datoon", "license": "MIT", "keywords": "llm", "prompt-engineering", "json", "toon", "data" } Evidence: `.claude-plugin/plugin.json`
- **Marketplace** (structured_config): { "name": "datoon-repo", "interface": { "displayName": "Datoon Repo" }, "plugins": { "name": "datoon", "source": { "source": "local", "path": "./plugins/datoon" }, "policy": { "installation": "AVAILABLE", "authentication": "ON INSTALL" }, "category": "Productivity" } } Evidence: `.agents/plugins/marketplace.json`
- **Plugin** (structured_config): { "name": "datoon", "version": "1.4.1", "description": "Smart JSON-to-TOON conversion with pragmatic auto-gating for LLM prompts.", "author": { "name": "Andrii Suruhov", "url": "https://github.com/andrii-su" }, "homepage": "https://github.com/andrii-su/datoon", "repository": "https://github.com/andrii-su/datoon", "license": "MIT", "keywords": "llm", "prompt-engineering", "json", "toon", "data" , "skills": "./skills/", "interface": { "displayName": "Datoon", "shortDescription": "Auto-convert JSON to TOON when token savings are meaningful.", "longDescription": "Smart structured-data mode for Codex that converts JSON to TOON only when payload shape and estimated savings justify it.", "develope… Evidence: `plugins/datoon/.codex-plugin/plugin.json`
- **CLAUDE.md - datoon** (documentation): This file is the maintainer guide for agents working in this repository. It explains source-of-truth files, generated mirrors, and checks that must stay green. Evidence: `CLAUDE.md`
- **Contributing to datoon** (documentation): Thanks for considering a contribution. datoon is a small package with several distribution surfaces: Python API, CLI, MCP server, Claude Code plugin, Codex skill/plugin, benchmark artifacts, and docs. Evidence: `CONTRIBUTING.md`
- **Install datoon** (documentation): datoon ships as a Python package, command-line tool, MCP server, and AI-agent skill/plugin. Use the path that matches how you want to consume it. Evidence: `INSTALL.md`
- **datoon** (skill_instruction): Before sending structured payloads to the model: Evidence: `SKILL.md`
- **datoon** (skill_instruction): Before sending structured payloads to the model: Evidence: `datoon/SKILL.md`
- **datoon** (skill_instruction): Before sending structured payloads to the model: Evidence: `plugins/datoon/skills/datoon/SKILL.md`
- **datoon** (skill_instruction): Before sending structured payloads to the model: Evidence: `skills/datoon/SKILL.md`
- **License** (source_file): Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files the "Software" , to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: Evidence: `LICENSE`
- **1.9.1 https://github.com/andrii-su/datoon/compare/v1.9.0...v1.9.1 2026-07-07** (documentation): 1.9.1 https://github.com/andrii-su/datoon/compare/v1.9.0...v1.9.1 2026-07-07 Evidence: `CHANGELOG.md`
- **Pyproject** (source_file): build-system requires = "setuptools =82.0.1", "setuptools-scm toml =10.0.5", "wheel" build-backend = "setuptools.build meta" Evidence: `pyproject.toml`
- **Init** (source_file): all = ⋮---- version = version "datoon" ⋮---- version = "0.0.0" Evidence: `src/datoon/__init__.py`
- **Analyzer** (source_file): def max depth value: Any, depth: int = 1 - int ⋮---- def iter arrays value: Any - Iterator list Any ⋮---- def is uniform object array items: list Any , min rows: int - bool ⋮---- first keys = list items 0 .keys ⋮---- def analyze payload data: Any, config: ConversionConfig - PayloadAnalysis ⋮---- max depth = max depth data uniform array count = sum Evidence: `src/datoon/analyzer.py`
- **Cli** (source_file): def build parser - argparse.ArgumentParser ⋮---- parser = argparse.ArgumentParser ⋮---- def read input path: str None - str ⋮---- def write text path: str None, text: str - None ⋮---- report json = json.dumps payload, ensure ascii=False, indent=2 ⋮---- def resolve format input path: str None, format override: str None - str ⋮---- detected = detect format input path ⋮---- def run mcp server - int ⋮---- def main argv: Sequence str None = None - int ⋮---- raw argv = list sys.argv 1: if argv is None else argv ⋮---- parser = build parser args = parser.parse args argv ⋮---- config = ConversionConfig ⋮---- fmt = resolve format args.input, args.format ⋮---- raw input = read input args.input outcome… Evidence: `src/datoon/cli.py`
- **Converter** (source_file): TOON CLI PACKAGE = "@toon-format/cli@2" ⋮---- all = "DatoonError", "convert json for llm", "estimate tokens" ⋮---- def reject non finite constant: str - Any ⋮---- def normalize json raw text: str - tuple Any, str ⋮---- """Parse and re-serialize JSON into a deterministic compact representation.""" ⋮---- parsed = json.loads raw text, parse constant= reject non finite ⋮---- normalized = json.dumps ⋮---- @lru cache maxsize=8 def load token encoder encoding: str - Any None ⋮---- """Load a token encoder once per name, None when the optional dep is absent.""" ⋮---- import tiktoken type: ignore ⋮---- def estimate tokens text: str, encoding: str = DEFAULT TOKEN ENCODING - int ⋮---- """Estimate token… Evidence: `src/datoon/converter.py`
- **Errors** (source_file): class DatoonError RuntimeError Evidence: `src/datoon/errors.py`
- **Mcp Server** (source_file): mcp = FastMCP ⋮---- config = ConversionConfig outcome = convert json for llm json text, config ⋮---- """Analyze a JSON payload and report whether it is a TOON candidate. Does not invoke the TOON CLI — safe to call without Node.js installed. """ ⋮---- parsed = json.loads json text ⋮---- analysis = analyze payload parsed, config ⋮---- rows = read tabular fmt, text=text json text = json.dumps rows, ensure ascii=False, separators= ",", ":" ⋮---- def main - None Evidence: `src/datoon/mcp_server.py`
- **Models** (source_file): Decision = Literal "convert", "skip" ⋮---- DEFAULT TOKEN ENCODING = "o200k base" ⋮---- @dataclass frozen=True, slots=True class ConversionConfig ⋮---- min savings ratio: float = 0.15 max depth: int = 6 min uniform rows: int = 3 force: bool = False toon cli timeout: int = 30 token encoding: str = DEFAULT TOKEN ENCODING ⋮---- def post init self - None ⋮---- @dataclass frozen=True, slots=True class PayloadAnalysis ⋮---- is candidate: bool reason: str max depth: int uniform array count: int ⋮---- @dataclass frozen=True, slots=True class ConversionReport ⋮---- decision: Decision ⋮---- was forced: bool input token estimate: int output token estimate: int savings ratio: float analysis: PayloadAnal… Evidence: `src/datoon/models.py`
- **Init** (source_file): BINARY FORMATS: frozenset str = frozenset TEXT FORMATS: frozenset str = frozenset {"csv", "jsonl", "yaml", "xml"} ALL FORMATS: frozenset str = BINARY FORMATS TEXT FORMATS ⋮---- EXTENSION MAP: dict str, str = { ⋮---- def detect format path: str Path - str None ⋮---- def read text fmt: str, text: str - list dict str, Any Evidence: `src/datoon/readers/__init__.py`
- **Coerce** (source_file): FLOAT RE = re.compile r"^ +- ? ?:0 1-9 \d ?:\.\d+ ? ?: eE +- ?\d+ ?$" ⋮---- def coerce scalar value: str - int float bool str None ⋮---- """Coerce a raw string cell to a JSON-safe scalar without losing meaning.""" stripped = value.strip ⋮---- low = stripped.lower ⋮---- as int = int stripped ⋮---- as float = float stripped Evidence: `src/datoon/readers/_coerce.py`
- **Tabular** (source_file): def header rows to dicts rows: list Any - list dict str, Any ⋮---- data rows = rows 1: ⋮---- widest = max len row for row in data rows , default=0 header row = rows 0 column count = max len header row , widest ⋮---- headers: list str = ⋮---- raw = header row i if i list Any ⋮---- cells = list row Evidence: `src/datoon/readers/_tabular.py`
- **Csv** (source_file): def read csv text: str, , coerce types: bool = True - list dict str, Any ⋮---- reader = csv.DictReader io.StringIO text Evidence: `src/datoon/readers/csv.py`
- **Jsonl** (source_file): def read jsonl text: str - list dict str, Any ⋮---- rows: list dict str, Any = ⋮---- line = line.strip ⋮---- obj = json.loads line Evidence: `src/datoon/readers/jsonl.py`
- **Xml** (source_file): DOCTYPE RE = re.compile r" list dict str, Any ⋮---- root = ET.fromstring text ⋮---- children = list root ⋮---- tag counts: dict str, int = {} ⋮---- dominant tag = max tag counts, key=lambda t: tag counts t items = c for c in children if c.tag == dominant tag ⋮---- def element to dict element: ET.Element - dict str, Any ⋮---- result: dict str, Any = {k: coerce v for k, v in element.attrib.items } Evidence: `src/datoon/readers/xml.py`
- **Yaml** (source_file): SHAPE ERROR = ⋮---- def read yaml text: str - list dict str, Any ⋮---- data = yaml.safe load text ⋮---- def normalize data: Any - list dict str, Any ⋮---- rows: list Any None = None ⋮---- rows = data ⋮---- lists = v for v in data.values if isinstance v, list and v ⋮---- rows = lists 0 Evidence: `src/datoon/readers/yaml.py`
- **Marketplace & Registry Listings** (documentation): How datoon is distributed across MCP marketplaces, what is automated, and the one-time manual steps a maintainer performs. Evidence: `MARKETPLACES.md`
- **Security Policy** (documentation): Security fixes are applied to the latest main branch state. Evidence: `SECURITY.md`
- **Agent Skill Evaluation Report** (documentation): Compare agent behavior on the same structured-data analysis tasks with and without the datoon skill. Evidence: `benchmarks/agent_skill_eval/REPORT.md`
- **Payloads** (structured_config): { "payloads": { "id": "users-small", "category": "uniform-array", "description": "Small uniform records", "data": { "users": { "id": 1, "name": "Ada", "role": "admin", "active": true }, { "id": 2, "name": "Lin", "role": "analyst", "active": true }, { "id": 3, "name": "Sam", "role": "viewer", "active": false } } }, { "id": "events-medium", "category": "uniform-array", "description": "Event table with timestamps and dimensions", "data": { "events": { "ts": "2026-01-01T00:00:00Z", "event": "page view", "user id": 1001, "country": "US", "is mobile": true }, { "ts": "2026-01-01T00:01:00Z", "event": "signup", "user id": 1002, "country": "DE", "is mobile": false }, { "ts": "2026-01-01T00:02:00Z",… Evidence: `benchmarks/payloads.json`
- **Glama** (structured_config): { "$schema": "https://glama.ai/mcp/schemas/server.json", "maintainers": "andrii-su" } Evidence: `glama.json`
- **Server** (structured_config): { "$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json", "name": "io.github.andrii-su/datoon", "title": "datoon", "description": "Smart structured-data to TOON gateway: converts to TOON only when it saves LLM tokens.", "version": "1.6.0", "websiteUrl": "https://github.com/andrii-su/datoon", "repository": { "url": "https://github.com/andrii-su/datoon", "source": "github" }, "packages": { "registryType": "pypi", "registryBaseUrl": "https://pypi.org", "identifier": "datoon", "version": "1.6.0", "runtimeHint": "uvx", "runtimeArguments": { "type": "named", "name": "--from", "value": "datoon mcp " } , "packageArguments": { "type": "positional", "value": "mcp" }… Evidence: `server.json`
- **Agent Results** (structured_config): { "variant": "with skill", "agent id": "019e6b45-6723-75c0-a2da-5a5af19635ac", "agent nickname": "Bacon", "payload name": "1 small.json", "result": { "mode": "convert", "payload file": "/Users/andriisuruhov/github/datoon/benchmarks/agent skill eval/payloads/1 small.json", "scenario": "small", "iteration": 1, "record count": 5, "active count": 4, "total revenue cents": 1385, "top region": "west", "top category": "hardware", "anomaly ids": "s-1-0001", "s-1-0005" , "elapsed seconds": 1.247206375002861, "notes": "datoon decision=convert; Estimated savings 47.79% threshold 15.00% ." } }, { "variant": "without skill", "agent id": "019e6b45-79f7-7033-a490-ec76d7a1817e", "agent nickname": "Euler",… Evidence: `benchmarks/agent_skill_eval/agent_results.json`
- **Expected Answers** (structured_config): { "1 small.json": { "scenario": "small", "iteration": 1, "record count": 5, "active count": 4, "total revenue cents": 1385, "top region": "west", "top category": "hardware", "anomaly ids": "s-1-0001", "s-1-0005" }, "1 medium.json": { "scenario": "medium", "iteration": 1, "record count": 75, "active count": 60, "total revenue cents": 175310, "top region": "west", "top category": "services", "anomaly ids": "m-1-0001", "m-1-0018", "m-1-0035", "m-1-0052", "m-1-0069" }, "1 large.json": { "scenario": "large", "iteration": 1, "record count": 450, "active count": 360, "total revenue cents": 4242985, "top region": "west", "top category": "services", "anomaly ids": "l-1-0001", "l-1-0098", "l-1-0195",… Evidence: `benchmarks/agent_skill_eval/expected_answers.json`
- **1 Large** (structured_config): { "scenario": "large", "iteration": 1, "source": "datoon agent skill evaluation", "records": { "id": "l-1-0001", "region": "south", "category": "software", "active": true, "units": 2, "revenue cents": -1311, "anomaly": true }, { "id": "l-1-0002", "region": "west", "category": "services", "active": true, "units": 3, "revenue cents": 1348, "anomaly": false }, { "id": "l-1-0003", "region": "south", "category": "training", "active": true, "units": 4, "revenue cents": 1385, "anomaly": false }, { "id": "l-1-0004", "region": "west", "category": "hardware", "active": true, "units": 5, "revenue cents": 1422, "anomaly": false }, { "id": "l-1-0005", "region": "south", "category": "software", "active":… Evidence: `benchmarks/agent_skill_eval/payloads/1_large.json`
- **1 Medium** (structured_config): { "scenario": "medium", "iteration": 1, "source": "datoon agent skill evaluation", "records": { "id": "m-1-0001", "region": "south", "category": "software", "active": true, "units": 2, "revenue cents": -1311, "anomaly": true }, { "id": "m-1-0002", "region": "west", "category": "services", "active": true, "units": 3, "revenue cents": 1348, "anomaly": false }, { "id": "m-1-0003", "region": "south", "category": "training", "active": true, "units": 4, "revenue cents": 1385, "anomaly": false }, { "id": "m-1-0004", "region": "west", "category": "hardware", "active": true, "units": 5, "revenue cents": 1422, "anomaly": false }, { "id": "m-1-0005", "region": "south", "category": "software", "active"… Evidence: `benchmarks/agent_skill_eval/payloads/1_medium.json`
- **1 Small** (structured_config): { "scenario": "small", "iteration": 1, "source": "datoon agent skill evaluation", "records": { "id": "s-1-0001", "region": "south", "category": "software", "active": true, "units": 2, "revenue cents": -1311, "anomaly": true }, { "id": "s-1-0002", "region": "west", "category": "services", "active": true, "units": 3, "revenue cents": 1348, "anomaly": false }, { "id": "s-1-0003", "region": "south", "category": "training", "active": true, "units": 4, "revenue cents": 1385, "anomaly": false }, { "id": "s-1-0004", "region": "west", "category": "hardware", "active": true, "units": 5, "revenue cents": 1422, "anomaly": false }, { "id": "s-1-0005", "region": "south", "category": "software", "active":… Evidence: `benchmarks/agent_skill_eval/payloads/1_small.json`
- **2 Large** (structured_config): { "scenario": "large", "iteration": 2, "source": "datoon agent skill evaluation", "records": { "id": "l-2-0001", "region": "east", "category": "services", "active": true, "units": 3, "revenue cents": -1422, "anomaly": true }, { "id": "l-2-0002", "region": "north", "category": "training", "active": true, "units": 4, "revenue cents": 1459, "anomaly": false }, { "id": "l-2-0003", "region": "east", "category": "hardware", "active": true, "units": 5, "revenue cents": 1496, "anomaly": false }, { "id": "l-2-0004", "region": "north", "category": "software", "active": false, "units": 6, "revenue cents": 1533, "anomaly": false }, { "id": "l-2-0005", "region": "east", "category": "services", "active":… Evidence: `benchmarks/agent_skill_eval/payloads/2_large.json`
- **2 Medium** (structured_config): { "scenario": "medium", "iteration": 2, "source": "datoon agent skill evaluation", "records": { "id": "m-2-0001", "region": "east", "category": "services", "active": true, "units": 3, "revenue cents": -1422, "anomaly": true }, { "id": "m-2-0002", "region": "north", "category": "training", "active": true, "units": 4, "revenue cents": 1459, "anomaly": false }, { "id": "m-2-0003", "region": "east", "category": "hardware", "active": true, "units": 5, "revenue cents": 1496, "anomaly": false }, { "id": "m-2-0004", "region": "north", "category": "software", "active": false, "units": 6, "revenue cents": 1533, "anomaly": false }, { "id": "m-2-0005", "region": "east", "category": "services", "active"… Evidence: `benchmarks/agent_skill_eval/payloads/2_medium.json`
- **2 Small** (structured_config): { "scenario": "small", "iteration": 2, "source": "datoon agent skill evaluation", "records": { "id": "s-2-0001", "region": "east", "category": "services", "active": true, "units": 3, "revenue cents": -1422, "anomaly": true }, { "id": "s-2-0002", "region": "north", "category": "training", "active": true, "units": 4, "revenue cents": 1459, "anomaly": false }, { "id": "s-2-0003", "region": "east", "category": "hardware", "active": true, "units": 5, "revenue cents": 1496, "anomaly": false }, { "id": "s-2-0004", "region": "north", "category": "software", "active": false, "units": 6, "revenue cents": 1533, "anomaly": false }, { "id": "s-2-0005", "region": "east", "category": "services", "active":… Evidence: `benchmarks/agent_skill_eval/payloads/2_small.json`
- **3 Large** (structured_config): { "scenario": "large", "iteration": 3, "source": "datoon agent skill evaluation", "records": { "id": "l-3-0001", "region": "west", "category": "training", "active": true, "units": 4, "revenue cents": -1533, "anomaly": true }, { "id": "l-3-0002", "region": "south", "category": "hardware", "active": true, "units": 5, "revenue cents": 1570, "anomaly": false }, { "id": "l-3-0003", "region": "west", "category": "software", "active": false, "units": 6, "revenue cents": 1607, "anomaly": false }, { "id": "l-3-0004", "region": "south", "category": "services", "active": true, "units": 7, "revenue cents": 1644, "anomaly": false }, { "id": "l-3-0005", "region": "west", "category": "training", "active":… Evidence: `benchmarks/agent_skill_eval/payloads/3_large.json`
- **3 Medium** (structured_config): { "scenario": "medium", "iteration": 3, "source": "datoon agent skill evaluation", "records": { "id": "m-3-0001", "region": "west", "category": "training", "active": true, "units": 4, "revenue cents": -1533, "anomaly": true }, { "id": "m-3-0002", "region": "south", "category": "hardware", "active": true, "units": 5, "revenue cents": 1570, "anomaly": false }, { "id": "m-3-0003", "region": "west", "category": "software", "active": false, "units": 6, "revenue cents": 1607, "anomaly": false }, { "id": "m-3-0004", "region": "south", "category": "services", "active": true, "units": 7, "revenue cents": 1644, "anomaly": false }, { "id": "m-3-0005", "region": "west", "category": "training", "active"… Evidence: `benchmarks/agent_skill_eval/payloads/3_medium.json`
- **3 Small** (structured_config): { "scenario": "small", "iteration": 3, "source": "datoon agent skill evaluation", "records": { "id": "s-3-0001", "region": "west", "category": "training", "active": true, "units": 4, "revenue cents": -1533, "anomaly": true }, { "id": "s-3-0002", "region": "south", "category": "hardware", "active": true, "units": 5, "revenue cents": 1570, "anomaly": false }, { "id": "s-3-0003", "region": "west", "category": "software", "active": false, "units": 6, "revenue cents": 1607, "anomaly": false }, { "id": "s-3-0004", "region": "south", "category": "services", "active": true, "units": 7, "revenue cents": 1644, "anomaly": false }, { "id": "s-3-0005", "region": "west", "category": "training", "active":… Evidence: `benchmarks/agent_skill_eval/payloads/3_small.json`
- **1 Large.Report** (structured_config): { "decision": "convert", "reason": "Estimated savings 62.42% threshold 15.00% .", "was forced": false, "input token estimate": 17756, "output token estimate": 6672, "savings ratio": 0.6242396936246902, "analysis": { "is candidate": true, "reason": "Detected 1 uniform object array s within depth 4.", "max depth": 4, "uniform array count": 1 } } Evidence: `benchmarks/agent_skill_eval/reports/1_large.report.json`
- **1 Medium.Report** (structured_config): { "decision": "convert", "reason": "Estimated savings 61.71% threshold 15.00% .", "was forced": false, "input token estimate": 2972, "output token estimate": 1138, "savings ratio": 0.617092866756393, "analysis": { "is candidate": true, "reason": "Detected 1 uniform object array s within depth 4.", "max depth": 4, "uniform array count": 1 } } Evidence: `benchmarks/agent_skill_eval/reports/1_medium.report.json`
- **1 Small.Report** (structured_config): { "decision": "convert", "reason": "Estimated savings 47.79% threshold 15.00% .", "was forced": false, "input token estimate": 226, "output token estimate": 118, "savings ratio": 0.4778761061946903, "analysis": { "is candidate": true, "reason": "Detected 1 uniform object array s within depth 4.", "max depth": 4, "uniform array count": 1 } } Evidence: `benchmarks/agent_skill_eval/reports/1_small.report.json`
- **2 Large.Report** (structured_config): { "decision": "convert", "reason": "Estimated savings 62.42% threshold 15.00% .", "was forced": false, "input token estimate": 17757, "output token estimate": 6673, "savings ratio": 0.6242045390550206, "analysis": { "is candidate": true, "reason": "Detected 1 uniform object array s within depth 4.", "max depth": 4, "uniform array count": 1 } } Evidence: `benchmarks/agent_skill_eval/reports/2_large.report.json`
- **2 Medium.Report** (structured_config): { "decision": "convert", "reason": "Estimated savings 61.71% threshold 15.00% .", "was forced": false, "input token estimate": 2972, "output token estimate": 1138, "savings ratio": 0.617092866756393, "analysis": { "is candidate": true, "reason": "Detected 1 uniform object array s within depth 4.", "max depth": 4, "uniform array count": 1 } } Evidence: `benchmarks/agent_skill_eval/reports/2_medium.report.json`
- **2 Small.Report** (structured_config): { "decision": "convert", "reason": "Estimated savings 47.56% threshold 15.00% .", "was forced": false, "input token estimate": 225, "output token estimate": 118, "savings ratio": 0.47555555555555556, "analysis": { "is candidate": true, "reason": "Detected 1 uniform object array s within depth 4.", "max depth": 4, "uniform array count": 1 } } Evidence: `benchmarks/agent_skill_eval/reports/2_small.report.json`
- **3 Large.Report** (structured_config): { "decision": "convert", "reason": "Estimated savings 62.42% threshold 15.00% .", "was forced": false, "input token estimate": 17758, "output token estimate": 6674, "savings ratio": 0.6241693884446446, "analysis": { "is candidate": true, "reason": "Detected 1 uniform object array s within depth 4.", "max depth": 4, "uniform array count": 1 } } Evidence: `benchmarks/agent_skill_eval/reports/3_large.report.json`
- **3 Medium.Report** (structured_config): { "decision": "convert", "reason": "Estimated savings 61.71% threshold 15.00% .", "was forced": false, "input token estimate": 2972, "output token estimate": 1138, "savings ratio": 0.617092866756393, "analysis": { "is candidate": true, "reason": "Detected 1 uniform object array s within depth 4.", "max depth": 4, "uniform array count": 1 } } Evidence: `benchmarks/agent_skill_eval/reports/3_medium.report.json`
- **3 Small.Report** (structured_config): { "decision": "convert", "reason": "Estimated savings 47.56% threshold 15.00% .", "was forced": false, "input token estimate": 225, "output token estimate": 118, "savings ratio": 0.47555555555555556, "analysis": { "is candidate": true, "reason": "Detected 1 uniform object array s within depth 4.", "max depth": 4, "uniform array count": 1 } } Evidence: `benchmarks/agent_skill_eval/reports/3_small.report.json`
- **Scored Agent Results** (structured_config): { "variant": "with skill", "agent id": "019e6b45-6723-75c0-a2da-5a5af19635ac", "agent nickname": "Bacon", "payload name": "1 small.json", "result": { "mode": "convert", "payload file": "/Users/andriisuruhov/github/datoon/benchmarks/agent skill eval/payloads/1 small.json", "scenario": "small", "iteration": 1, "record count": 5, "active count": 4, "total revenue cents": 1385, "top region": "west", "top category": "hardware", "anomaly ids": "s-1-0001", "s-1-0005" , "elapsed seconds": 1.247206375002861, "notes": "datoon decision=convert; Estimated savings 47.79% threshold 15.00% ." }, "correct": true, "mismatches": {} }, { "variant": "without skill", "agent id": "019e6b45-79f7-7033-a490-ec76d7a… Evidence: `benchmarks/agent_skill_eval/scored_agent_results.json`
- **.editorconfig** (source_file): charset = utf-8 end of line = lf insert final newline = true indent style = space indent size = 4 trim trailing whitespace = true Evidence: `.editorconfig`
- **.gitattributes** (source_file): .skill binary Evidence: `.gitattributes`
- **Python** (source_file): Python pycache / .py cod .pyo .pyd .python-version .venv/ venv/ env/ Evidence: `.gitignore`
- The remaining 15 evidence entries are in `AI_CONTEXT_PACK.json` or `EVIDENCE_INDEX.json`.

## Rules the Host AI Must Follow

- **Treat this asset as pre-work context, not a runtime environment.**: The AI Context Pack contains only an evidence-backed understanding of the project, not the project's executable state. Evidence: `README.md`, `skills/datoon/README.md`, `.claude-plugin/marketplace.json`
- **When answering the user, distinguish what can be previewed from what can only be verified after install.**: The consumer value of the pre-install experience comes from reducing bad installs and misjudgments, not from pretending to be a real run. Evidence: `README.md`, `skills/datoon/README.md`, `.claude-plugin/marketplace.json`

## Questions the User Should Answer First

- Which host AI or local environment do you plan to use it in?
- Do you just want to experience the workflow first, or are you ready to actually install?
- What matters most to you: install cost, output quality, or conflicts with your existing rules?

## Acceptance Checks

- Every capability claim can be traced back to a file path in evidence_refs.
- AI_CONTEXT_PACK.md does not package previews as a real run.
- The user can understand who it fits, what it can do, how to start, and the risk boundaries within 3 minutes.

---

## Doramagic Context Augmentation

The following sections strengthen the repository context for a host AI. Human Manual data is a reading route, and pitfall notes become operating constraints.

## Human Manual Outline

Usage rule: this is only a reading route and salience signal, not factual authority. Concrete claims must still return to repo evidence or Claim Graph.

Host AI hard rules:
- Do not treat page titles, section order, summaries, or importance values as factual project evidence.
- When explaining the Human Manual outline, state that it is only a reading route or salience signal.
- Capability, installation, compatibility, runtime state, and risk claims must cite repo evidence, source paths, or Claim Graph.

- **Project Overview and Conversion Decision Engine**: importance `high`
  - source_paths: src/datoon/__init__.py, src/datoon/analyzer.py, src/datoon/converter.py, src/datoon/models.py, src/datoon/errors.py
- **Multi-Format Readers and Data Ingestion**: importance `high`
  - source_paths: src/datoon/readers/__init__.py, src/datoon/readers/_coerce.py, src/datoon/readers/_tabular.py, src/datoon/readers/csv.py, src/datoon/readers/jsonl.py
- **Token Estimation and Encoding Configuration**: importance `high`
  - source_paths: src/datoon/converter.py, src/datoon/models.py, pyproject.toml
- **CLI, MCP Server, and Agent Plugin Surfaces**: importance `high`
  - source_paths: src/datoon/cli.py, src/datoon/mcp_server.py, .claude-plugin/marketplace.json, .claude-plugin/plugin.json, .agents/plugins/marketplace.json

## Repo Inspection Evidence

- repo_clone_verified: true
- repo_inspection_verified: true
- repo_commit: `ccf316238253a626ece1c3046e8d3af441b87788`
- inspected_files: `README.md`, `pyproject.toml`, `uv.lock`, `docs/app.js`, `src/datoon/__init__.py`, `src/datoon/analyzer.py`, `src/datoon/cli.py`, `src/datoon/converter.py`, `src/datoon/errors.py`, `src/datoon/mcp_server.py`, `src/datoon/models.py`, `src/datoon/readers/__init__.py`, `src/datoon/readers/_coerce.py`, `src/datoon/readers/_tabular.py`, `src/datoon/readers/columnar.py`, `src/datoon/readers/csv.py`, `src/datoon/readers/excel.py`, `src/datoon/readers/jsonl.py`, `src/datoon/readers/numbers.py`, `src/datoon/readers/xml.py`

Host AI hard rules:
- Without repo_clone_verified=true, do not claim that the source code has been read.
- Without repo_inspection_verified=true, do not write README, docs, or package-file conclusions as facts.
- Without quick_start_verified=true, do not claim that the Quick Start path has run successfully.

## Doramagic Pitfall Constraints

These rules come from Doramagic discovery, validation, or compilation findings. The host AI must treat them as operating constraints, not background notes.

### Constraint 1: Configuration risk requires verification

- Trigger: Developers should check this configuration risk before relying on the project: Token estimate uses cl100k_base, not the tokenizer of target models
- Host AI rule: Before packaging this project, run the relevant install/config/quickstart check for: Token estimate uses cl100k_base, not the tokenizer of target models. Context: Source discussion did not expose a precise runtime context.
- Why it matters: Developers may misconfigure credentials, environment, or host setup: Token estimate uses cl100k_base, not the tokenizer of target models
- Evidence: failure_mode_cluster:github_issue | https://github.com/andrii-su/datoon/issues/42
- Hard boundary: Do not present this pitfall as solved, verified, or ignorable unless later evidence explicitly closes it.

### Constraint 2: Configuration risk requires verification

- Trigger: Developers should check this configuration risk before relying on the project: v1.9.0
- Host AI rule: Before packaging this project, run the relevant install/config/quickstart check for: v1.9.0. Context: Source discussion did not expose a precise runtime context.
- Why it matters: Upgrade or migration may change expected behavior: v1.9.0
- Evidence: failure_mode_cluster:github_release | https://github.com/andrii-su/datoon/releases/tag/v1.9.0
- Hard boundary: Do not present this pitfall as solved, verified, or ignorable unless later evidence explicitly closes it.

### Constraint 3: Capability evidence risk requires verification

- Trigger: README/documentation is current enough for a first validation pass.
- Host AI rule: Reproduce the official install and quickstart path in an isolated environment.
- Why it matters: May increase setup, validation, or first-run risk for the user.
- Evidence: capability.assumptions | https://github.com/andrii-su/datoon
- Hard boundary: Do not present this pitfall as solved, verified, or ignorable unless later evidence explicitly closes it.

### Constraint 4: Maintenance risk requires verification

- Trigger: Developers should check this migration risk before relying on the project: cli.py imports Sequence from typing (deprecated alias)
- Host AI rule: Before packaging this project, run the relevant install/config/quickstart check for: cli.py imports Sequence from typing (deprecated alias). Context: Observed when using python
- Why it matters: Developers may hit a documented source-backed failure mode: cli.py imports Sequence from typing (deprecated alias)
- Evidence: failure_mode_cluster:github_issue | https://github.com/andrii-su/datoon/issues/44
- Hard boundary: Do not present this pitfall as solved, verified, or ignorable unless later evidence explicitly closes it.

### Constraint 5: Security or permission risk requires verification

- Trigger: no_demo
- Host AI rule: Reproduce the official install and quickstart path in an isolated environment.
- Why it matters: May increase setup, validation, or first-run risk for the user.
- Evidence: downstream_validation.risk_items | https://github.com/andrii-su/datoon
- Hard boundary: Do not present this pitfall as solved, verified, or ignorable unless later evidence explicitly closes it.