# oto-cli - 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 oto-cli. 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_0002` supported 0.86 ## What It Can Do - **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: `README.md` Claim: `clm_0001` supported 0.86 ## How to Start - `pipx install oto-cli` Evidence: `README.md` Claim: `clm_0003` supported 0.86, `clm_0004` supported 0.86, `clm_0005` supported 0.86 - `pipx install "oto-cli[google,browser]"` Evidence: `README.md` Claim: `clm_0004` supported 0.86 - `pipx install "oto-cli[all]"` Evidence: `README.md` Claim: `clm_0005` supported 0.86 - `git clone https://github.com/otomata-tech/oto-cli.git` Evidence: `README.md` Claim: `clm_0006` supported 0.86 ## Continue-or-Stop Decision Card - **Current recommendation**: Needs admin / security approval - **Why**: Continuing may involve secrets, accounts, external services, or sensitive context; get admin or security approval first. ### 30-Second Read - **What to do now**: Needs admin / security approval - **Minimum safe next step**: Run Prompt Preview first; if credentials or an enterprise environment are involved, get approval before trialing - **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_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: `README.md` Claim: `clm_0001` 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: `README.md` Claim: `clm_0003` supported 0.86, `clm_0004` supported 0.86, `clm_0005` 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: `CLAUDE.md` - **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. - **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: `README.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: `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: `CLAUDE.md` - **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: `README.md` - **Environment variables / API keys**: Project entry docs explicitly showing API key, token, secret, or account credential configuration. Why: If a real install needs credentials, use test credentials first and go through a permission/compliance review. Evidence: `CLAUDE.md`, `README.md`, `docs/concepts.md`, `docs/installation.md` - **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.) - **Do not use real production credentials**: Once an environment variable / API key enters the host or toolchain, it can create account and compliance risk. (applies when: When environment signals like API, TOKEN, KEY, or SECRET appear.) - **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. - **Be ready to revoke test API keys or tokens**: If test credentials leak or are misused, you can cut losses quickly. - **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_0007` inferred 0.45 - **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: `README.md` Claim: `clm_0008` 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 - **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: `README.md` Claim: `clm_0001` supported 0.86 ### Context Scale - Total files: 44 - Important-file coverage: 24/44 - Evidence index entries: 23 - Role / Skill entries: 11 ### 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 oto-cli, 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 oto-cli 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 oto-cli, 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 11 role / Skill / project-doc entries. - **oto — CLI toolkit for AI agents** (project_doc): Your LLM already uses gh for GitHub, aws for AWS, gcloud for GCP. oto covers the long tail — the SaaS products that don't have a CLI. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `README.md` - **oto-cli** (project_doc): Façade CLI d'Oto — commandes Typer oto au-dessus de la lib oto-core . Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `CLAUDE.md` - **Concepts** (project_doc): LLMs are good at using CLI tools — they were trained on billions of terminal interactions. For popular tools gh , aws , gcloud , docker , the LLM already knows the commands. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/concepts.md` - **Create a connector** (project_doc): A connector is a pair: command + tool client . Write clear --help strings — that's how agents and the oto plugin's universal skill discover it. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/create-connector.md` - **Installation** (project_doc): oto is a CLI toolkit. Install it once with pipx , then every connector is a sub-command oto google , oto linkedin , … . The CLI is self-documenting via --help , so your AI agent Claude Code, Cursor, … discovers how to drive it. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/installation.md` - **Gmail OAuth Setup** (project_doc): Gmail nécessite un OAuth2 user flow pas un service account car il accède aux données personnelles de l'utilisateur. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/gmail-oauth-setup.md` - **Gmail — markdown / HTML body rendering** (project_doc): Gmail — markdown / HTML body rendering Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/gmail.md` - **Google Docs — markdown import & styling** (project_doc): Google Docs — markdown import & styling Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/google-docs.md` - **Google Service Account Setup** (project_doc): This guide explains how to create a Google Service Account from scratch to use with otomata tools. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/google-service-account-setup.md` - **Zoho Desk — OAuth setup** (project_doc): Steps to obtain the secrets needed by oto zohodesk . Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `docs/zoho-desk-oauth-setup.md` - **TODO / Roadmap** (project_doc): Multi-backend support Currently using Patchright undetectable Playwright . Consider adding alternative backends: Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `TODO.md` ## Evidence Index - Indexed 23 evidence entries. - **oto — CLI toolkit for AI agents** (documentation): Your LLM already uses gh for GitHub, aws for AWS, gcloud for GCP. oto covers the long tail — the SaaS products that don't have a CLI. Evidence: `README.md` - **oto-cli** (documentation): Façade CLI d'Oto — commandes Typer oto au-dessus de la lib oto-core . Evidence: `CLAUDE.md` - **Concepts** (documentation): LLMs are good at using CLI tools — they were trained on billions of terminal interactions. For popular tools gh , aws , gcloud , docker , the LLM already knows the commands. Evidence: `docs/concepts.md` - **Create a connector** (documentation): A connector is a pair: command + tool client . Write clear --help strings — that's how agents and the oto plugin's universal skill discover it. Evidence: `docs/create-connector.md` - **Installation** (documentation): oto is a CLI toolkit. Install it once with pipx , then every connector is a sub-command oto google , oto linkedin , … . The CLI is self-documenting via --help , so your AI agent Claude Code, Cursor, … discovers how to drive it. Evidence: `docs/installation.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` - **Gmail OAuth Setup** (documentation): Gmail nécessite un OAuth2 user flow pas un service account car il accède aux données personnelles de l'utilisateur. Evidence: `docs/gmail-oauth-setup.md` - **Gmail — markdown / HTML body rendering** (documentation): Gmail — markdown / HTML body rendering Evidence: `docs/gmail.md` - **Google Docs — markdown import & styling** (documentation): Google Docs — markdown import & styling Evidence: `docs/google-docs.md` - **Google Service Account Setup** (documentation): This guide explains how to create a Google Service Account from scratch to use with otomata tools. Evidence: `docs/google-service-account-setup.md` - **Zoho Desk — OAuth setup** (documentation): Steps to obtain the secrets needed by oto zohodesk . Evidence: `docs/zoho-desk-oauth-setup.md` - **Cli** (source_file): app = typer.Typer ⋮---- commands dir = Path file .parent / "commands" ⋮---- module name = cmd file.stem ⋮---- module = importlib.import module f"oto.commands.{ module name}" ⋮---- plugin app = ep.load ⋮---- EXTRA FOR MODULE = { ⋮---- def extra for missing: str - str ⋮---- def main ⋮---- extra = extra for e.name or "" Evidence: `oto/cli.py` - **stock duckdb/pyarrow/pandas EN BASE, pas en extra : la CLI est le** (source_file): build-system requires = "setuptools =68" build-backend = "setuptools.build meta" Evidence: `pyproject.toml` - **Browser** (source_file): app = typer.Typer help="Browser automation tools LinkedIn, Crunchbase, Indeed, Google, etc. " ⋮---- linkedin app = typer.Typer help="LinkedIn sourcing profile/company/search + outreach connect/send . Run 'oto browser linkedin login' once first — a logged-in local browser profile is the robust path. Injected cookies work only with the real Google Chrome channel matching TLS fingerprint ; sharing one cookie to a server logs the user out — prefer the local profile." ⋮---- DEFAULT LINKEDIN PROFILE = "~/.config/browser/linkedin" ⋮---- def linkedin client kwargs ⋮---- async def run ⋮---- result = asyncio.run run ⋮---- kw list = keywords.split "," if keywords else None ⋮---- sncf app = typer.Typer… Evidence: `oto/commands/browser.py` - **Config** (source_file): app = typer.Typer help="Configuration and secrets management" ⋮---- TRACKED SECRETS = ⋮---- @app.callback invoke without command=True def show ctx: typer.Context ⋮---- provider = get provider search provider = get search provider ⋮---- project secrets = find project secrets user secrets = get user secrets ⋮---- status = "+" if get secret name else "-" ⋮---- provider app = typer.Typer help="Manage providers secrets, search " ⋮---- config = get oto config .copy ⋮---- @app.command "secrets-push" def secrets push ⋮---- secrets file = get user secrets ⋮---- secrets = parse env file secrets file ⋮---- revision = push secrets secrets ⋮---- @app.command "secrets-pull" def secrets pull ⋮---- secrets… Evidence: `oto/commands/config.py` - **Enrichment** (source_file): app = typer.Typer help="Contact enrichment tools" ⋮---- kaspr app = typer.Typer help="Kaspr contact enrichment" ⋮---- hunter app = typer.Typer help="Hunter.io email tools" ⋮---- lemlist app = typer.Typer help="Lemlist campaign & lead management" ⋮---- client = KasprClient data to get = "workEmail", "phone" if with phone else None result = client.enrich linkedin linkedin slug, name=name, data to get=data to get ⋮---- client = HunterClient result = client.domain search domain, limit=limit ⋮---- result = client.email finder domain, full name=name ⋮---- result = client.email verifier email ⋮---- @lemlist app.command "campaigns" def lemlist campaigns ⋮---- client = LemlistClient campaigns = clie… Evidence: `oto/commands/enrichment.py` - **Google** (source_file): app = typer.Typer help="Google Workspace tools Drive, Docs, Sheets, Slides, Gmail, Chat, Calendar, Tasks " ⋮---- ALL SCOPES = list set GMAIL SCOPES + CHAT SCOPES + CALENDAR SCOPES + DRIVE SCOPES + TASKS SCOPES ⋮---- accounts = list accounts Evidence: `oto/commands/google.py` - **Notion** (source_file): app = typer.Typer help="Notion tools" ⋮---- client = NotionClient results = client.search query, filter type=filter type ⋮---- page = client.get page page id ⋮---- results = client.query database database id, page size=limit ⋮---- db = client.get database database id Evidence: `oto/commands/notion.py` - **Search** (source_file): app = typer.Typer help="Web search dispatches to serper or browser via config " ⋮---- def search serper query: str, num: int, tbs: Optional str - dict ⋮---- def search browser query: str, num: int - dict ⋮---- async def run ⋮---- provider = get search provider ⋮---- result = search browser query, num ⋮---- result = search serper query, num, tbs ⋮---- client = SerperClient result = client.search news query, num=num, tbs=tbs Evidence: `oto/commands/search.py` - **Serper** (source_file): app = typer.Typer help="Serper API Google search, news, scrape " ⋮---- client = SerperClient result = client.search query, num=num, tbs=tbs ⋮---- result = client.search news query, num=num, tbs=tbs ⋮---- result = client.scrape page url, include markdown=markdown ⋮---- result = client.get suggestions query, country=country Evidence: `oto/commands/serper.py` - **TODO / Roadmap** (documentation): Multi-backend support Currently using Patchright undetectable Playwright . Consider adding alternative backends: Evidence: `TODO.md` - **Secrets** (source_file): venv/ pycache / .egg-info/ dist/ oto/tools/whatsapp/node/node modules/ Evidence: `.gitignore` - **2. Injecte chaque entrée** (source_file): SOURCE = Path "/mnt/otomata/time-entries.json" NAMESPACE = "timetrack" ⋮---- def main ⋮---- parser = argparse.ArgumentParser ⋮---- args = parser.parse args ⋮---- payload = json.loads args.source.read text entries = payload.get "entries", ⋮---- row = entry to row e ⋮---- client = DatastoreClient ⋮---- ns = client.create namespace NAMESPACE ⋮---- 2. Injecte chaque entrée ok = 0 failed = ⋮---- def entry to row e: dict - dict Evidence: `scripts/migrate_timetrack.py` ## 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`, `CLAUDE.md`, `docs/concepts.md` - **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`, `CLAUDE.md`, `docs/concepts.md` ## 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. - **Introduction to oto-cli**: importance `high` - source_paths: README.md, pyproject.toml, docs/installation.md - **Architecture and Auto-Discovery**: importance `high` - source_paths: oto/cli.py, oto/commands/__init__.py, docs/concepts.md - **Built-in Connectors Reference**: importance `medium` - source_paths: oto/commands/browser.py, oto/commands/google.py, oto/commands/notion.py, oto/commands/serper.py, oto/commands/search.py - **Creating Custom Connectors**: importance `high` - source_paths: docs/create-connector.md, oto/commands/__init__.py, oto/cli.py ## Repo Inspection Evidence - repo_clone_verified: true - repo_inspection_verified: true - repo_commit: `ef46ac998cd115aa5ba18694e33bac76d1f499ee` - inspected_files: `README.md`, `pyproject.toml`, `docs/concepts.md`, `docs/create-connector.md`, `docs/gmail-oauth-setup.md`, `docs/gmail.md`, `docs/google-docs.md`, `docs/google-service-account-setup.md`, `docs/installation.md`, `docs/zoho-desk-oauth-setup.md` 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: 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/otomata-tech/oto-cli - Hard boundary: Do not present this pitfall as solved, verified, or ignorable unless later evidence explicitly closes it. ### Constraint 2: 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/otomata-tech/oto-cli - Hard boundary: Do not present this pitfall as solved, verified, or ignorable unless later evidence explicitly closes it. ### Constraint 3: 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: risks.scoring_risks | https://github.com/otomata-tech/oto-cli - 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: issue_or_pr_quality=unknown。 - 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: evidence.maintainer_signals | https://github.com/otomata-tech/oto-cli - Hard boundary: Do not present this pitfall as solved, verified, or ignorable unless later evidence explicitly closes it. ### Constraint 5: Maintenance risk requires verification - Trigger: release_recency=unknown。 - 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: evidence.maintainer_signals | https://github.com/otomata-tech/oto-cli - Hard boundary: Do not present this pitfall as solved, verified, or ignorable unless later evidence explicitly closes it.