# pipelex - 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 pipelex. 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_0003` supported 0.86 - **Users who want to bring professional workflows into a host AI**: The repo contains Skill documents. Evidence: `.claude/skills/add-model/SKILL.md`, `.claude/skills/drift-review/SKILL.md`, `.claude/skills/release/SKILL.md`, `.claude/skills/test-model/SKILL.md` et al. Claim: `clm_0004` 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: `.claude/skills/add-model/SKILL.md`, `.claude/skills/drift-review/SKILL.md`, `.claude/skills/release/SKILL.md`, `.claude/skills/test-model/SKILL.md` et al. Claim: `clm_0001` 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: `README.md` Claim: `clm_0002` supported 0.86 ## How to Start - `npm install -g mthds` Evidence: `README.md` Claim: `clm_0005` supported 0.86 - `uv tool install pipelex` Evidence: `README.md` Claim: `clm_0006` supported 0.86 - `npm install mthds` Evidence: `README.md` Claim: `clm_0007` 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_0003` 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: `.claude/skills/add-model/SKILL.md`, `.claude/skills/drift-review/SKILL.md`, `.claude/skills/release/SKILL.md`, `.claude/skills/test-model/SKILL.md` et al. Claim: `clm_0004` 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: `.claude/skills/add-model/SKILL.md`, `.claude/skills/drift-review/SKILL.md`, `.claude/skills/release/SKILL.md`, `.claude/skills/test-model/SKILL.md` et al. Claim: `clm_0001` 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_0002` 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_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/skills/add-model/SKILL.md`, `.claude/skills/drift-review/SKILL.md`, `.claude/skills/release/SKILL.md`, `.claude/skills/test-model/SKILL.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. - **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/skills/add-model/SKILL.md`, `.claude/skills/drift-review/SKILL.md`, `.claude/skills/release/SKILL.md`, `.claude/skills/test-model/SKILL.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: `README.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.) - **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_0008` 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_0009` 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: `.claude/skills/add-model/SKILL.md`, `.claude/skills/drift-review/SKILL.md`, `.claude/skills/release/SKILL.md`, `.claude/skills/test-model/SKILL.md` et al. Claim: `clm_0001` 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: `README.md` Claim: `clm_0002` supported 0.86 ### Context Scale - Total files: 1627 - Important-file coverage: 40/1627 - Evidence index entries: 80 - Role / Skill entries: 5 ### 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 pipelex, 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 pipelex 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 pipelex, 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 5 role / Skill / project-doc entries. - **add-model** (skill): Activation hint: When the user's task is highly relevant to the workflow described by “add-model”, use it for a pre-install experience first, then decide whether to install. Evidence: `.claude/skills/add-model/SKILL.md` - **drift-review** (skill): Activation hint: When the user's task is highly relevant to the workflow described by “drift-review”, use it for a pre-install experience first, then decide whether to install. Evidence: `.claude/skills/drift-review/SKILL.md` - **release** (skill): Activation hint: When the user's task is highly relevant to the workflow described by “release”, use it for a pre-install experience first, then decide whether to install. Evidence: `.claude/skills/release/SKILL.md` - **test-model** (skill): Activation hint: When the user's task is highly relevant to the workflow described by “test-model”, use it for a pre-install experience first, then decide whether to install. Evidence: `.claude/skills/test-model/SKILL.md` - **update-graph-ui** (skill): Activation hint: When the user's task is highly relevant to the workflow described by “update-graph-ui”, use it for a pre-install experience first, then decide whether to install. Evidence: `.claude/skills/update-graph-ui/SKILL.md` ## Evidence Index - Indexed 80 evidence entries. - **What a Method Looks Like** (documentation): Build & Run AI Methods A method is a reusable, typed AI procedure — declared in a .mthds file and executed by Pipelex. Each step is explicit, each output is structured, and every run is repeatable. Evidence: `README.md` - **Documentation Site Architecture** (documentation): This file documents non-obvious architectural decisions for the docs site docs.pipelex.com . Read this before modifying anything under docs/ . Evidence: `docs/CLAUDE.md` - **Autofix track** (documentation): Deterministic auto-fixing of .mthds validation errors — fixes attached to validation diagnostics, applied by a convergence loop. Evidence: `wip/autofix/README.md` - **Boot split** (documentation): The composition root pipelex/pipelex.py was split into a runtime-layer RuntimeBoot pipelex/runtime boot.py and the interpreter-layer Pipelex that subclasses it. Track complete — PR 1073 https://github.com/Pipelex/pipelex/pull/1073 squash-merged into dev as 8448c5ca2 on 2026-07-30, and the repo-root TODOS.md tracker was archived here as boot-split-tracker.md boot-split-tracker.md . Evidence: `wip/boot-split/README.md` - **Concept purity — working docs** (documentation): Working notes for the refactor/Concept-purity track: getting the process-global class-registry reads out of Concept , the MTHDS-protocol wire model. Track complete — PR 1072 https://github.com/Pipelex/pipelex/pull/1072 is open against dev with CI green and both review bots clean. The repo-root TODOS.md tracker was archived here once the PR was finalized. Evidence: `wip/concept-purity/README.md` - **Drift contracts — track folder** (documentation): Working notes for the drift-contracts system the shipped system itself is documented at docs/contribute/drift-contracts.md ; the original design rationale at ../drift-contracts-design.md . Evidence: `wip/drift-contracts/README.md` - **Hub layering — working docs** (documentation): Working notes for the refactor/Hub track: splitting pipelex.hub into two hubs and drawing an enforced layering boundary between them. Track complete — the repo-root TODOS.md tracker was archived here on 2026-07-27 once hub/ moved on to the refactor/Modularity-3 track. What is left is not a phase, it is the release-gated cross-repo sweep see the tracker's Cross-repo sweep hub-split-tracker.md cross-repo-sweep section . Evidence: `wip/hub/README.md` - **Inputs track — roadmap and reading guide** (documentation): Inputs track — roadmap and reading guide Evidence: `wip/inputs/README.md` - **Optionals ? / ! — track folder** (documentation): Phase 1 the language core is COMPLETE — all steps A–F landed, final checkpoint cleared. The authoritative record is optionals-phase1-tracker.md optionals-phase1-tracker.md archived from the worktree-root TODOS.md ; checkpoint log, decisions/deviations, hand-off notes . PR 1021 → dev is merge-ready; Louis merges. Evidence: `wip/optionals/README.md` - **Provider plugins track — storage & secrets** (documentation): Provider plugins track — storage & secrets Evidence: `wip/plugins/README.md` - **Contributing** (documentation): --- title: Contributing to Pipelex description: "Contribute to Pipelex — guidelines for reporting issues, submitting pull requests, and developing the open-source AI methods runtime." --- --8<-- "CONTRIBUTING.md" Evidence: `docs/contributing.md` - **Pipelex Coding Rules** (documentation): After making code changes, you must always lint using make agent-check . Evidence: `AGENTS.md` - **Pipelex Coding Rules** (documentation): After making code changes, you must always lint using make agent-check . Evidence: `CLAUDE.md` - **Builder** (documentation): Provides the spec layer for authoring Pipelex pipeline bundles .mthds files . Specs are a convenience authoring format for AI agents — they compile down to blueprints via to blueprint . Evidence: `pipelex/builder/CLAUDE.md` - **Agent CLI pipelex-agent** (documentation): Machine-first CLI for running and validating Pipelex method bundles .mthds files . No Rich formatting, no interactive prompts. Evidence: `pipelex/cli/agent_cli/CLAUDE.md` - **Contributing to Pipelex** (documentation): Thank you for your interest in contributing! Contributions are very welcome. We appreciate first time contributors and we are happy help you get started. Join our community on Discord and feel free to reach out with questions in the code-contributions and pipeline-contributions channels. Evidence: `CONTRIBUTING.md` - **Add a New AI Model** (skill_instruction): This skill walks through all the steps needed to register a new AI model in the Pipelex inference system. The process touches several files across the codebase and must be done in order to keep everything consistent. Evidence: `.claude/skills/add-model/SKILL.md` - **Drift Review** (skill_instruction): Resolve open drift contracts: review the declared targets against what actually changed, fix staleness, record the ack, log a dogfood observation. Full system reference: docs/contribute/drift-contracts.md . The manifest is drift.toml at the repo root. Evidence: `.claude/skills/drift-review/SKILL.md` - **Pipelex Release Workflow** (skill_instruction): This skill handles the full release cycle for the pipelex Python package. Evidence: `.claude/skills/release/SKILL.md` - **Update Graph UI Assets CDN + SRI** (skill_instruction): Bump the pinned @pipelex/mthds-ui version that the generated ReactFlow HTML loads from cdn.jsdelivr.net , refresh the Subresource Integrity hashes, and verify everything still renders. Evidence: `.claude/skills/update-graph-ui/SKILL.md` - **Test a Model on a Backend** (skill_instruction): This skill runs inference tests for a specific model on a specific backend. It creates a temporary test profile, regenerates fixtures, runs the tests, and cleans up. Evidence: `.claude/skills/test-model/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` - **Code Of Conduct** (documentation): --- title: Code of Conduct description: "Code of conduct for the Pipelex open-source community — guidelines for respectful collaboration and contribution." --- --8<-- "CODE OF CONDUCT.md" Evidence: `docs/CODE_OF_CONDUCT.md` - **Changelog** (documentation): --- title: Changelog description: "Pipelex changelog — release history, new features, bug fixes, and breaking changes for the Pipelex runtime and CLI." --- --8<-- "CHANGELOG.md" Evidence: `docs/changelog.md` - **Build & Run AI Methods** (documentation): ! Pipelex Banner https://d2cinlfp2qnig1.cloudfront.net/banners/pipelex banner docs v2.png Evidence: `docs/index.md` - **License** (documentation): --- title: License description: "Pipelex license — MIT-licensed open-source runtime for the Methods standard, maintained by Evotis S.A.S." hide: - feedback --- License Evidence: `docs/license.md` - **Content Generator Injection** (documentation): !!! warning "Under Construction" This section is currently under development. Evidence: `docs/advanced/content-generator-injection.md` - **Dependency Injection** (documentation): Pipelex uses dependency injection to manage service dependencies and make components more modular and testable. The system allows you to customize and extend Pipelex's functionality by injecting your own implementations of various services. Evidence: `docs/advanced/index.md` - **Observer Provider Injection** (documentation): The Observer system in Pipelex allows you to monitor and collect data about pipe executions. This guide explains how to implement your own observer by following the dependency injection pattern and the ObserverProtocol. Evidence: `docs/advanced/observer-provider-injection.md` - **Pipe Router Injection** (documentation): !!! warning "Under Construction" This section is currently under development. Evidence: `docs/advanced/pipe-router-injection.md` - **Reporting Delegate Injection** (documentation): !!! warning "Under Construction" This section is currently under development. Evidence: `docs/advanced/reporting-delegate-injection.md` - **Secrets Provider Injection** (documentation): !!! warning "Under Construction" This section is currently under development. Evidence: `docs/advanced/secrets-provider-injection.md` - **Storage Provider Injection** (documentation): !!! warning "Under Construction" This section is currently under development. Evidence: `docs/advanced/storage-provider-injection.md` - **Playbook: debugging a hanging pytest run in one shot** (documentation): Playbook: debugging a hanging pytest run in one shot Evidence: `docs/agents/debugging-hanging-pytest-runs.md` - **Observer Data Extraction** (documentation): The Observer system in Pipelex automatically collects execution data from your pipes for later analysis. This data extraction feature is currently a work in progress but provides valuable insights into pipeline performance, usage patterns, and debugging information. Evidence: `docs/analytics/data-extraction.md` - **LLM Prompting Style Configuration** (documentation): The PromptingConfig class controls how Pipelex handles prompting styles for different LLM targets. Evidence: `docs/building-methods/adapt-to-llm-prompting-style-openai-anthropic-mistral.md` - **Defining Your Concepts** (documentation): !!! tip "MTHDS Standard Reference" Concepts are part of the MTHDS open standard. For the authoritative language specification, see Concepts https://mthds.ai/latest/language/concepts/ on mthds.ai. This page documents Pipelex-specific behavior and usage. Evidence: `docs/building-methods/concepts/define_your_concepts.md` - **Inline Structure Definition** (documentation): Define structured concepts directly in your .mthds files using pipelex syntax. This is the recommended approach for most use cases, offering rapid development without Python boilerplate. Evidence: `docs/building-methods/concepts/inline-structures.md` - **Native Concepts** (documentation): Pipelex includes several built-in native concepts that cover common data types in AI methods. These concepts come with predefined structures and are automatically available in all pipelines—no setup required. Evidence: `docs/building-methods/concepts/native-concepts.md` - **Python StructuredContent Classes** (documentation): For advanced features beyond inline structures, create explicit Python classes that inherit from StructuredContent . This approach gives you full Pydantic power with custom validation, computed properties, and reusable business logic. Evidence: `docs/building-methods/concepts/python-classes.md` - **Refining Concepts** (documentation): Concept refinement allows you to create more specific versions of existing concepts while inheriting their structure. This provides semantic clarity and type safety for domain-specific methods. Evidence: `docs/building-methods/concepts/refining-concepts.md` - **LLM Settings Guide** (documentation): Pipelex provides a flexible way to configure and manage your LLM Large Language Model integrations through the inference backend configuration system. Evidence: `docs/building-methods/configure-ai-llm-to-optimize-methods.md` - **Understanding Domains** (documentation): !!! tip "MTHDS Standard Reference" Domains are part of the MTHDS open standard. For the authoritative language specification, see Domains https://mthds.ai/latest/language/domains/ on mthds.ai. This page documents Pipelex-specific behavior and usage. Evidence: `docs/building-methods/domain.md` - **Kicking off a Pipelex Method Project** (documentation): Kicking off a Pipelex Method Project Evidence: `docs/building-methods/kick-off-a-methods-project.md` - **Libraries** (documentation): A Library in Pipelex is a complete collection of domains, concepts, and pipes that can be loaded and used together. It represents the full set of Pipelex definitions available for execution within a specific context, typically for a single pipeline run. Evidence: `docs/building-methods/libraries.md` - **Packages** (documentation): A package is a collection of .mthds bundles with a METHODS.toml manifest at the project root. The manifest gives the package an identity and defines which pipes are exported to the outside world. Evidence: `docs/building-methods/packages.md` - **Pipelex Bundle Specification** (documentation): !!! tip "MTHDS Standard Reference" The .mthds bundle format is defined by the MTHDS open standard. For the authoritative language specification, see Bundles https://mthds.ai/latest/language/bundles/ on mthds.ai. This page documents Pipelex-specific behavior and usage. Evidence: `docs/building-methods/pipelex-bundle-specification.md` - **CSV Input & Output** (documentation): Pipelex can read a CSV file directly into a typed list and write a list output back out as CSV. A CSV maps to a ListContent of a flat concept: each row becomes one instance of the concept, and each column maps to one of its fields. No new language construct is involved — you describe the row concept exactly as you would any structured concept. Evidence: `docs/building-methods/pipes/csv-input-and-output.md` - **Executing Pipelines** (documentation): Once your pipes are defined in .mthds files, you can execute them in multiple ways. Evidence: `docs/building-methods/pipes/executing-pipelines.md` - **Designing Pipelines** (documentation): In Pipelex, a pipeline is not just a rigid sequence of steps; it's a dynamic and intelligent method built by composing individual, reusable components called pipes . This approach allows you to break down complex AI tasks into manageable, testable, and reliable units. Evidence: `docs/building-methods/pipes/index.md` - **PipeBatch** (documentation): The PipeBatch controller provides a powerful "map" operation for your pipelines. It takes a list of items as input and runs the same pipe on each item in the list, executing the branches concurrently for efficiency. Evidence: `docs/building-methods/pipes/pipe-controllers/PipeBatch.md` - **PipeCondition** (documentation): The PipeCondition controller adds branching logic to your pipelines. It evaluates an expression and, based on the string result, chooses which subsequent pipe to execute from a map of possibilities. Evidence: `docs/building-methods/pipes/pipe-controllers/PipeCondition.md` - **PipeParallel** (documentation): The PipeParallel controller executes multiple pipes simultaneously, then combines their results into a single output. This is highly effective for running independent tasks concurrently in isolated branches. Evidence: `docs/building-methods/pipes/pipe-controllers/PipeParallel.md` - **PipeSequence** (documentation): The PipeSequence controller is used to execute a series of pipes one after another. It is the fundamental building block for creating linear methods where the output of one step becomes the input for the next. Evidence: `docs/building-methods/pipes/pipe-controllers/PipeSequence.md` - **Pipe Controllers** (documentation): !!! tip "MTHDS Standard Reference" Pipe controllers are part of the MTHDS open standard. For the authoritative language specification, see Pipes & Controllers https://mthds.ai/latest/language/pipes-controllers/ on mthds.ai. This page documents Pipelex-specific behavior and usage. Evidence: `docs/building-methods/pipes/pipe-controllers/index.md` - **PipeCompose** (documentation): The PipeCompose operator composes data from your pipeline's working memory into new outputs. It supports two modes: Evidence: `docs/building-methods/pipes/pipe-operators/PipeCompose.md` - **PipeExtract** (documentation): The PipeExtract operator extracts structured content from documents. For PDFs and images, it performs OCR to extract text, embedded images, and full-page renderings. For web pages, it fetches and extracts page content. Evidence: `docs/building-methods/pipes/pipe-operators/PipeExtract.md` - **PipeFunc** (documentation): The PipeFunc operator provides an essential escape hatch, allowing you to execute arbitrary Python code from within a pipeline. This is useful for custom data manipulation, complex logic, or integrating with external services not supported by other operators. Evidence: `docs/building-methods/pipes/pipe-operators/PipeFunc.md` - **PipeImgGen** (documentation): The PipeImgGen operator is used to generate images from a text prompt using a specified image generation model. Evidence: `docs/building-methods/pipes/pipe-operators/PipeImgGen.md` - **PipeLLM** (documentation): PipeLLM is the core operator in Pipelex for leveraging Large Language Models LLMs . It can be used for a wide range of tasks, including text generation, summarization, classification, and structured data extraction. Evidence: `docs/building-methods/pipes/pipe-operators/PipeLLM.md` - The remaining 20 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`, `docs/CLAUDE.md`, `wip/autofix/README.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`, `docs/CLAUDE.md`, `wip/autofix/README.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. - **Overview & MTHDS Language**: importance `high` - source_paths: README.md, pipelex/__init__.py, pipelex/pipelex.py, pipelex/cli/_cli.py, pipelex/language/mthds_config.py - **System Architecture & Runtime**: importance `high` - source_paths: pipelex/runtime_boot.py, pipelex/runtime_hub.py, pipelex/interpreter_hub.py, pipelex/runtime_bridge/bootstrap.py, pipelex/runtime_bridge/direct_orchestrator.py - **Pipes, Concepts & Orchestration**: importance `high` - source_paths: pipelex/pipe_machinery/pipe_abstract.py, pipelex/pipe_controllers/sequence/pipe_sequence.py, pipelex/pipe_controllers/parallel/pipe_parallel.py, pipelex/pipe_controllers/batch/pipe_batch.py, pipelex/pipe_controllers/condition/pipe_condition.py - **Inference, Providers & Extensibility**: importance `high` - source_paths: pipelex/cogt/model_backends/backend.py, pipelex/cogt/models/model_deck.py, pipelex/cogt/model_routing/routing_profile.py, pipelex/providers/openai/openai_plugin.py, pipelex/providers/anthropic/anthropic_plugin.py ## Repo Inspection Evidence - repo_clone_verified: true - repo_inspection_verified: true - repo_commit: `de469459ef0458b8ba9f35a3d673fef642140a44` - inspected_files: `README.md`, `pyproject.toml`, `uv.lock`, `docs/.meta.yml`, `docs/CLAUDE.md`, `docs/CODE_OF_CONDUCT.md`, `docs/advanced/content-generator-injection.md`, `docs/advanced/index.md`, `docs/advanced/observer-provider-injection.md`, `docs/advanced/pipe-router-injection.md`, `docs/advanced/reporting-delegate-injection.md`, `docs/advanced/secrets-provider-injection.md`, `docs/advanced/storage-provider-injection.md`, `docs/agents/debugging-hanging-pytest-runs.md`, `docs/analytics/data-extraction.md`, `docs/building-methods/adapt-to-llm-prompting-style-openai-anthropic-mistral.md`, `docs/building-methods/concepts/define_your_concepts.md`, `docs/building-methods/concepts/inline-structures.md`, `docs/building-methods/concepts/native-concepts.md`, `docs/building-methods/concepts/python-classes.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: Installation risk requires verification - Trigger: Developers should check this installation risk before relying on the project: v0.35.0 - Host AI rule: Before packaging this project, run the relevant install/config/quickstart check for: v0.35.0. Context: Observed when using python - Why it matters: Upgrade or migration may change expected behavior: v0.35.0 - Evidence: failure_mode_cluster:github_release | https://github.com/Pipelex/pipelex/releases/tag/v0.35.0 - Hard boundary: Do not present this pitfall as solved, verified, or ignorable unless later evidence explicitly closes it. ### Constraint 2: Installation risk requires verification - Trigger: Developers should check this installation risk before relying on the project: v0.39.1 - Host AI rule: Before packaging this project, run the relevant install/config/quickstart check for: v0.39.1. Context: Observed when using python - Why it matters: Upgrade or migration may change expected behavior: v0.39.1 - Evidence: failure_mode_cluster:github_release | https://github.com/Pipelex/pipelex/releases/tag/v0.39.1 - Hard boundary: Do not present this pitfall as solved, verified, or ignorable unless later evidence explicitly closes it. ### Constraint 3: Installation risk requires verification - Trigger: Developers should check this installation risk before relying on the project: v0.40.0 - Host AI rule: Before packaging this project, run the relevant install/config/quickstart check for: v0.40.0. Context: Observed when using node, python - Why it matters: Upgrade or migration may change expected behavior: v0.40.0 - Evidence: failure_mode_cluster:github_release | https://github.com/Pipelex/pipelex/releases/tag/v0.40.0 - Hard boundary: Do not present this pitfall as solved, verified, or ignorable unless later evidence explicitly closes it. ### Constraint 4: Configuration risk requires verification - Trigger: Developers should check this configuration risk before relying on the project: v0.37.0 - Host AI rule: Before packaging this project, run the relevant install/config/quickstart check for: v0.37.0. Context: Observed when using python - Why it matters: Upgrade or migration may change expected behavior: v0.37.0 - Evidence: failure_mode_cluster:github_release | https://github.com/Pipelex/pipelex/releases/tag/v0.37.0 - Hard boundary: Do not present this pitfall as solved, verified, or ignorable unless later evidence explicitly closes it. ### Constraint 5: Configuration risk requires verification - Trigger: Developers should check this configuration risk before relying on the project: v0.38.0 - Host AI rule: Before packaging this project, run the relevant install/config/quickstart check for: v0.38.0. Context: Observed when using python - Why it matters: Upgrade or migration may change expected behavior: v0.38.0 - Evidence: failure_mode_cluster:github_release | https://github.com/Pipelex/pipelex/releases/tag/v0.38.0 - Hard boundary: Do not present this pitfall as solved, verified, or ignorable unless later evidence explicitly closes it. ### Constraint 6: Configuration risk requires verification - Trigger: Developers should check this configuration risk before relying on the project: v0.39.0 - Host AI rule: Before packaging this project, run the relevant install/config/quickstart check for: v0.39.0. Context: Observed when using python - Why it matters: Upgrade or migration may change expected behavior: v0.39.0 - Evidence: failure_mode_cluster:github_release | https://github.com/Pipelex/pipelex/releases/tag/v0.39.0 - Hard boundary: Do not present this pitfall as solved, verified, or ignorable unless later evidence explicitly closes it. ### Constraint 7: Configuration risk requires verification - Trigger: Developers should check this configuration risk before relying on the project: v0.41.0 - Host AI rule: Before packaging this project, run the relevant install/config/quickstart check for: v0.41.0. Context: Observed when using python - Why it matters: Upgrade or migration may change expected behavior: v0.41.0 - Evidence: failure_mode_cluster:github_release | https://github.com/Pipelex/pipelex/releases/tag/v0.41.0 - Hard boundary: Do not present this pitfall as solved, verified, or ignorable unless later evidence explicitly closes it. ### Constraint 8: 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/Pipelex/pipelex - Hard boundary: Do not present this pitfall as solved, verified, or ignorable unless later evidence explicitly closes it. ### Constraint 9: Maintenance risk requires verification - Trigger: Developers should check this migration risk before relying on the project: v0.35.1 - Host AI rule: Before packaging this project, run the relevant install/config/quickstart check for: v0.35.1. Context: Observed when using python - Why it matters: Upgrade or migration may change expected behavior: v0.35.1 - Evidence: failure_mode_cluster:github_release | https://github.com/Pipelex/pipelex/releases/tag/v0.35.1 - Hard boundary: Do not present this pitfall as solved, verified, or ignorable unless later evidence explicitly closes it.