Agent skill

Maa Pipeline History Audit

by duorua in duorua/narutomobile

Audit a MaaFramework/Maa-series project's Git history to learn how Pipeline JSON, interface options, Python AgentServer CustomAction code, and related data tables evolved.

AGPL-3.0Auto-check passedDevelopment

Install Maa Pipeline History Audit

skills CLI
$ npx skills add duorua/narutomobile --skill maa-pipeline-history-audit -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install duorua/narutomobile maa-pipeline-history-audit --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/duorua/narutomobile.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/maa-pipeline-history-audit .claude/skills/maa-pipeline-history-audit && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
maa-pipeline-history-audit
GitHub stars
340
Token cost
~1.6k tokens
SKILL.md length
652 words
Files
2
Skills in repo
11
Repo updated
First seen
Licence
AGPL-3.0

At a glance

Audit a MaaFramework/Maa-series project's Git history to learn how Pipeline JSON, interface options, Python AgentServer CustomAction code, and related data tables evolved.

  • Works in 7 steps: Ground the target. → Classify every commit. → Scan historical change themes. → …
  • Asked to review a Maa project from initial commit through a target commit
  • SKILL.md covers Overview, Workflow, Report Judgement Rules and Useful Commands
  • Calls git

What it does

Maa Pipeline History Audit is an agent skill from duorua/narutomobile. Audit a MaaFramework/Maa-series project's Git history to learn how Pipeline JSON, interface options, Python AgentServer CustomAction code, and related data tables evolved. Use when asked to review a Maa project from initial commit through a target commit, map action: Custom pipeline nodes to @AgentServer.customaction(...) implementations, find pipeline/custom/option patterns or breakages, and produce a report plus skill-improvement recommendations.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).

It sits in Development, covering Git workflow. It works with Python and Git. The licence is AGPL-3.0.

When your agent uses it

  • Asked to review a Maa project from initial commit through a target commit
  • Map action: Custom pipeline nodes to @AgentServer.customaction(...) implementations
  • Find pipeline/custom/option patterns
  • Produce a report plus skill-improvement recommendations

Example prompts

  • “/maa-pipeline-history-audit”

Requirements

  • Python 3

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Ground the target.
  2. Classify every commit.
  3. Scan historical change themes.
  4. Parse the target tree.
  5. Review key commits.
  6. Validate the target.
  7. Write the report.

What it can do on your machine

Read from SKILL.md and the folder at commit e3ff401. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Maa Pipeline History Audit loads about 1.6k tokens when it runs. Until then it costs about 121 tokens; SKILL.md has 652 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~121
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from duorua/narutomobile at commit e3ff401, republished under its AGPL-3.0 licence (© duorua). 652 words, ~1,634 tokens.

Download SKILL.mdSave it as .claude/skills/maa-pipeline-history-audit/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
maa-pipeline-history-audit
description
Audit a MaaFramework/Maa-series project's Git history to learn how Pipeline JSON, interface options, Python AgentServer CustomAction code, and related data tables evolved. Use when asked to review a Maa project from initial commit through a target commit, map `action: Custom` pipeline nodes to `@AgentServer.custom_action(...)` implementations, find pipeline/custom/option patterns or breakages, and produce a report plus skill-improvement recommendations.

Pipeline History Audit

Overview

Use this skill to let a Maa project "teach" its Pipeline and CustomAction conventions from real Git history. The output is an audit report, not a code change, unless the user explicitly asks to fix findings afterward.

Default to the current repository and current HEAD unless the user provides a path or commit. If the target checkout is not at the requested commit, read from git show <target>:<path> and git log <target> instead of moving the user's branch.

Workflow

  1. Ground the target.

    • Record repo root, current branch, HEAD, requested target commit, root commit, and git status --short.
    • Treat uncommitted changes as user work. Do not reset, checkout, clean, or format them.
    • If the user names a future/other commit that is not checked out, audit that commit object directly.
  2. Classify every commit.

    • Traverse with git log --reverse --date=short --format=%H%x00%h%x00%ad%x00%s <target>.
    • For each commit, inspect git diff-tree --root --no-commit-id --name-status -r <commit>.
    • Mark a commit as relevant if it touches:
      • assets/resource/**/pipeline*.json
      • assets/resource/**/pipeline/**/*.json
      • assets/resource/**/default_pipeline.json
      • assets/interface.json
      • agent/**/*.py
      • assets/table/**/*.json or intelligence_data/**
    • skills/maa-pipeline-* or legacy .claude/skills/pipeline-*
    • Relevant type labels are Pipeline, Option, Agent, Table, and Skill; labels may overlap.
    • List all commits in an appendix, including irrelevant commits.
  3. Scan historical change themes.

    • Use git log -G over the relevant pathspecs for:
      • "action": "Custom"
      • custom_action and @AgentServer.custom_action
      • custom_action_param
      • "next": and [JumpBack]
      • pipeline_override
      • enable / enabled
      • OCR, TemplateMatch, ColorMatch, color_filter
      • run_task(, run_recognition(, get_node_data(
    • Use these counts as navigation aids, not as the final conclusion.
  4. Parse the target tree.

    • Load every target pipeline JSON and count files, nodes, action types, recognition types, next entries, and [JumpBack] entries.
    • Extract action: Custom nodes with custom_action and custom_action_param.
    • Parse agent/**/*.py with Python ast. Walk both ast.ClassDef and ast.FunctionDef decorator lists because Maa projects commonly register CustomAction implementations by decorating classes. Extract:
      • @AgentServer.custom_action("Name")
      • context.run_task("Node")
      • context.run_recognition("Node")
      • context.get_node_data("Node")
    • Build a Pipeline node -> custom_action -> Python registration table and explicitly list missing registrations.
    • Parse assets/interface.json for option/task counts and pipeline_override usage.
  5. Review key commits.

    • Always sample major introduction/refactor/fix commits found by history, especially commits that introduce CustomAction, new pipeline files, options, ColorMatch/color_filter, or skill updates.
    • Use git show --stat --oneline <commit> plus targeted git show <commit>:<path> reads.
    • Explain what the commit teaches, not just which files changed.
  6. Validate the target.

    • Run JSON parsing on all pipeline files.
    • If a resource checker exists, run it against the target commit. For this repo family, prefer:
      powershell
      python tools\ci\check_resource.py assets\resource\base
    • If checking an un-checked-out target commit, create a temporary detached worktree, run validation there, then remove the worktree. Verify the temp path before recursive deletion.
    • Note that resource loading may not detect missing Python CustomAction registrations; report both results separately.
  7. Write the report.

    • Prefer docs/zh_cn/develop/pipeline_history_audit.md in the target repo if that path exists; otherwise use docs/pipeline_history_audit.md.
    • Include:
      • Method and target commit
      • Coverage stats
      • Historical keyword counts
      • Current pipeline/custom/option asset snapshot
      • Custom mapping table
      • Key timeline
      • Findings by subsystem
      • Skill improvement recommendations
      • Validation results
      • Full commit index appendix
      • Registered custom action appendix
Show full SKILL.md (161 more words)Show less

Report Judgement Rules

  • Do not claim "all Custom nodes are valid" unless every custom_action has a matching decorator in the target tree.
  • Do not treat context.run_task() result .nodes as proof of a hit. Prefer completed or recognition.hit when describing good patterns.
  • Treat pipeline_override as a merge into existing nodes; flag cases where the target node is missing or Python reads a different field path.
  • Call out enable vs enabled explicitly. Recommend compatibility helpers only when history already uses both.
  • Prefer JSON state machines (next + [JumpBack]) for finite UI flows. Reserve Python orchestration for runtime loops, counters, event libraries, screenshot parsing, and business decisions.
  • For OCR stability, look for ROI narrowing, expected text changes, ColorMatch, and color_filter before recommending TemplateMatch.
  • Distinguish resource loading from end-to-end execution. A resource check can pass while CustomAction registration is missing.

Useful Commands

powershell
git -C <repo> status --short
git -C <repo> rev-list --count <target>
git -C <repo> log --reverse --date=short --format="%H%x00%h%x00%ad%x00%s" <target>
git -C <repo> diff-tree --root --no-commit-id --name-status -r <commit>
git -C <repo> log --format=%H -G'"action"\s*:\s*"Custom"' <target> -- assets/resource agent assets/interface.json
git -C <repo> show --stat --oneline <commit>
git -C <repo> show <target>:assets/interface.json

When generating analysis scripts, keep them temporary unless the user asks for a reusable script. Do not leave generated helper scripts in the repo.

© duorua, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in .agents/skills/maa-pipeline-history-audit of duorua/narutomobile.

  • SKILL.md
  • agents/openai.yaml

Open the folder on GitHubat commit e3ff401

Compare with similar skills

Maa Pipeline History Audit next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Maa Pipeline History Audit compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Maa Pipeline History Audit this skillduorua/narutomobile340—~1.6kAutomated safety check: PassAGPL-3.0
Skyvern Version BumpSkyvern-AI/skyvern23k—~1kAutomated safety check: NotesAGPL-3.0
Saleor Commit Workflowsaleor/saleor23k—~575Automated safety check: PassBSD-3-Clause
pybind11 Release Preparationpybind/pybind1118k—~1.7kAutomated safety check: PassCustom licence
pybind11 Release Publicationpybind/pybind1118k—~2.5kAutomated safety check: PassCustom licence
Git History Bug Auditben-manes/caffeine18k—~3.3kAutomated safety check: PassApache-2.0

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Works with

Categories

Questions about Maa Pipeline History Audit

What does Maa Pipeline History Audit do?

Audit a MaaFramework/Maa-series project's Git history to learn how Pipeline JSON, interface options, Python AgentServer CustomAction code, and related data tables evolved. Maa Pipeline History Audit is an agent skill from duorua/narutomobile. Audit a MaaFramework/Maa-series project's Git history to learn how Pipeline JSON, interface options, Python AgentServer CustomAction code, and related data tables evolved.

When should I use Maa Pipeline History Audit?

Maa Pipeline History Audit fits situations like: asked to review a Maa project from initial commit through a target commit; map action: Custom pipeline nodes to @AgentServer.customaction(...) implementations; find pipeline/custom/option patterns; produce a report plus skill-improvement recommendations.

How do I install Maa Pipeline History Audit in Claude Code?

Run `npx skills add duorua/narutomobile --skill maa-pipeline-history-audit -a claude-code`. Or copy the skill folder (.agents/skills/maa-pipeline-history-audit in duorua/narutomobile) into .claude/skills/maa-pipeline-history-audit in your project. Claude Code loads it when a task matches its description.

How do I install Maa Pipeline History Audit in Codex?

Run `npx skills add duorua/narutomobile --skill maa-pipeline-history-audit -a codex`. Or copy the skill folder (.agents/skills/maa-pipeline-history-audit in duorua/narutomobile) into .agents/skills/maa-pipeline-history-audit in your project. Codex loads it when a task matches its description.

Can I use Maa Pipeline History Audit in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add duorua/narutomobile --skill maa-pipeline-history-audit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/maa-pipeline-history-audit, .gemini/skills/maa-pipeline-history-audit, .github/skills/maa-pipeline-history-audit and .opencode/skills/maa-pipeline-history-audit in your project.

What does Maa Pipeline History Audit need to run?

Going by SKILL.md and its folder, Maa Pipeline History Audit needs the command-line tools its instructions call (git). Our summary lists: Python 3.

Does Maa Pipeline History Audit access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Maa Pipeline History Audit safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Maa Pipeline History Audit use?

Maa Pipeline History Audit is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Maa Pipeline History Audit use?

About 1.6k tokens (SKILL.md is roughly 6.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Maa Pipeline History Audit?

Skills that share tags, products or a category with Maa Pipeline History Audit: Skyvern Version Bump (Skyvern-AI/skyvern, 23k stars), Saleor Commit Workflow (saleor/saleor, 23k stars), pybind11 Release Preparation (pybind/pybind11, 18k stars) and pybind11 Release Publication (pybind/pybind11, 18k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Maa Pipeline History Audit?

duorua (a GitHub user) maintains it in duorua/narutomobile, which has 340 GitHub stars. The repository holds 11 skills in this directory. The repository was last updated on October 10, 2026.

Source: duorua/narutomobile on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.