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Subject Audit

CommunityPopular
yjl9903
subject-audit

Review AnimeGarden animation resource-to-Bangumi Subject bindings for a specified time range, identify missing or incorrect bindings, and report evidence-backed Subject search improvements. Use for manager-side audits; online binding corrections are optional and require explicit user confirmation.

Overview

Publisheryjl9903
RepositoryAnimeGarden
Skill namesubject-audit
Stars
1.4K
Forks
46
Bundled files
6
LicenseAGPL-3.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by yjl9903 on GitHub. Read the source before you install it.

Installation

Install the Subject Audit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/yjl9903/AnimeGarden/tree/main/.agents/skills/subject-audit
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/yjl9903/AnimeGarden.git /tmp/AnimeGarden
mkdir -p .claude/skills
cp -r /tmp/AnimeGarden/.agents/skills/subject-audit .claude/skills/subject-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Subject Audit in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Subject Audit on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Subject Audit is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AnimeGarden Subject Audit

Review every animation resource in a requested time range, determine which registered Bangumi Subject it most likely belongs to, and assess both its current binding and the target Subject's search coverage. The current subjectId and search configuration are evidence about system state rather than the basis for attribution.

The standard path produces a read-only audit report. An optional confirmed step can apply reliable target Subject IDs to current online resources before the report is finalized.

Read references/audit-rules.md for the attribution standard, search diagnosis, and report format.

Workflow

1. Collect the audit evidence

Create a fresh task-scoped temporary directory and run from this skill directory:

bash
node scripts/fetch-resources.mjs --start <ISO timestamp> --end <ISO timestamp> --output <tmp-dir>/resources.jsonl
node scripts/fetch-subjects.mjs --output <tmp-dir>/subjects.jsonl

The first file contains all animation resources in the interval and their current subjectId; the second contains the complete registered Subject index. Confirm the declared counts, then review the resource file in manageable batches until every record has been assessed.

2. Attribute every resource

Identify the work, installment, and episode from the complete release context. For an existing binding, evaluate the bound Subject alongside other plausible registered Subjects. For an unbound resource, search the registry using the title forms suggested by the release.

Weigh the evidence that is informative for that resource: names and aliases, season or part context, episode progression, publication timing, related Subjects, and neighboring releases. Their relevance varies by title family, so attribution follows the explanation that best fits the whole release context. Record uncertainty when the evidence does not support one clear Subject.

When several registered installments share a practical title or continue episode numbering, fetch the direct predecessor and sequel relations for the plausible candidates:

bash
node scripts/fetch-subject-relations.mjs --subject-ids <id[,id...]> --output <tmp-dir>/subject-relations.jsonl

Use this optional evidence to navigate quarter and part boundaries in long-running families such as ONE PIECE and BLEACH. Relations narrow the candidate set; the resource title, episode sequence, and publication context still determine the attribution.

3. Check the target Subject's effective match

For every resource with a reliably established target, write one record per line to <tmp-dir>/targets.jsonl:

json
{ "provider": "mikan", "providerId": "123", "targetSubjectId": 456 }

When the target list is non-empty, run the production-equivalent match check:

bash
node scripts/check-subject-matches.mjs --resources <tmp-dir>/resources.jsonl --subjects <tmp-dir>/subjects.jsonl --targets <tmp-dir>/targets.jsonl --output <tmp-dir>/matches.jsonl

The result answers whether the selected target Subject matches each resource under the current production title normalization and search semantics. It also identifies the exact failed condition: an include miss or formatting-only near miss, missing keywords, matched excludes, and violated time boundaries. Combine those diagnostics with the current subjectId to describe the present search coverage and binding state. This check measures the current search configuration; the release evidence from step 2 remains the basis for attribution.

4. Diagnose Subject search coverage

Inspect both coverage and selectivity in the target Subject's current search settings and, for a wrong existing binding, the currently bound Subject as well. A successful match confirms coverage but can still be too broad; short or common terms need supporting context from actual release families. Recommend the focused include, keywords, exclude, after, or before changes that best separate the current Subjects. Time signals may complement titles when resource publication ranges provide a reliable present-day boundary.

Treat include-near-miss as an inspection hint: the include becomes a substring after ignoring spaces, punctuation, and symbol separators. The production result remains unchanged, and the safe search adjustment depends on the complete release family.

5. Optionally apply confirmed resource bindings

If the user asks to apply reliable target bindings before the Subject search configuration is updated, generate a preview:

bash
node scripts/patch-resource-bindings.mjs --input <tmp-dir>/matches.jsonl --output <tmp-dir>/binding-patch-preview.jsonl

Present the exact resource count and current-to-target Subject mappings from the preview. After the user explicitly confirms that patch set, apply that preview file with the confirmed count:

bash
node scripts/patch-resource-bindings.mjs --input <tmp-dir>/binding-patch-preview.jsonl --output <tmp-dir>/binding-patch-results.jsonl --apply --expected-count <confirmed count>

Apply mode reads ADMIN_SECRET from the project root .env and records server results in a journal. Keep the audit read-only until the user confirms the exact previewed patch set.

6. Report the findings

Write the report in Chinese using the tables defined in the reference. Include the scan interval, summary counts, linked resource examples, and the correct numeric Subject ID with its Bangumi link whenever attribution is reliable. Show both current and correct Subjects for a wrong binding, and state the remaining ambiguity for unresolved cases.

When the optional online patch ran, include its changed, unchanged, and failed counts and identify any resource that was not completed.

Present Subject search recommendations as an unranked list and keep them focused on current title patterns and Subject search fields.

Compare with an earlier round when an actual prior evidence snapshot is available.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Subject Audit AI skill do?

Review AnimeGarden animation resource-to-Bangumi Subject bindings for a specified time range, identify missing or incorrect bindings, and report evidence-backed Subject search improvements. Use for manager-side audits; online binding corrections are optional and require explicit user confirmation.

Why use Subject Audit on TypingMind?

Because you install it once and use it with any model. Subject Audit is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Subject Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yjl9903/AnimeGarden/tree/main/.agents/skills/subject-audit. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Subject Audit?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Subject Audit?

As many as you like. As long as a model supports skills, you can use Subject Audit with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Subject Audit AI skill free?

Yes. It is published on GitHub by yjl9903 under the AGPL-3.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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