Traceability Review logo

Traceability Review

OrganizationPopular
ai4s-research
traceability-review

Use when the user asks to review, verify, or audit a report, manuscript, or analysis in the workspace for traceability — resolving citations, flagging numbers with no source, and checking figures against the code that generated them. Emits a structured review block the app renders as reviewer findings. Verifies traceability, never "correctness".

Overview

Publisherai4s-research
Repositoryopen-science
Skill nametraceability-review
Stars
1.7K
Forks
201
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by ai4s-research on GitHub. Read the source before you install it.

Installation

Install the Traceability Review 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.
  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/ai4s-research/open-science.git /tmp/open-science
mkdir -p .claude/skills
cp -r /tmp/open-science/runtime/skills/core/traceability-review .claude/skills/traceability-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Traceability Review 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 Traceability Review 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 Traceability Review 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.

Traceability Review

Audit a workspace document (report, manuscript, or notebook) with three checks. You verify traceability — that claims trace to sources, data, and code — not truth. Never state or imply that the document is error-free.

PDF manuscripts — extract first, never guess

If the document is a PDF, do not read the raw bytes or infer its contents. Run the bundled extractor first — it pulls the text plus the concrete citation identifiers and quantitative claims deterministically, so you audit real identifiers, not ones recalled from memory:

bash
python "$XDG_CONFIG_HOME/opencode/skills/traceability-review/pdf_extract.py" MANUSCRIPT.pdf

It prints JSON: {backend, pages, chars, citations:{dois,arxiv,pmids}, claims:[{kind,text,context}], text}. Use citations as the input to Check 1, claims as the input to Check 2, and text to locate figure references for Check 3. If it returns {"error": …} (no PDF backend installed), say so plainly and fall back to whatever text you can read — do not fabricate identifiers.

Check 1 · Citation audit

  1. Extract every citation identifier from the document: DOI (10.xxxx/…), arXiv id, PMID, or title + year when no identifier is given.
  2. Resolve each against a public registry (no API key needed):
    • DOI: curl -s "https://api.crossref.org/works/<doi>"
    • arXiv: curl -s "http://export.arxiv.org/api/query?id_list=<id>"
    • PMID: curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=pubmed&id=<pmid>&retmode=json"
  3. Findings:
    • error — the identifier does not resolve (HTTP 404 / empty result).
    • warn — it resolves, but the registry's title/authors/year clearly disagree with how the document cites it.
    • warn — network unavailable: report "could not verify (offline)" rather than skipping silently.

Check 2 · Untraceable numbers

  1. List the document's quantitative claims: statistics, percentages, sample sizes, effect sizes, p-values, model scores.
  2. For each, look for its source inside the workspace: a data file, a code or notebook output, or an execution log that produces that value.
  3. Finding: warn for any number with no traceable source. Quote the exact sentence in the evidence.

Check 3 · Figure ↔ code consistency

  1. Read .openscience/provenance.jsonl in the workspace — one JSON record per line: {path, version, ts, tool, content, …}; ts is epoch seconds. It records every file version the agent wrote. The directory is hidden: read the file directly (cat .openscience/provenance.jsonl) instead of relying on ls. Fall back to file mtimes only when the file is truly absent.
  2. For each figure the document references:
    • Latest record ts for the figure file (fall back to file mtime when the figure has no record).
    • Latest record ts of the script/notebook that generates it — match by scanning record content and workspace code for the figure's filename.
  3. Findings:
    • warn — the generating code has a newer version than the figure: "figure may be stale — regenerate it from the current code".
    • warn — a referenced figure has no provenance record and no matching workspace file.

Output contract

End the reply with exactly one fenced block (the app renders it as reviewer cards; keep it as the LAST thing in the message):

review
{"findings":[{"level":"error","check":"citation","title":"DOI does not resolve","evidence":"10.9999/fake.2026 → Crossref 404"}],"note":"Traceability review — verified what could be traced. Absence of findings is not a guarantee of correctness."}
  • level: error | warn | ok · check: citation | number | figure.
  • One finding per issue; ok findings are allowed for confirmed traceable items worth stating explicitly.
  • Evidence: the exact identifier / quoted sentence / file paths, plus what you observed.
  • The note must never claim the document has no errors.

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 Traceability Review AI skill do?

Use when the user asks to review, verify, or audit a report, manuscript, or analysis in the workspace for traceability — resolving citations, flagging numbers with no source, and checking figures against the code that generated them. Emits a structured review block the app renders as reviewer findings. Verifies traceability, never "correctness".

Why use Traceability Review on TypingMind?

Because you install it once and use it with any model. Traceability Review 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 Traceability Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ai4s-research/open-science/tree/master/runtime/skills/core/traceability-review. 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 Traceability Review?

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 Traceability Review?

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

Is the Traceability Review AI skill free?

It is published on GitHub by ai4s-research. Check the repository for licensing terms. 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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