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Claim Evidence Matrix

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WILLOSCAR
claim-evidence-matrix

Seed `outline/claim_evidence_matrix.md` from outline+notes; claim-matrix-rewriter rewrites it later from evidence packs.

Overview

PublisherWILLOSCAR
Repositoryresearch-units-pipeline-skills
Skill nameclaim-evidence-matrix
Stars
513
Forks
39
Bundled files
1
Links
Skill sourceRepository
  • 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 WILLOSCAR on GitHub. Read the source before you install it.

Installation

Install the Claim Evidence Matrix 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/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/claim-evidence-matrix
  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/WILLOSCAR/research-units-pipeline-skills.git /tmp/research-units-pipeline-skills
mkdir -p .claude/skills
cp -r /tmp/research-units-pipeline-skills/.codex/skills/claim-evidence-matrix .claude/skills/claim-evidence-matrix
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Claim Evidence Matrix 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 Claim Evidence Matrix 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 Claim Evidence Matrix 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.

Claim–Evidence Matrix

Triggers & routing

  • Trigger: claim–evidence matrix, evidence mapping, 证据矩阵, 主张-证据对齐.
  • Use when: 写 prose 之前需要把每个小节的可检验主张与证据来源显式化（outline + paper notes 已就绪）。
  • Skip if: 缺少 outline/outline.yml 或 papers/paper_notes.jsonl。
  • Network: none.
  • Guardrail: bullets-only（NO PROSE）；每个 claim 至少 2 个证据来源（或显式说明例外）。

Make the survey’s claims explicit and auditable before writing prose.

This should stay bullets-only (NO PROSE). The goal is to make later writing easy and to prevent “template prose” from sneaking in.

Inputs

  • outline/outline.yml
  • papers/paper_notes.jsonl
  • Optional: outline/mapping.tsv

Output

  • outline/claim_evidence_matrix.md

Workflow (heuristic)

Uses: outline/outline.yml, outline/mapping.tsv.

  1. For each subsection, write 1–3 claims that are:
    • specific (mechanism / assumption / empirical finding)
    • falsifiable (“X reduces tool errors under Y evaluation”, not “X is important”)
  2. For each claim, list ≥2 evidence sources:
    • prefer different styles of evidence (method paper + eval/benchmark paper, or two competing approaches)
  3. Keep it tight: claim → evidence → (optional) caveat/limitations.
  4. If evidence is weak or only abstract-level, say so explicitly (don’t overclaim).
  5. If bibkey exists in papers/paper_notes.jsonl, include [@BibKey] next to evidence items to make later prose/LaTeX conversion smoother.

Quality checklist

  • Every subsection has ≥1 claim.
  • Each claim lists ≥2 evidence sources (or an explicit exception).
  • Claims are not copy-pasted templates (avoid “围绕…总结…” boilerplate).

Helper script (optional)

Quick Start

  • uv run python .codex/skills/claim-evidence-matrix/scripts/run.py --help
  • uv run python .codex/skills/claim-evidence-matrix/scripts/run.py --workspace <workspace>

All Options

  • See --help (this helper is intentionally minimal)

Examples

  • Generate a first-pass matrix, then refine manually:
    • Run the helper once, then refine outline/claim_evidence_matrix.md by tightening claims and adding caveats when evidence is abstract-level.

Notes

  • The helper generates a baseline matrix (claims + evidence) and never overwrites non-placeholder work; in pipeline.py --strict it will be blocked only if placeholder markers remain.

Troubleshooting

Issue: claims are generic or read like outline boilerplate

Fix:

  • Tighten each claim to a falsifiable statement and add an explicit caveat if evidence is abstract-only.

Issue: you cannot add [@BibKey] because keys are missing

Fix:

  • Run citation-verifier to generate citations/ref.bib, then use the produced keys in the matrix.

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 Claim Evidence Matrix AI skill do?

Seed `outline/claim_evidence_matrix.md` from outline+notes; claim-matrix-rewriter rewrites it later from evidence packs.

Why use Claim Evidence Matrix on TypingMind?

Because you install it once and use it with any model. Claim Evidence Matrix 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 Claim Evidence Matrix in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/claim-evidence-matrix. 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 Claim Evidence Matrix?

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 Claim Evidence Matrix?

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

Is the Claim Evidence Matrix AI skill free?

It is published on GitHub by WILLOSCAR. 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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