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Anchor Sheet

Community
WILLOSCAR
anchor-sheet

Extract per-subsection “anchor facts” (NO PROSE) from evidence packs so the writer is forced to include concrete numbers/benchmarks/limitations instead of generic summaries.

Overview

PublisherWILLOSCAR
Repositoryresearch-units-pipeline-skills
Skill nameanchor-sheet
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 Anchor Sheet 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/anchor-sheet
  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/anchor-sheet .claude/skills/anchor-sheet
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Anchor Sheet 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 Anchor Sheet 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 Anchor Sheet 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.

Anchor Sheet (evidence → write hooks) [NO PROSE]

Triggers & routing

  • Trigger: anchor sheet, anchor facts, numeric anchors, evidence hooks, 写作锚点, 数字锚点, 证据钩子.
  • Use when: outline/evidence_drafts.jsonl exists and you want stronger, evidence-anchored writing in sections/*.md.

Purpose: make “what to actually say” explicit:

  • select quantitative snippets (numbers/percentages)
  • select evaluation anchors (benchmarks/datasets/metrics)
  • select limitations/failure hooks

This prevents the writer from producing paragraph-shaped but content-poor prose.

Inputs

  • outline/evidence_drafts.jsonl
  • citations/ref.bib

Outputs

  • outline/anchor_sheet.jsonl

Output format (outline/anchor_sheet.jsonl)

JSONL (one object per H3 subsection).

Required fields:

  • sub_id, title
  • anchors (list; each anchor has hook_type, text, citations, and optional paper_id/evidence_id/pointer)

Workflow

  1. Read outline/evidence_drafts.jsonl.
  2. Prefer anchors that contain:
    • a number (%, counts, scores)
    • an explicit benchmark/dataset/metric name
    • an explicit limitation/failure statement
  3. Filter anchors to only citation keys present in citations/ref.bib.
  4. Write outline/anchor_sheet.jsonl.

Quality checklist

  • Every H3 has >=10 cite-backed anchors (A150++ hard target).
  • At least 1 anchor contains digits when the evidence pack contains digits.
  • No placeholders (TODO/…/(placeholder)).

Consumption policy (for C5 writers)

Anchors are intended to prevent “long but empty” prose. Treat them as must-use hooks, not optional ideas.

Recommended minimums per H3 (A150++):

  • =3 protocol anchors (benchmark/dataset/metric/budget/tool access)

  • =3 limitation/failure hooks (concrete, not generic “future work”)

  • If digits exist in the evidence pack: include >=1 cited numeric anchor (digit + citation in the same paragraph)

Note:

  • Anchor text is trimmed for readability and does not include ellipsis markers (to reduce accidental leakage into prose).

Script

Quick Start

  • uv run python .codex/skills/anchor-sheet/scripts/run.py --help
  • uv run python .codex/skills/anchor-sheet/scripts/run.py --workspace <workspace>

All Options

  • --workspace <dir>
  • --unit-id <U###>
  • --inputs <semicolon-separated>
  • --outputs <semicolon-separated>
  • --checkpoint <C#>

Examples

  • Default IO:
    • uv run python .codex/skills/anchor-sheet/scripts/run.py --workspace <workspace>
  • Explicit IO:
    • uv run python .codex/skills/anchor-sheet/scripts/run.py --workspace <workspace> --inputs "outline/evidence_drafts.jsonl;citations/ref.bib" --outputs "outline/anchor_sheet.jsonl"

Refinement marker (recommended; prevents churn)

When you are satisfied with anchor facts (and they are actually subsection-specific), create:

  • outline/anchor_sheet.refined.ok

This is an explicit "I reviewed/refined this" signal:

  • prevents scripts from regenerating and undoing your work
  • (in strict runs) can be used as a completion signal before writing

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 Anchor Sheet AI skill do?

Extract per-subsection “anchor facts” (NO PROSE) from evidence packs so the writer is forced to include concrete numbers/benchmarks/limitations instead of generic summaries.

Why use Anchor Sheet on TypingMind?

Because you install it once and use it with any model. Anchor Sheet 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 Anchor Sheet in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/anchor-sheet. 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 Anchor Sheet?

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 Anchor Sheet?

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

Is the Anchor Sheet 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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