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Outline Refiner

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WILLOSCAR
outline-refiner

Planner-pass coverage + redundancy report for an outline+mapping, producing `outline/coverage_report.md` and `outline/outline_state.jsonl`.

Overview

PublisherWILLOSCAR
Repositoryresearch-units-pipeline-skills
Skill nameoutline-refiner
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 Outline Refiner 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/outline-refiner
  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/outline-refiner .claude/skills/outline-refiner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Outline Refiner 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 Outline Refiner 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 Outline Refiner 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.

Outline Refiner (Planner pass, NO PROSE)

Triggers & routing

  • Trigger: planner, dynamic outline, outline refinement, coverage report, 大纲迭代, 覆盖率报告.
  • Use when: you have outline/outline.yml + outline/mapping.tsv and want a verifiable, NO-PROSE planner pass before writing.

Goal: make the outline auditable by adding an explicit planner stage that answers:

  • Do we have enough mapped evidence per H3?
  • Are the same few papers reused everywhere?
  • Are subsection axes still generic/scaffold-y?
  • Is the outline likely to produce a paper-like structure (final ToC budget: ~6–8 H2; fewer, thicker H3s)?

This is a deterministic “planner” unit: it must not write survey prose.

Inputs

Required:

  • outline/outline.yml
  • outline/mapping.tsv

Optional (best-effort diagnosis; may be missing early in the pipeline):

  • outline/OUTLINE_BUDGET_REPORT.md (if present: explains recent merges; helps interpret mapping/coverage changes)
  • papers/paper_notes.jsonl (for evidence levels)
  • outline/subsection_briefs.jsonl (for axis specificity)
  • GOAL.md (for scope drift hints)

Outputs

  • outline/coverage_report.md (bullets + small tables; NO PROSE)
  • outline/outline_state.jsonl (append-only JSONL; one record per run)

Workflow (planner pass, NO PROSE)

  1. Parse outline/outline.yml to enumerate H2 sections + H3 subsections (section sizing / budget).
    • If outline/OUTLINE_BUDGET_REPORT.md exists, use it as the merge/change log so the coverage report can explain why structure changed.
  2. Read outline/mapping.tsv and compute per-H3 coverage and reuse hotspots.
  3. If papers/paper_notes.jsonl exists, summarize evidence levels (fulltext/abstract/title) for mapped papers.
  4. If outline/subsection_briefs.jsonl exists, compute axis specificity (generic vs specific axes) per H3.
  5. Optionally use GOAL.md to flag obvious scope drift (keywords not reflected in outline).
  6. Write outline/coverage_report.md and append a run record to outline/outline_state.jsonl.

Freeze policy

  • If outline/coverage_report.refined.ok exists, the script will not overwrite outline/coverage_report.md.

Script

Quick Start

  • uv run python .codex/skills/outline-refiner/scripts/run.py --help
  • uv run python .codex/skills/outline-refiner/scripts/run.py --workspace <workspace>

All Options

  • --workspace <dir>: workspace root
  • --unit-id <U###>: unit id (optional; for logs)
  • --inputs <semicolon-separated>: override inputs (rare; prefer defaults)
  • --outputs <semicolon-separated>: override outputs (rare; prefer defaults)
  • --checkpoint <C#>: checkpoint id (optional; for logs)

Examples

  • Planner-pass diagnostics after section-mapper:
    • uv run python .codex/skills/outline-refiner/scripts/run.py --workspace <workspace>

Troubleshooting

Issue: report is missing evidence-level or axis-specificity columns

Cause:

  • Optional inputs are missing (no papers/paper_notes.jsonl and/or no outline/subsection_briefs.jsonl).

Fix:

  • Run paper-notes and/or subsection-briefs, then rerun outline-refiner.

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 Outline Refiner AI skill do?

Planner-pass coverage + redundancy report for an outline+mapping, producing `outline/coverage_report.md` and `outline/outline_state.jsonl`.

Why use Outline Refiner on TypingMind?

Because you install it once and use it with any model. Outline Refiner 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 Outline Refiner in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/outline-refiner. 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 Outline Refiner?

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 Outline Refiner?

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

Is the Outline Refiner 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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