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Front Matter Writer

Community
WILLOSCAR
front-matter-writer

Write the survey's front matter files (Abstract, Introduction, Related Work, Discussion, Conclusion) in paper voice, with high citation density and a single evidence-policy paragraph.

Overview

PublisherWILLOSCAR
Repositoryresearch-units-pipeline-skills
Skill namefront-matter-writer
Stars
513
Forks
39
Bundled files
16
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.

  • 16 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 Front Matter Writer 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/front-matter-writer
  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/front-matter-writer .claude/skills/front-matter-writer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Front Matter Writer 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 Front Matter Writer 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 Front Matter Writer 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.

Front Matter Writer (compatibility router)

Triggers & routing

  • Trigger: front matter writer, introduction writer, related work writer, abstract writer, discussion writer, conclusion writer, 引言, 相关工作, 摘要, 讨论, 结论.

Purpose: produce the paper-level shell while preserving the current output contract and approval gate.

Load Order

Always read:

  • references/overview.md

Read by task:

  • references/abstract_archetypes.md for sections/abstract.md
  • references/context_projection.md when adjusting how chapter briefs are projected into Introduction / Related Work context
  • references/introduction_jobs.md for Introduction body files
  • references/related_work_positioning.md for Related Work body files
  • references/discussion_conclusion_patterns.md for sections/discussion.md and sections/conclusion.md
  • references/forbidden_stems.md before revising any reader-facing prose
  • references/examples_good.md and references/examples_bad.md for calibration only

Machine-readable assets:

  • assets/front_matter_context.schema.json
  • assets/front_matter_context_schema.json (compatibility alias for checklist-aligned naming)
  • assets/front_matter_contract.json
  • assets/front_matter_templates.json
  • assets/front_matter_context_projection.json

Inputs

Required:

  • DECISIONS.md with Approve C2
  • outline/outline.yml
  • outline/mapping.tsv
  • citations/ref.bib

Optional but useful:

  • GOAL.md
  • queries.md
  • papers/retrieval_report.md
  • papers/core_set.csv
  • outline/coverage_report.md
  • outline/writer_context_packs.jsonl

Outputs

Keep the current output contract:

  • sections/abstract.md
  • sections/S<sec_id>.md for front-matter H2 bodies
  • sections/discussion.md
  • sections/conclusion.md
  • output/FRONT_MATTER_REPORT.md
  • output/FRONT_MATTER_CONTEXT.json, including the base template bank, any selected domain overlay, and their repository-relative SHA-256 hashes

Compatibility mode

Current mode is reference-first with script compatibility:

  • writing guidance lives in references/
  • the structured job contract lives in assets/front_matter_contract.json
  • the hook bank used by the script lives in assets/front_matter_templates.json
  • scripts/run.py still owns approval checks, metadata parsing, deterministic hook selection, file emission, and context-sidecar writing

That means:

  • preserve current file shapes and approval behavior
  • treat the assets as the active semantic source for section jobs, hook banks, and render guardrails
  • avoid moving prose policy back into SKILL.md

Script boundary

Use scripts/run.py as a deterministic helper for:

  • approval gate enforcement
  • front-matter file discovery / path selection
  • metadata extraction
  • rendering from assets/front_matter_contract.json + assets/front_matter_templates.json
  • writing the report and context sidecar

Do not treat the script as the main place for long-form writing guidance.

Output shape rules

Keep these stable:

  • sections/abstract.md starts with ## Abstract or ## 摘要
  • Introduction / Related Work sections/S<sec_id>.md files are body-only
  • sections/discussion.md includes ## Discussion
  • sections/conclusion.md includes ## Conclusion
  • methodology note appears once in normal prose, not as pipeline/process narration

Quick Start

  • uv run python .codex/skills/front-matter-writer/scripts/run.py --workspace <workspace>

Troubleshooting

  • If the front matter sounds narrated, reload references/forbidden_stems.md and references/examples_bad.md.
  • If Related Work turns into a survey list, reload references/related_work_positioning.md.
  • If the asset and the references drift, fix the asset/routing inside this skill package rather than expanding SKILL.md again.

Execution notes

When running this skill in compatibility mode, scripts/run.py currently reads these inputs directly:

  • DECISIONS.md for Approve C2
  • outline/outline.yml to resolve Introduction / Related Work section ids
  • outline/mapping.tsv for front-matter citation placement context
  • citations/ref.bib for citation-key scope
  • GOAL.md, queries.md, papers/retrieval_report.md, papers/core_set.csv, outline/coverage_report.md, and outline/writer_context_packs.jsonl as optional context sources

Script

Quick Start

  • uv run python .codex/skills/front-matter-writer/scripts/run.py --workspace <workspace>

All Options

  • --workspace <dir>
  • --unit-id <id>
  • --inputs <path1;path2>
  • --outputs <path1;path2>
  • --checkpoint <C*>

Examples

  • uv run python .codex/skills/front-matter-writer/scripts/run.py --workspace <workspace>

Troubleshooting

  • If DECISIONS.md lacks Approve C2, the script will write a checkpoint block and exit.
  • If citations/ref.bib or outline/outline.yml is missing, restore those inputs before rerunning.
  • If the front matter feels too template-like, inspect the hook banks / section job graph before touching the script.

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 Front Matter Writer AI skill do?

Write the survey's front matter files (Abstract, Introduction, Related Work, Discussion, Conclusion) in paper voice, with high citation density and a single evidence-policy paragraph.

Why use Front Matter Writer on TypingMind?

Because you install it once and use it with any model. Front Matter Writer 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 Front Matter Writer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/front-matter-writer. 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 Front Matter Writer?

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 Front Matter Writer?

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

Is the Front Matter Writer 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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