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Opener Variator

Community
WILLOSCAR
opener-variator

Rewrite subsection openers so they stop reading like a generated table-of-contents: remove \"overview/narration\" stems and reduce repeated opener cadences across H3s.

Overview

PublisherWILLOSCAR
Repositoryresearch-units-pipeline-skills
Skill nameopener-variator
Stars
513
Forks
39
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 WILLOSCAR on GitHub. Read the source before you install it.

Installation

Install the Opener Variator 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/opener-variator
  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/opener-variator .claude/skills/opener-variator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Opener Variator 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 Opener Variator 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 Opener Variator 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.

Opener Variator (H3 first paragraph rewrite)

Triggers & routing

  • Trigger: opener variator, opener rewrite, rewrite openers, overview opener, 开头改写, 小节开头, 去overview, 去旁白.

Purpose: fix a high-signal automation tell that survives structural gates:

  • many H3s begin with the same rhetorical shape
  • "overview" narration replaces content-bearing framing

This skill is intentionally narrow:

  • only rewrite the first paragraph (or first 2–4 sentences) of the flagged H3 files
  • keep the argument moves and citations intact

Inputs

Required:

  • output/WRITER_SELFLOOP_TODO.md (Style Smells section)
  • the referenced sections/S<sub_id>.md files

Optional (helps you stay aligned):

  • outline/writer_context_packs.jsonl (use opener_mode, tension_statement, thesis)

Outputs

Run this targeted pass immediately after style-harmonizer and before logic polish. The deterministic script is a certification adapter: it refuses to create the marker while the latest writer-selfloop report still names flagged files or predates any sections/*.md file. Perform the semantic rewrite through this Skill or the responsible upstream writer, rerun writer-selfloop, then retry the adapter. A passing marker records the certified Section-tree SHA256.

  • Updated sections/S<sub_id>.md files (still body-only; no headings)

Workflow (route from the self-loop report)

  1. Open output/WRITER_SELFLOOP_TODO.md and locate ## Style Smells.
  2. Treat the flagged sections/S*.md list as the only scope for this pass.
  3. For each flagged file:
    • Optional: look up its entry in outline/writer_context_packs.jsonl and read opener_mode / tension_statement / thesis to stay aligned.
    • Do the real rewrite upstream in subsection-writer or chapter-lead-writer; do not rely on blind local regex passes.
    • Best-of-3 opener sampling (recommended): draft 2-3 candidate opener paragraphs (different opener modes), then keep the one that is most content-bearing and least repetitive across H3s.
  4. Rerun writer-selfloop and confirm the Style Smells list shrinks.

Role prompt: Opener Editor (paper voice)

text
You are rewriting the opening paragraph of a survey subsection.

Goal:
- replace narration/overview openers with a content-bearing framing
- vary opener cadence across subsections so the paper reads authored

Constraints:
- do not invent facts
- do not add/remove/move citation keys
- do not change the subsection’s thesis

Checklist:
- sentence 1 is content-bearing (tension/decision/failure/protocol/contrast), not “what we do in this section”
- paragraph 1 ends with a clear thesis/takeaway
- no slide navigation (“Next, we…”, “In this subsection…”, “This section provides an overview…“)

What to delete (high-signal narration)

Rewrite immediately if the opener contains any of:

  • “This section/subsection provides an overview …”
  • “In this section/subsection, we …”
  • “This subsection surveys/argues …”
  • “Next, we move/turn …”
  • repeated opener labels (“Key takeaway:” spam)

What to replace with (opener moves)

Pick one opener mode per H3 (the writer pack may suggest opener_mode). Do not copy labels; write as natural prose.

Allowed opener moves (choose 1; keep it concrete):

  • Tension-first: state the real trade-off; why it matters; end with thesis.
  • Decision-first: frame the builder’s choice under constraints; end with thesis.
  • Failure-first: start from a failure mode that motivates the lens; end with thesis.
  • Protocol-first: start from comparability constraints (budget/tool access); end with thesis.
  • Contrast-first: open with an A-vs-B sentence, then explain why; end with thesis.
  • Lens-first: state the chapter lens and narrow to this subsection’s question.

Mini examples (paraphrase; do not copy)

Bad (overview narration):

  • This subsection provides an overview of tool interfaces for agents.

Better (content-bearing):

  • Tool interfaces define what actions are executable; interface contracts therefore determine which evaluation claims transfer across environments.

Bad (process narration):

  • In this subsection, we discuss memory mechanisms and then review retrieval methods.

Better (tension-first):

  • Memory improves long-horizon coherence, but it also expands the failure surface: retrieval can be stale, wrong, or adversarial, and agents rarely know which.

Done checklist

  • No flagged file starts with “overview/narration” stems.
  • Paragraph 1 ends with a thesis/takeaway (same meaning).
  • Citation keys are unchanged (no adds/removes/moves).
  • writer-selfloop still PASSes and Style Smells shrink.

Script

Quick Start

  • uv run python .codex/skills/opener-variator/scripts/run.py --workspace <workspace>

All Options

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

Examples

  • Rewrite openers in a survey workspace:
    • uv run python .codex/skills/opener-variator/scripts/run.py --workspace workspaces/survey-llm-agents

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 Opener Variator AI skill do?

Rewrite subsection openers so they stop reading like a generated table-of-contents: remove \"overview/narration\" stems and reduce repeated opener cadences across H3s.

Why use Opener Variator on TypingMind?

Because you install it once and use it with any model. Opener Variator 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 Opener Variator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/opener-variator. 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 Opener Variator?

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 Opener Variator?

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

Is the Opener Variator 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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