本文へ移動
cccskills
無料GitHub で公開

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.

インストール方法を見る

含まれるファイル(2)

  • SKILL.md5.3 KB
  • scripts/run.py3.3 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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)

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

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Internal template for creating or refactoring a skill into the repository's reference-first shape. **Trigger**: reference-first template, blueprint skill, create a reusable skill, refactor a script-heavy skill. **Use when**: you need a lean `SKILL.md`, explicit `references/`, machine-readable `assets/`, and a minimal deterministic `run.py`.

日本語の概要は準備中です。原文の説明を表示しています。

WILLOSCAR/research-units-pipeline-skills5132026年10月5日 更新

Download a small corpus of open-access arXiv survey/review PDFs about agentic systems and extract text for style learning.

日本語の概要は準備中です。原文の説明を表示しています。

WILLOSCAR/research-units-pipeline-skills5132026年10月5日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

WILLOSCAR/research-units-pipeline-skills5132026年10月5日 更新

Curate reader-facing survey tables for the Appendix (clean layout + high information density), using only in-scope evidence and existing citation keys.

日本語の概要は準備中です。原文の説明を表示しています。

WILLOSCAR/research-units-pipeline-skills5132026年10月5日 更新

Build a final, machine-readable argument snapshot from current H3 sections and refresh section fingerprints before merge.

日本語の概要は準備中です。原文の説明を表示しています。

WILLOSCAR/research-units-pipeline-skills5132026年10月5日 更新

Audit one research Workspace for declared Unit outputs and Pipeline target Artifacts, writing `output/CONTRACT_REPORT.md`; use for mid-Run coverage snapshots or final delivery completeness, not deep provenance integrity.

日本語の概要は準備中です。原文の説明を表示しています。

WILLOSCAR/research-units-pipeline-skills5132026年10月5日 更新

WILLOSCAR のスキルをすべて見る

このスキルの問題を報告する