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section-logic-polisher

Logic coherence pass for per-H3 section files: enforce a clear paragraph-1 thesis and surface paragraph-island risks (connector stats are diagnostic, not a quota) before merging.

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含まれるファイル(2)

  • SKILL.md6.0 KB
  • scripts/run.py11.2 KB

SKILL.md(原文)

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Section Logic Polisher (thesis + argument bridges)

Triggers & routing

  • Trigger: logic polisher, section logic, thesis statement, connectors, 段落逻辑, 连接词, 论证主线, 润色逻辑.
  • Use when: sections/S*.md exist but read like paragraph islands; you want a targeted, debuggable self-loop before section-merger.

Purpose: close the main “paper feel” gap that remains even when a subsection is long and citation-dense:

  • missing/weak thesis (paragraph 1 never commits to a claim)
  • weak inter-paragraph flow (paragraph islands; no content-bearing bridges)

This is a local, per-H3 polish step that happens after drafting and before merging.

Note: if the main problem is paragraph-count overgrowth, run paragraph-curator after this check. It only merges adjacent paragraph boundaries and preserves all prose; semantic redundancy still belongs to the owning writer.

What this skill blocks on (and what it does not)

Blocking (must fix):

  • paragraph 1 lacks an explicit thesis / takeaway (a content claim)

Non-blocking (diagnostic only):

  • connector word counts (e.g., “moreover/however/therefore”). Counts are a proxy for paragraph islands, but forcing them as a quota often creates “generator cadence” (paragraph-initial adverbs). Treat these stats as signals, not goals.

Role prompt: Logic Editor (argument flow)

You are the logic editor for one survey subsection.

Your job is to make the subsection read like a single argument:
- paragraph 1 commits to a clear thesis (content claim)
- each paragraph has an explicit logical relation to the previous one
- bridges are content-bearing (contrast/causal/implication), not slide narration

Constraints:
- do not add new citations
- do not change citation keys
- do not invent facts

Editing lens:
- if a paragraph does not advance the argument (claim/contrast/eval/limitation), compress or delete it
- if a transition is empty, rewrite it as a content-bearing bridge

Inputs

  • sections/ (expects H3 body files like S<sec>_<sub>.md)
  • outline/subsection_briefs.jsonl (use thesis + paragraph_plan[].connector_phrase as intent)
  • Optional: outline/writer_context_packs.jsonl (preferred; has trimmed anchors/comparisons + must_use)

Outputs

  • output/SECTION_LOGIC_REPORT.md (PASS/FAIL for thesis; connector stats shown for diagnosis)

Manual / LLM-first (in place):

  • Update the H3 body files under sections/ (e.g., sections/S<sec>_<sub>.md) to fix thesis/bridges (no new citations; keep keys stable)

Workflow (self-loop)

  1. Run the checker script to surface the exact failing files.

  2. For each failing H3 file:

  • Work on the concrete H3 body file (pattern): sections/S<sec>_<sub>.md

  • Use outline/subsection_briefs.jsonl as the source of truth for the subsection thesis and paragraph-plan intent.

  • If available, prefer outline/writer_context_packs.jsonl to stay aligned with must_use anchors/constraints (no new cites).

  • Thesis (blocking)

    • Make paragraph 1 end with a conclusion-first thesis sentence.
    • Prefer a content claim, not meta narration. Avoid repetitive openers like This subsection argues/surveys ....
    • Minimal shape (3 sentences; paraphrase, don’t copy):
      1. claim / tension
      2. why it matters (protocol/evaluation relevance)
      3. how the subsection will resolve it (what contrasts/anchors it will use)
  • Flow (fix only when needed)

    • Add 1–2 short bridges where paragraphs feel disconnected.
    • Prefer subject-first sentences and mid-sentence glue (because/while/which) over paragraph-start adverbs.
    • Avoid PPT navigation (Next, we ..., We now turn to ...).
  1. Rerun the checker until output/SECTION_LOGIC_REPORT.md is PASS, then proceed to paragraph-curator, evaluation-anchor-checker, the final argument-selfloop snapshot, and merge.

Examples

Thesis signal (paragraph 1)

Bad (topic setup only):

  • Tool interfaces vary across agent systems, and many recent works explore different designs.

Better (conclusion-first claim):

  • A central tension in tool interfaces is balancing expressivity with verifiability; as a result, interface contracts often determine which evaluation claims transfer across environments.

Bad (meta narration):

  • This subsection argues that memory is important for agents.

Better (content claim):

  • Memory designs trade off retrieval reliability against write-time contamination, and this trade-off shows up as distinct failure modes under fixed evaluation protocols.

Bridges (avoid paragraph islands)

Bad (no relation):

  • X does ... (para 2)
  • Y does ... (para 3)

Better (explicit tie):

  • Whereas X optimizes for <axis>, Y shifts the bottleneck to <axis>; under fixed budgets, this changes whether the reported gains reflect better planning or simply more expensive search.

Done criteria

  • output/SECTION_LOGIC_REPORT.md shows - Status: PASS
  • No section file contains placeholders (TODO/…/...) or outline meta markers (Intent:/RQ:/Evidence needs:)
  • Every H3 has a clear paragraph-1 thesis; bridges are added only where flow is actually broken

Script

Quick Start

  • uv run python .codex/skills/section-logic-polisher/scripts/run.py --workspace <workspace>

Notes:

  • The script is a checker; it does not rewrite prose.
  • Connector stats are printed for diagnosis. Do not “write to the counter”; write to the argument.

All Options

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

Examples

  • Default run:

    uv run python .codex/skills/section-logic-polisher/scripts/run.py --workspace <workspace>

  • Explicit output path (rare override; prefer defaults):

    uv run python .codex/skills/section-logic-polisher/scripts/run.py --workspace <workspace> --outputs output/SECTION_LOGIC_REPORT.md

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

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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

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