Agent skill
draft-polisher
Audit-style editing pass for `output/DRAFT.md`: remove template boilerplate, improve coherence, and enforce citation anchoring. **Trigger**: polish draft, de-template, coherence pass, remove boilerplate, 润色, 去套话, 去重复, 统一术语. **Use when**: a first-pass draft exists but reads like scaffolding (repetition/ellipsis/template phrases) or needs a coherence pass before global review/LaTeX. **Skip if**: the draft already reads human-grade and passes quality gates; or prose is not approved in `DECISIONS.md`. **Network**: none. **Guardrail**: do not add/remove/invent citation keys; do not move citations across subsections; do not change claims beyond what existing citations support.
Install this agent skill to your Project
npx add-skill https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/draft-polisher
SKILL.md
Draft Polisher (Audit-style editing)
Goal: turn a first-pass draft into readable survey prose without breaking the evidence contract.
This is a local polish pass: de-template + coherence + terminology + redundancy pruning.
Note: if the main issue is structural redundancy from section accumulation, push the change upstream to sections/ and use paragraph-curator before merge. draft-polisher should not be the primary place where you decide which paragraphs to keep.
Role cards (use explicitly)
Style Harmonizer (editor)
Mission: remove generator voice and make prose read like one author wrote it.
Do:
- Delete narration openers and slide navigation; replace with argument bridges.
- Vary rhythm; remove repeated template stems.
- Collapse repeated disclaimers into one front-matter methodology paragraph.
Avoid:
- Adding or removing citation keys.
- Moving citations across subsections.
Evidence Contract Guard (skeptic)
Mission: prevent polishing from inflating claims beyond evidence.
Do:
- Keep quantitative statements scoped (task/metric/constraint) or weaken them.
- Treat missing evidence as a failure signal; route upstream rather than rewriting around gaps.
Avoid:
- Overconfident language when evidence is abstract-only.
Role prompt: Style Harmonizer (editor expert)
You are the style and coherence editor for a technical survey.
Your goal is to make the draft read like one careful author wrote it, without changing the evidence contract.
Hard constraints:
- do not add/remove citation keys
- do not move citations across ### subsections
- do not strengthen claims beyond what existing citations support
High-leverage edits:
- delete generator voice (This subsection..., Next we move..., We now turn...)
- replace navigation with argument bridges (content-bearing handoffs)
- collapse repeated disclaimers into one methodology paragraph in front matter
- keep quantitative statements well-scoped (task/metric/constraint in the same sentence)
Working style:
- rewrite sentences so they carry content, not process
- vary rhythm, but avoid “template stems” repeating across H3s
Inputs
output/DRAFT.md- Optional context (read-only; helps avoid “polish drift”):
outline/outline.ymloutline/subsection_briefs.jsonloutline/evidence_drafts.jsonlcitations/ref.bib
Outputs
output/DRAFT.md(in-place refinement)output/citation_anchors.prepolish.jsonl(baseline, generated on first run by the script)
Non-negotiables (hard rules)
- Citation keys are immutable
- Do not add new
[@BibKey]keys. - Do not delete citation markers.
- If
citations/ref.bibexists, do not introduce any key that is not defined there.
- Citation anchoring is immutable
- Do not move citations across
###subsections. - If you must restructure across subsections, stop and push the change upstream (outline/briefs/evidence), then regenerate.
- No evidence inflation
- If a sentence sounds stronger than the evidence level (abstract-only), rewrite it into a qualified statement.
- When in doubt, check the subsection’s evidence pack in
outline/evidence_drafts.jsonland keep claims aligned to snippets.
- Citation shape normalization
- Merge adjacent citation blocks in the same sentence (avoid
[@a] [@b]). - Deduplicate keys inside one block (avoid
[@a; @a]). - Avoid tail-only citation dumps: keep some citations in the claim sentence itself (mid-sentence), not only paragraph end.
- Quantitative claim hygiene
- If you keep a number, ensure the sentence also states (without guessing): task type + metric definition + relevant constraint (budget/cost/tool access), and the citation is embedded in that sentence.
- Avoid ambiguous model naming (e.g., “GPT-5”) unless the cited paper uses that exact label; otherwise use the paper’s naming or a neutral description.
- No pipeline voice
- Remove scaffolding phrases like:
- “We use the following working claim …”
- “The main axes we track are …”
- “abstracts are treated as verification targets …”
- “Method note (evidence policy): …” (avoid labels; rewrite as plain survey methodology)
- “this run is …” (rewrite as survey methodology: “This survey is …”)
- “Scope and definitions / Design space / Evaluation practice …”
- “Next, we move from …”
- “We now turn to …”
- “From <X> to <Y>, ...” (title narration; rewrite as an argument bridge)
- “In the next section/subsection …”
- “Therefore/As a result, survey synthesis/comparisons should …” (rewrite as literature-facing observation)
- Also remove generator-like thesis openers that read like outline narration:
- “This subsection surveys …”
- “This subsection argues …”
Three passes (recommended)
Pass 1 — Subsection polish (structure + de-template)
Best-of-2 micro-polish (recommended):
- For any sentence/paragraph you touch, draft 2 candidate rewrites, then keep the better one.
- Choose with a simple rubric: move clarity, no template stem, citations stay anchored, and citation shape stays reader-facing (no adjacent cite blocks / dup keys).
- Do not keep both candidates. Pick one and move on (the goal is convergence, not endless rewriting).
Role split:
- Editor: rewrite sentences for clarity and flow.
- Skeptic: deletes any generic/template sentence.
Targets:
- Each H3 reads like: tension → contrast → evidence → limitation.
- Remove repeated “disclaimer paragraphs”; keep evidence-policy in one place (prefer a single paragraph in Introduction or Related Work phrased as survey methodology, not as pipeline/execution logs).
- Use
outline/outline.yml(if present) to avoid heading drift during edits. - If present, use
outline/subsection_briefs.jsonlto keep each H3’s scope/RQ consistent while improving flow. - Do a quick “pattern sweep” (semantic, not mechanical):
- delete outline narration:
This subsection ...,In this subsection ... - delete slide navigation:
Next, we move from ...,We now turn to ...,In the next section ... - delete title narration:
From <X> to <Y>, ... - replace with: content claims + argument bridges + organization sentences (no new facts/citations)
- delete outline narration:
- If
citation-injectorwas used, smooth any budget-injection sentences so they read paper-like:- Keep the citation keys unchanged.
- Avoid list-injection stems (e.g., “A few representative references include …”, “Notable lines of work include …”, “Concrete examples ... include ...”).
- Prefer integrating the added citations into an existing argument sentence, or rewrite as a short parenthetical
e.g., ...clause tied to the subsection’s lens (no new facts). - Vary phrasing; avoid repeating the same opener stem across many H3s.
- Tone: keep it calm and academic; remove hype words and repeated opener labels (e.g., literal
Key takeaway:across many H3s). - Reduce repeated synthesis stems (e.g., many paragraphs starting with
Taken together, ...); vary synthesis phrasing and keep it content-bearing.- Treat repeated "Taken together," as a generator-voice smell. If it appears more than twice (or clusters in one chapter), rewrite to vary phrasing and keep each synthesis sentence content-specific.
- Vary synthesis openings: "In summary," "Across these studies," "The pattern that emerges," "A key insight," "Collectively," "The evidence suggests," or directly state the conclusion without a synthesis marker.
- Each synthesis opening should be content-specific, not a template label.
Rewrite recipe for subsection openers (paper voice, no new facts):
- Delete:
This subsection surveys/argues.../In this subsection, we... - Replace with a compact opener that does 2–3 of these (no labels; vary across subsections):
- Content claim: the subsection-specific tension/trade-off (optionally with 1–2 embedded citations)
- Why it matters: link the claim to evaluation/engineering constraints (benchmark/protocol/cost/tool access)
- Preview: what you will contrast next and on what lens (A vs B; then evaluation anchors; then limitations)
- Example skeletons (paraphrase; don’t reuse verbatim):
- Tension-first:
A central tension is ...; ...; we contrast ... - Decision-first:
For builders, the crux is ...; ... - Lens-first:
Seen through the lens of ..., ...
- Tension-first:
Pass 2 — Terminology normalization
Role split:
- Taxonomist: chooses canonical terms and synonym policy.
- Integrator: applies consistent replacements across the draft.
Targets:
- One concept = one name across sections.
- Headings, tables, and prose use the same canonical terms.
Pass 3 — Redundancy pruning (global repetition)
Role split:
- Compressor: collapses repeated boilerplate.
- Narrative keeper: ensures removing repetition does not break the argument chain.
Targets:
- Cross-section repeated intros/outros are removed.
- Only subsection-specific content remains inside subsections.
Script
Quick Start
python .codex/skills/draft-polisher/scripts/run.py --helppython .codex/skills/draft-polisher/scripts/run.py --workspace workspaces/<ws>
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
-
First polish pass (creates anchoring baseline
output/citation_anchors.prepolish.jsonl):python .codex/skills/draft-polisher/scripts/run.py --workspace workspaces/<ws>
-
Reset the anchoring baseline (only if you intentionally accept citation drift):
- Delete
output/citation_anchors.prepolish.jsonl, then rerun the polisher.
- Delete
Acceptance checklist
- No
TODO/TBD/FIXME/(placeholder). - No
…or...truncation. - No repeated boilerplate sentence across many subsections.
- Citation anchoring passes (no cross-subsection drift).
- Each H3 has at least one cross-paper synthesis paragraph (>=2 citations).
Troubleshooting
Issue: polishing causes citation drift across subsections
Fix:
- Keep citations inside the same
###subsection; if restructuring is intentional, deleteoutput/citation_anchors.prepolish.jsonland regenerate a new baseline.
Issue: draft polishing is requested before writing approval
Fix:
- Record the relevant approval in
DECISIONS.md(typicallyApprove C2) before doing prose-level edits.
Recommended Agent Skills
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thesis-compile-review
对中文毕业论文进行编译、warning 分级、模板模式检查、数据与引用复查,并把问题回写成可继续迭代的 review checklist。 **Trigger**: 毕业论文编译检查, thesis compile review, warning 分级, 终稿复查, main.pdf 检查. **Use when**: 论文已经回写到 TeX 交付层,需要确认是否真正达到“可提交”的质量,而不是只做到能编译。 **Skip if**: 还处于中间层重构阶段,`chapters/*.tex` 尚未形成稳定交付稿。 **Network**: none. **Guardrail**: 不在这里重构章节主线;如果发现结构问题,明确回退到上游修复。
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. **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. **Use when**: you are in C5 (prose allowed) and need the paper-like shell to stop the draft reading like stitched subsections. **Skip if**: `Approve C2` is missing in `DECISIONS.md`, or `citations/ref.bib` is missing. **Network**: none. **Guardrail**: no invented facts/citations; no pipeline jargon in final prose; no repeated evidence disclaimers; only use keys present in `citations/ref.bib`.
thesis-question-list
维护中文毕业论文的 `codex_md/question_list.md`:把本轮问题、边界、优先级、协作方案和验收口径结构化,作为整条 thesis pipeline 的控制面。 **Trigger**: 毕业论文问题清单, thesis question list, 论文修改清单, 本轮目标, 结构问题梳理, review问题整理. **Use when**: 你已经有一批材料或上一轮 review 结果,需要明确这一轮到底修什么、不修什么,并给后续重构与编译复查提供统一入口。 **Skip if**: 当前只是在做一次性局部措辞修改,且没有形成新一轮结构/证据/编译问题。 **Network**: none. **Guardrail**: 不在这里写正文;不把问题单写成长篇散文;每条问题必须可执行、可验收。
novelty-matrix
Create a novelty/prior-work matrix comparing the submission’s contributions against related work (overlaps vs deltas). **Trigger**: novelty matrix, prior-work matrix, overlap/delta, 相关工作对比, 新颖性矩阵. **Use when**: peer review 中评估 novelty/positioning,需要把贡献与相关工作逐项对齐并写出差异点证据。 **Skip if**: 缺少 claims(先跑 `claims-extractor`)或你不打算做新颖性定位分析。 **Network**: none (retrieval of additional related work is out-of-scope unless provided). **Guardrail**: 明确 overlap 与 delta;尽量给出可追溯证据来源(来自稿件/引用/作者陈述)。
protocol-writer
Write a systematic review protocol into `output/PROTOCOL.md` (databases, queries, inclusion/exclusion, time window, extraction fields). **Trigger**: protocol, PRISMA, systematic review, inclusion/exclusion, 检索式, 纳入排除. **Use when**: systematic review pipeline 的起点(C1),需要先锁定 protocol 再开始 screening/extraction。 **Skip if**: 不是做 systematic review(或 protocol 已经锁定且不允许修改)。 **Network**: none. **Guardrail**: protocol 必须包含可执行的检索与筛选规则;需要 HUMAN 签字后才能进入 screening。
rubric-writer
Write a rubric-based peer review report (`output/REVIEW.md`) using extracted claims and evidence gaps (novelty/soundness/clarity/impact). **Trigger**: rubric review, referee report, peer review write-up, 审稿报告, REVIEW.md. **Use when**: peer-review pipeline 的最后阶段(C3),已有 `output/CLAIMS.md` + `output/MISSING_EVIDENCE.md`(以及可选 novelty matrix)。 **Skip if**: 上游产物未就绪(claims/evidence gaps 缺失)或你不打算输出完整审稿报告。 **Network**: none. **Guardrail**: 给可执行建议(actionable feedback),并覆盖 novelty/soundness/clarity/impact;避免泛泛而谈。
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