condense

name	condense
description	精简技能。对整篇文档剔除**所有**不必要的、以及上下文/全文其他地方已有重复的内容：跨节重复的 claim/数字、零信息增量段落、死定义/死图表、冗长表达。执行 SCIPAPER_STANDARD §5.3（改写、删减、精简，而不是堆叠）：默认方向永远是更短，删除 > 原地压缩 > 等长改写，增长最后且必须有记录理由。每个事实只保留一个 canonical 位置，副本删除或改为交叉引用。保真不变量（§6）全程保护。Use when 用户说 "精简" / "太长了" / "重复" / "废话太多" / "condense" / "trim" / "去冗余"，或 paper-review 维度 I 的冗余 finding 需要执行修复时。
disable-model-invocation	false
argument-hint	<file_path> [--section <name>] [--max-iter N] [--report-only] [--field <name>]

Normative authority: docs/SCIPAPER_STANDARD.md. §5.3 (condense, do not accumulate) is the policy this skill executes; the policy text lives ONLY in the standard — this skill implements it and never restates a competing version. §6 rewrite eligibility defines the fidelity invariants every deletion and compression must preserve.

condense — 精简：one canonical home per fact

0. What this skill does

A whole-document sweep that removes all content which is unnecessary or already stated elsewhere in the document, then proves the result with the mechanical length gate. It is the plugin's redundancy/length action surface: paper-review dimension I detects redundancy as findings; condense executes the removal. --report-only stops after §1: the ranked redundancy map is returned with no deletion applied.

Boundary with /sci-paper:de-ai (canonical statement, mirrored there): de-ai removes the authorship fingerprint (L0-L4 signals, structural tells, voice; its Pass-3 length cap only guards its own rewrites against growth). condense removes redundancy and length (cross-document deduplication under one-canonical-home-per-fact). At the overlap (verbose AI-isms such as rule-of-three padding or connective stacking), de-ai DETECTS the tell; when the right fix is deletion rather than rewrite, the deletion EXECUTES under condense's ranked sweep.

0.1 Non-negotiable rules

1. Preference order: delete > condense-in-place > same-length rewrite. Growth is forbidden; the only exceptions are §5.3's own (author-requested new content, or a source-verified scientific necessity), each with a recorded justification.
2. Fidelity invariants (§6). Every surviving passage preserves claims, numbers, units, uncertainties, citations, named entities, comparison direction, negation, causal direction, scope, stance, qualifiers, and logical dependencies. Never delete a fact's sole support: dedup keeps exactly one canonical home; the copies are deleted or replaced by a cross-reference — the fact itself never vanishes.
3. One canonical home per fact. A number, definition, or claim stated in two places is a defect with one exception (rule 4). The canonical home is where the reader needs it — normally the most detailed, first-consumed occurrence.
4. Genre carve-out. The abstract's summary of key results and the conclusion's restatement of the take-home message are scholarly convention, NOT duplication. Everything else that has a home elsewhere goes.
5. Forward narrative. Removal never leaves a scar ("as discussed above, we omit..."); the text reads as if written short from the start.
6. Deletions are evidence-backed. A "dead" symbol, figure, table, or definition is deleted only after a whole-document grep proves nothing consumes it; the grep evidence goes in the report.

1. Measure — build the redundancy map

1. Read the target file completely. Resolve --field as in /sci-paper:de-ai §1 (shared style-profile convention).
2. Snapshot the length baseline before any edit: cp <file> <scratch>/length-baseline.tex (or record the clean git ref). Without an honest baseline the closing gate is meaningless.
3. Build the redundancy map, four sweeps:
   • (a) Cross-section duplicates. Grep every number/macro and scan each section pair for the same claim, definition, or figure description stated twice. Record: content, all locations, proposed canonical home.
   • (b) Zero-information-gain text. Paragraphs or sentences that add no claim, definition, evidence, qualification, or necessary link — assurance refrains, restated setup, roadmap prose the structure already carries.
   • (c) Dead artifacts. Defined-but-unused symbols, \labels never \ref'd, figures/tables never cited, definitions never consumed — each with the grep output that proves deadness.
   • (d) Verbose constructions. Multi-sentence spans whose content survives in fewer words (nominalization chains, redundant qualifiers, repeated glosses of the same symbol).
4. Rank the map by removable mass (largest duplication first).

2. Act — ranked removal

For each map entry, in rank order:

1. Duplicates (a): choose the canonical location; delete the copies, or where the distant reader genuinely needs the pointer, replace with a cross-reference (Section~\ref{...}); re-read both sites in context.
2. Zero-gain text (b): delete. If a fragment carries one load-bearing clause, fold that clause into the neighboring sentence — do not keep the paragraph for one clause.
3. Dead artifacts (c): delete the artifact and every orphaned mention; re-run the deadness grep afterward to confirm nothing dangles.
4. Verbose spans (d): rewrite shorter. Gate every shortened rewrite with python tools/rewrite_reward.py --reference <claim-record> --original <span> --candidates ... — a candidate that drops a protected invariant is ineligible regardless of its brevity; among eligible candidates the shorter wins.

Apply each change with a minimal Edit and re-read the affected region plus every cross-reference into it.

3. Verify — prove the shrink

1. Length gate: python tools/length_gate.py <file> --before <scratch>/length-baseline.tex. Required outcome: exit 0 with a zero-or-negative net delta for a pure condensation pass. Any length-growth:<section> finding must be dispositioned (condense back, or record the §5.3 justification) before the pass may close.
2. No orphans: rebuild/compile the document if it is LaTeX (0 errors, 0 undefined references, no newly-missing labels); grep every deleted label and symbol to confirm zero remaining consumers.
3. Fidelity: re-check the §6 invariants over every edited span against the snapshot; every number, citation, and claim in the final text traces to the same source as before.

4. Loop until dry

Repeat §1-§3 over the whole document until two consecutive full sweeps find nothing removable. A single clean sweep is not convergence; simple counters miss the tail that the first removal round exposes.

5. Report

# condense — Report
Target: <file> | Baseline: <snapshot/ref> | Sweeps: K (last 2 dry)
Net length delta: <words, per section and total — zero or negative>

## Removed duplicates
- <content> : canonical home <loc>; copies removed <locs>

## Deleted (zero-gain / dead artifacts)
- <item> : deadness evidence <grep summary>

## Compressed spans
- <loc>: <before-words> -> <after-words>; eligibility PASS

## Dispositions
- <any length-growth findings and their recorded justifications>
No number, citation, claim, or sole-support fact was deleted.

6. Anti-patterns

• Appending an explanation instead of deleting the redundancy (the §5.3 explanatory patch — the exact disease this skill exists to cure).
• Deleting a fact's only occurrence because it "felt repeated" — dedup requires proving the survivor exists.
• Gutting the abstract or conclusion under the dedup rule (genre carve-out).
• Trimming hedges, uncertainties, or qualifiers to save words — those are protected invariants, not padding.
• Declaring convergence after one clean sweep.
• Leaving removal scars or drafting-history narration in the text.
• Doing de-ai's job: structural-tell rewriting (voice, templates, L0 words) belongs to /sci-paper:de-ai; condense only deletes and compresses.

7. Interfaces

• docs/SCIPAPER_STANDARD.md — §5.3 (the policy this skill executes), §6 (fidelity invariants), §0 (consequence classes and dispositions).
• tools/length_gate.py — the mechanical closing gate (exit 0/1/2).
• tools/rewrite_reward.py — eligibility + condensation ranking for shortened rewrites.
• /sci-paper:paper-review — dimension I findings are this skill's input queue.
• /sci-paper:de-ai — the authorship-fingerprint surface (boundary in §0).