
redundancy-pruner
by WILLOSCAR
Research pipelines as semantic execution units: each skill declares inputs/outputs, acceptance criteria, and guardrails. Evidence-first methodology prevents hollow writing through structured intermediate artifacts.
SKILL.md
name: redundancy-pruner description: | Remove repeated boilerplate across sections (methodology disclaimers, generic transitions, repeated summaries) while preserving citations and meaning. Trigger: redundancy, repetition, boilerplate removal, 去重复, 去套话, 合并重复段落. Use when: the draft feels rigid because the same paragraph shape and disclaimer repeats across many subsections. Skip if: you are still drafting major missing sections (finish drafting first). Network: none. Guardrail: do not add/remove citation keys; do not move citations across subsections; do not delete subsection-specific content.
Redundancy Pruner
Purpose: make the survey feel intentional by removing “looped template paragraphs” and consolidating global disclaimers, while keeping meaning and citations stable.
Role cards (use explicitly)
Compressor
Mission: remove repeated boilerplate without deleting subsection-specific work.
Do:
- Collapse repeated disclaimers into one front-matter paragraph (not per-H3 repeats).
- Delete repeated narration stems and empty glue sentences.
- Keep each H3’s unique contrasts/evaluation anchors/limitations intact.
Avoid:
- Cutting unique comparisons because they sound similar.
- Turning pruning into a rewrite (this skill is subtraction-first).
Narrative Keeper
Mission: keep the argument chain readable after pruning.
Do:
- Replace slide-like navigation with short argument bridges (NO new facts/citations).
- Ensure each H3 still has a thesis, contrasts, and at least one limitation.
Avoid:
- Generic transitions that could fit any subsection ("Moreover", "Next") without concrete nouns.
Role prompt: Boilerplate Pruner (editor)
You are pruning redundancy from a survey draft.
Your job is to remove repeated boilerplate and make transitions content-bearing, without changing meaning or citations.
Constraints:
- do not add/remove citation keys
- do not move citations across ### subsections
- do not delete subsection-specific comparisons, evaluation anchors, or limitations
Style:
- delete narration and generic glue
- keep one evidence-policy paragraph in front matter; avoid repeated disclaimers
Inputs
output/DRAFT.md- Optional (helps avoid accidental drift):
outline/outline.yml(subsection boundaries)output/citation_anchors.prepolish.jsonl(if you are enforcing anchoring)
Outputs
output/DRAFT.md(in-place edits)
Workflow
Use the role cards above.
Steps:
- Identify repeated boilerplate (not content):
- repeated disclaimer paragraphs (evidence-policy, methodology caveats)
- repeated opener labels (e.g.,
Key takeaway:spam) - repeated slide-like narration stems (e.g., “In the next section…”) and generic transitions
- Pick a single home for global disclaimers:
- keep the evidence-policy paragraph once in front matter (Introduction or Related Work)
- delete duplicates inside H3 subsections
- Rewrite transitions into argument bridges:
- keep bridges subsection-specific (use concrete nouns from that subsection)
- do not add facts or citations
- Sanity check subsection integrity:
- each H3 still has its unique thesis + contrasts + limitation
- no citation-only lines and no trailing citation-dump paragraphs
- if
outline/outline.ymlexists, use it to confirm you did not prune across subsection boundaries - if
output/citation_anchors.prepolish.jsonlexists, treat it as a regression anchor (no cross-subsection citation drift)
Guardrails (do not violate)
- Do not add/remove citation keys.
- Do not move citations across
###subsections. - Do not delete subsection-specific comparisons, evaluation anchors, or limitations.
Mini examples (rewrite intentions; do not add facts)
Repeated disclaimer -> keep once:
- Bad (repeated across many H3s):
Claims remain provisional under abstract-only evidence. - Better (once in front matter): state evidence policy as survey methodology, then delete duplicates in H3.
Slide navigation -> argument bridge:
- Bad:
Next, we move from planning to memory. - Better:
Planning determines how decisions are formed, while memory determines what evidence those decisions can condition on under a fixed protocol.
Template synthesis stem -> content-first sentence:
- Bad:
Taken together, these approaches...(repeated many times) - Better: state the specific pattern directly (e.g.,
Across reported protocols, X trades off Y against Z...).
Troubleshooting
Issue: pruning removes subsection-specific content
Fix:
- Restrict edits to obviously repeated boilerplate; keep anything that encodes a unique comparison/limitation for that subsection.
Issue: pruning changes citation placement
Fix:
- Undo; citations must remain in the same subsection and keys must not change.
Score
Total Score
Based on repository quality metrics
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Reviews
Reviews coming soon

