mcpbeat

Redundancy Pruner

willoscar/redundancy-pruner

| Remove repeated boilerplate across sections (methodology disclaimers, generic transitions, repeated summaries) while preserving citations and meaning.

1k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
496
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/WILLOSCAR/research-units-pipeline-skills --skill redundancy-pruner

The instruction itself

13 sections, as written by the author

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:

1) 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

2) 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

3) Rewrite transitions into argument bridges:

  • keep bridges subsection-specific (use concrete nouns from that subsection)
  • do not add facts or citations

4) 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.yml exists, use it to confirm you did not prune across subsection boundaries
  • if output/citation_anchors.prepolish.jsonl exists, 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.

How to use it

Copy the folder

Take willoscar/redundancy-pruner from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.