Skip to content
cccskills
FreePublished on GitHub

terminology-normalizer

Normalize terminology across a draft (canonical terms + synonym policy) without changing citations or meaning.

The English summary is being prepared. Showing the original description.

Run score Not tested

See how to install

Files included (1)

  • SKILL.md4.2 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Terminology Normalizer

Triggers & routing

  • Trigger: terminology, glossary, consistent terms, 术语统一, 统一叫法, 术语表.
  • Use when: the draft has concept drift (same thing called 2–3 names) or global-review flags terminology inconsistency.

Purpose: make the draft read like one author wrote it by enforcing consistent naming (canonical terms + synonym policy), without changing citations or meaning.

Role cards (use explicitly)

Taxonomist (canonicalizer)

Mission: decide one canonical term per concept and a light synonym policy.

Do:

  • Prefer taxonomy node names (outline/taxonomy.yml) as canonical labels when available.
  • Define a short synonym policy only where readers expect it (use sparingly).
  • Keep headings and tables aligned with canonical terms.

Avoid:

  • Renaming proper nouns (paper titles, benchmark names, model names).
  • Over-normalizing away meaningful distinctions (e.g., collapsing two different mechanisms into one word).

Integrator (apply without drift)

Mission: apply replacements consistently without changing meaning or citations.

Do:

  • Keep replacements local and conservative; reread sentences that become ambiguous.
  • Preserve citation placement and subsection boundaries.

Avoid:

  • Introducing new claims while rewriting for terminology.
  • Moving citations across subsections.

Role prompt: Terminology Editor (one voice)

You are normalizing terminology in a technical survey draft.

Your job is to make the draft read like one author wrote it by enforcing consistent naming.

Constraints:
- do not add/remove citation keys
- do not move citations across ### subsections
- do not introduce new claims while renaming

Method:
- pick a canonical term per concept
- define allowed synonyms (optional, minimal)
- apply consistently across headings, prose, and tables

Inputs

  • output/DRAFT.md
  • Optional (read-only context):
    • outline/outline.yml (heading consistency)
    • outline/taxonomy.yml (canonical labels)

Outputs

  • output/DRAFT.md (in place)
  • Optional: output/GLOSSARY.md (short appendix/glossary table, if useful)

Workflow

Use the role cards above.

Steps:

  1. Build a glossary candidate list from the draft (10–30 key terms):
  • core objects (agent, tool, environment, protocol)
  • key components (planner/executor, memory, verifier)
  • evaluation terms (benchmark, metric, budget)
  1. Choose canonical names and a synonym policy:
  • one concept = one canonical term
  • define allowed synonyms only when readers expect them (and use them sparingly)
  • if outline/taxonomy.yml exists: prefer taxonomy node names as canonical labels (avoid inventing new names)
  • if outline/outline.yml exists: keep section headings aligned with the same canonical terms
  1. Apply replacements conservatively:
  • do not alter paper names, model names, benchmark names
  • keep terminology consistent across headings, prose, and table captions
  1. Optional: write a small output/GLOSSARY.md:
  • term | canonical | allowed synonyms | notes

Mini examples (what to do / what to avoid)

  • Bad (term drift): tool API, tool interface, action schema used interchangeably without a rule.

  • Better (canonical + light synonym policy): pick one canonical term (e.g., tool interface) and allow one synonym only when first introduced (e.g., tool interface (API contract)), then stick to canonical thereafter.

  • Bad (over-normalization): replacing distinct terms so a contrast disappears.

  • Better: keep distinct terms when they encode different mechanisms; normalize only spelling and naming consistency.

Guardrails (do not violate)

  • Do not add/remove citation keys.
  • Do not move citations across ### subsections.
  • Do not introduce new claims while renaming.

Troubleshooting

Issue: normalization changes citation keys or moves citations

Fix:

  • Revert; this skill must not add/remove keys or move citations across subsections.

Issue: synonyms policy is unclear

Fix:

  • Define one canonical term per concept and list allowed synonyms; apply consistently across headings, tables, and prose.

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

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`.

The English summary is being prepared. Showing the original description.

WILLOSCAR/research-units-pipeline-skills515Updated Oct 5, 2026

Download a small corpus of open-access arXiv survey/review PDFs about agentic systems and extract text for style learning.

The English summary is being prepared. Showing the original description.

WILLOSCAR/research-units-pipeline-skills515Updated Oct 5, 2026

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.

The English summary is being prepared. Showing the original description.

WILLOSCAR/research-units-pipeline-skills515Updated Oct 5, 2026

Curate reader-facing survey tables for the Appendix (clean layout + high information density), using only in-scope evidence and existing citation keys.

The English summary is being prepared. Showing the original description.

WILLOSCAR/research-units-pipeline-skills515Updated Oct 5, 2026

Build a final, machine-readable argument snapshot from current H3 sections and refresh section fingerprints before merge.

The English summary is being prepared. Showing the original description.

WILLOSCAR/research-units-pipeline-skills515Updated Oct 5, 2026

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.

The English summary is being prepared. Showing the original description.

WILLOSCAR/research-units-pipeline-skills515Updated Oct 5, 2026

See all skills by WILLOSCAR

Report a problem with this skill