From thinking-frameworks-skills
Ranks framings against a writer's voice profile using analogy-direction priority (biology > organizational > sports). Filters physics/military analogies as voice violations. Use after generating framings in the Intuition Builder pipeline.
How this skill is triggered — by the user, by Claude, or both
Slash command
/thinking-frameworks-skills:voice-fitness-checkThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
- [Analogy direction priority](#analogy-direction-priority)
Related skills: Called by the Intuition Builder in the pipeline (step 5). Reads shared-context/voice-profile.md and shared-context/voices/{section}.md if a target section is specified.
From voice-profile.md section 9:
Everything else is Tier 4 (acceptable but not a voice signature).
Physics, military, warfare, weapons, combat — these are voice violations. Flag strongly. Propose a replacement in Tier 1–3 direction if one is available.
For each of the 5 framings:
- [ ] Step 1: Classify the source domain
- [ ] Step 2: Assign a tier (1-4) based on the priority table
- [ ] Step 3: Flag any Tier 4 or voice-violating framings
- [ ] Step 4: Rank the 5 framings overall (tier + craft quality)
- [ ] Step 5: Recommend first choice
Look at the source. Is it:
"Neural network" itself counts as biology — but only if the framing actually uses biological relations (neurons firing, learning as development), not just the name.
Topic: Multi-agent AI system.
5 framings (from generate-analogy-set):
Ranking:
Recommendation: First choice is the parliament contrarian. Swap framing 5 for the biological alternative.
voices/{section}.md) with different priorities, apply those on top of global.npx claudepluginhub lyndonkl/claude --plugin thinking-frameworks-skillsMeasure and enforce a user's writing voice via stylometry (function-word vectors, lexical diversity, sentence-length burstiness, register, opener POS, punctuation rates). Accepts 5-20 writing samples, builds a local YAML fingerprint, and gates drafts against deterministic bands.
Verifies each analogy in a substacker draft carries mechanical weight (explains vs. decorates) and checks analogy-catalog.md for novelty and domain fit.
Extracts a voice fingerprint from strong passages to audit and repair voice departures in multi-author documents or when brand voice has drifted.