lyndonkl/skills/platform-voice-check
skills/platform-voice-check/SKILL.md
--- name: platform-voice-check description: Runs a voice-fidelity audit on each of the four substacker platform outputs (Substack Note, X thread, LinkedIn post, cross-post blurb) before reporting Distribution Translator completion. Checks for voice-don'ts (banned vocabulary, emoji, generic openers, marketing math without source), voice-do compliance (paper attribution preserved, hedges preserved, em-dash reframes present), platform-specific tonal shifts. Emits voice-check.md with pass/fail per a
$ install --global
skillsauthnpx skillsauth add lyndonkl/claude skills/platform-voice-checkInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Last scanned: Apr 25, 2026, 10:18 AM169.8s1 file scanned
SKILL.md
- name:
- platform-voice-check
- description:
- Runs a voice-fidelity audit on each of the four substacker platform outputs (Substack Note, X thread, LinkedIn post, cross-post blurb) before reporting Distribution Translator completion. Checks for voice-don'ts (banned vocabulary, emoji, generic openers, marketing math without source), voice-do compliance (paper attribution preserved, hedges preserved, em-dash reframes present), platform-specific tonal shifts. Emits voice-check.md with pass/fail per artifact. Trigger keywords: platform voice check, voice-check, gate, distribution voice, slop-leak check.
Platform Voice Check
Workflow
Voice-audit the 4 platform outputs:
- [ ] Step 1: Read all 4 output files + voice-profile.md + voices/{section}.md (if applicable)
- [ ] Step 2: For each file:
- Scan voice-don'ts (delve, unpack, paradigm shift, emoji, exclamations, I think, AI-is-transforming, custom CTA)
- Scan voice-dos (opener classification, em-dash reframes, hedge preservation, paper attribution)
- Platform-specific tonal shift check
- [ ] Step 3: Emit voice-check.md with pass/fail per file
- [ ] Step 4: If FAIL on any file, return the flag list to Distribution Translator for loop-back
Platform-specific tonal checks
| Platform | Expected tone shift | Flag if | |---|---|---| | Substack Note | Closest to essay voice | Any over-polishing that reads AI-rewritten | | X | More declarative, more quotable | Over-hedging ("Substack leak on X") | | LinkedIn | Practitioner, slightly less confessional | Raw confession ("Substack leak on LinkedIn") | | Cross-post | Third person | Any first-person use ("I argue…") |
Output format
ops/distribution/{date}-{slug}/voice-check.md:
---
agent: distribution-translator
date: YYYY-MM-DD
post_slug: {slug}
results:
substack-note.md: PASS | FAIL
x-thread.md: PASS | FAIL
linkedin-post.md: PASS | FAIL
cross-post-blurb.md: PASS | FAIL
---
## substack-note.md: PASS | FAIL
- (line / location) — issue — voice-profile citation
## x-thread.md: PASS | FAIL
- variant: short — line X — issue
- variant: medium — line Y — issue
## linkedin-post.md: PASS | FAIL
- ...
## cross-post-blurb.md: PASS | FAIL
- ...
Loop-back
If voice-check reports FAIL on any file, Distribution Translator MUST loop back to the matching rewrite skill with the flags as input. Max 2 loops per artifact. After 2 loops:
DESIGN-NOTE: This essay's voice may not translate cleanly to {platform}. User review recommended.
Ship best-so-far; flag for writer.
Guardrails
- Every flag must cite a voice-profile line. No flag without citation.
- Advisory on first pass; blocking after 2 loops (then ship best-so-far with a design-note).
- Do not write to the platform files directly. Emit the flag list; the rewrite skill re-runs.
- Check banned-vocabulary regex first (fast); tonal-shift checks second (slower).
- Paper attribution rule: if a paper is cited in the essay, check every platform file mentions Author, Institution, Year on first mention.
- Emoji check: zero tolerance across all four files.
Quick reference
- Runs as the final gate in the Distribution Translator pipeline.
- Advisory → loop-back → ship-best-after-2-loops.
- Output: one voice-check.md in the post's distribution folder.
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