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feat(linkedin-studio): short-form de-AI gate via differentiation-checker + voice-guardian

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Wire the orphan differentiation-checker (#10) into the five short-form
creation commands (post/quick/react/carousel/video) as a De-AI /
Differentiation Gate at each command's quality-check step: confirm the
LinkedIn-named substance signals (personal substance, original thinking,
concrete specifics, genuine voice) + a soft engagement-bait check, and
delegate an originality pass to linkedin-studio:differentiation-checker
when the angle risks commodity content. Add Task to allowed-tools in
quick/react/carousel (post/video already had it from Step 13).

Extend (not duplicate) hooks/prompts/voice-guardian.md's AI-pattern
section with the same named signals from research/01 D8 + research/03 D4.
Runtime-loaded prompt — no compile-hooks.py, no hooks.json change
(verified: compile-hooks --check reports no drift).

Test: new hooks/scripts/__tests__/linkedin-content-filter.test.mjs pins
the content/non-content boundary the gate is scoped by (14 tests).
Full hook suite 76/76, structure lint 61/61.

Plan Step 14 (Wave 4 S2). Counts unchanged (26 commands / 19 agents).
[skip-docs]: tre-doc + version bump deferred to Step 21 per remediation plan.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Kjell Tore Guttormsen 2026-05-30 02:31:41 +02:00
commit e2ed3eb0aa
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13
plugins/linkedin-studio/hooks/prompts/voice-guardian.md
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@ -13,6 +13,19 @@ Scan for these common AI writing patterns:
If 3+ AI patterns detected, flag: 'Voice Guardian Alert: This content scores below authenticity threshold. AI patterns found: [list specific patterns]. Suggested fixes: [specific rewrites using natural language].'
### LinkedIn-named substance signals (official de-AI down-rank)
LinkedIn confirmed (VP Laura Lorenzetti, 2026-05-19) an active program that **reach-suppresses** generic AI posts/comments and attention-bait — down to first-degree connections, not deletion — using ML trained on human-annotated "original thinking" vs "lacking substance." Beyond the generic tells above, check the draft for the four signals LinkedIn *named*. This is the differentiation surface, not an unverified SEO tell-list:
- **Personal substance** — a lived detail, stake, or first-hand observation only this author has. Generic advice anyone could have written is the failure mode.
- **Original thinking** — a take or synthesis, not a restatement of the consensus.
- **Concrete specifics** — named tools, real numbers, a dated example — not abstract nouns.
- **Genuine voice** — reads as the author, not a model-default cadence.
If two or more of these are missing, flag it alongside the AI-pattern alert: the post risks the low-substance down-rank, not merely sounding generic.
**Soft engagement-bait check:** block mechanical-response CTAs — "Comment YES", "Like for Part 2", "DM me 'X'", "Repost if you agree" — which trigger a post-level throttle. A *genuine* open question is not penalized; the line is a real answer vs a reflexive token.
## 2. Six-Dimension Voice Drift Scoring
Read the voice profile and collected post samples from `${CLAUDE_PLUGIN_ROOT}/assets/voice-samples/authentic-voice-samples.md`.