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docs(linkedin-studio): M0-14 — D3 path-convention + voice-readers prototype

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Kjell Tore Guttormsen 2026-06-18 12:38:17 +02:00
commit c0abb82d9c
21 changed files with 138 additions and 38 deletions
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2
agents/trend-spotter.md
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@ -41,7 +41,7 @@ Before scanning, load the user's content pillars and expertise areas:
- Extract: 5 core expertise areas, target audience, voice preferences
- If file does not exist, ask the user for their 5 content pillars before proceeding
2. **Read voice samples:** `${CLAUDE_PLUGIN_ROOT}/assets/voice-samples/` (glob for .md files)
2. **Read voice samples:** `${LINKEDIN_STUDIO_DATA:-$HOME/.claude/linkedin-studio}/voice-samples/` (glob for .md files)
- Understand their typical angle and tone
3. **Check recent posts:** `${CLAUDE_PLUGIN_ROOT}/assets/analytics/posts/` (if available)

4
agents/video-scripter.md
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@ -34,7 +34,7 @@ ${CLAUDE_PLUGIN_ROOT}/references/video-strategy-guide.md → Script
${CLAUDE_PLUGIN_ROOT}/references/linkedin-formats.md → Video specs, algorithm data, technical requirements
${CLAUDE_PLUGIN_ROOT}/references/engagement-frameworks.md → Hook types, CTAs, story structures
${CLAUDE_PLUGIN_ROOT}/references/thought-leadership-angles.md → 8 universal angles
${CLAUDE_PLUGIN_ROOT}/assets/voice-samples/ → User's authentic voice (ALWAYS read before scripting)
${LINKEDIN_STUDIO_DATA:-$HOME/.claude/linkedin-studio}/voice-samples/ → User's authentic voice (ALWAYS read before scripting)
${CLAUDE_PLUGIN_ROOT}/assets/examples/high-engagement-posts.md → Successful content patterns
~/.claude/linkedin-studio.local.md → User state, recent topics, streak
```
@ -150,7 +150,7 @@ When converting an existing text post to video:
After drafting the script:
1. Read `assets/voice-samples/` to match the user's natural speech patterns
1. Read `${LINKEDIN_STUDIO_DATA:-$HOME/.claude/linkedin-studio}/voice-samples/` to match the user's natural speech patterns
2. Check for:
- **Sentence length** — match their natural rhythm
- **Vocabulary level** — match their word choices

4
agents/voice-scrubber.md
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@ -52,7 +52,7 @@ This is the single most important rule of this agent.
- The gold standard for Norwegian chronicle voice is the **approved Norwegian
editions** (e.g. the series' approved Del 1 + Del 2). The caller passes the
path(s); read them as the corpus before scrubbing.
- **Do NOT calibrate against `assets/voice-samples/authentic-voice-samples.md`.**
- **Do NOT calibrate against `${LINKEDIN_STUDIO_DATA:-$HOME/.claude/linkedin-studio}/voice-samples/authentic-voice-samples.md`.**
That corpus is for **English short-form posts** and encodes rules that are
WRONG for Norwegian chronicle — e.g. it forbids the em-dash, which the author
*does* use in long-form Norwegian. Using it as the gold standard would actively
@ -111,7 +111,7 @@ overwrite identity-level voice.
After scrubbing, append what you learned to a drift log so the agent gets sharper
each edition:
- Write to `${CLAUDE_PLUGIN_ROOT}/assets/voice-samples/chronicle-voice-drift-log.md`
- Write to `${LINKEDIN_STUDIO_DATA:-$HOME/.claude/linkedin-studio}/voice-samples/chronicle-voice-drift-log.md`
(create if absent). One dated entry per run: which tells recurred, which voice
traits the draft drifted on, and any newly-confirmed gold-standard pattern.
- Do **not** rewrite the general voice profile (`config/user-profile.local.md`) —

4
agents/voice-trainer.md
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@ -133,7 +133,7 @@ Architecture: [prose/sectioned/framework]
### Analysis Process
1. **Gather** — Read all files in `${CLAUDE_PLUGIN_ROOT}/assets/voice-samples/`, existing profile from `config/user-profile.local.md`, and template from `config/user-profile.template.md`
1. **Gather** — Read all files in `${LINKEDIN_STUDIO_DATA:-$HOME/.claude/linkedin-studio}/voice-samples/`, existing profile from `config/user-profile.local.md`, and template from `config/user-profile.template.md`
2. **Analyze** — Apply all six dimensions to each sample. Note dates for temporal analysis. Flag inconsistent samples as outliers or evolution.
3. **Synthesize** — Patterns in 70%+ of samples = core traits. 40-70% = situational traits (note context). <40% = experimental traits. Track temporal trends.
4. **Build** — Compile into Voice Profile Document format. Include confidence levels (high/medium/low) and concrete examples for every trait.
@ -323,7 +323,7 @@ Fixes: [specific corrections with baseline examples]
## References
Read these files for context and methodology:
- `${CLAUDE_PLUGIN_ROOT}/assets/voice-samples/` — Source samples for analysis
- `${LINKEDIN_STUDIO_DATA:-$HOME/.claude/linkedin-studio}/voice-samples/` — Source samples for analysis
- `${CLAUDE_PLUGIN_ROOT}/config/user-profile.template.md` — Profile structure template
- `${CLAUDE_PLUGIN_ROOT}/config/user-profile.local.md` — Current voice profile (if exists)
- `${CLAUDE_PLUGIN_ROOT}/references/ai-content-framework.md` — AI content anti-patterns and quality checklist