open/linkedin-studio
1
0
Fork 0
Code Issues Pull requests Releases Wiki Activity Actions
linkedin-studio/config/user-profile.template.md
Kjell Tore Guttormsen b89868e3b1 feat(linkedin-studio): research-engine config layer — sources + scoring modes + MCP profile (§5 slice 2a) [skip-docs] 
Declaration/config groundwork that slice 2b's trend-spotter upgrade reads.
Standalone (no agent wiring yet — that's 2b), mirroring slice 1's pattern;
[skip-docs] for the same reason slice 1 was — user-facing docs land when 2b
wires the engine live.

- references/trend-scoring-modes.md: methodology SSOT for two rubrics —
  kortform (feed post, timing 20%) + long-form (chronicle, depth 25% / timing 10%,
  per tema-research-motor-spec §4.2). Both sum to 100%. trend-spotter renders from
  this in 2b instead of inlining a matrix (S12-consistent).
- config/trends-sources.template.md: shipped generic source-list defaults →
  user override at ${LINKEDIN_STUDIO_DATA}/trends/sources.md (data-dir, survives
  reinstall; same template->data-dir pattern as user-profile).
- user-profile.template.md: new "Research Tooling" section — declared research MCPs
  (Tavily/Gemini/Perplexity/Other) + WebSearch/WebFetch floor. 2b routes MCP-first.
- setup.md Step 3f + onboarding.md Phase 2: ask "which research MCPs?" -> profile.
  Store only what the user declares; no hard-coded MCP names.
- test-runner.sh: EXPECT_REFS 26->27; generalized the M0 +1 delta-guard into a
  named-post-M0-additions guard (POSTM0_REFS) so a legit later ref doc passes while
  the anti-masking intent holds. Gate green 84/0/0.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBMKqPSVbvSZHtQ4heM1UY
2026-06-22 13:36:28 +02:00

4.7 KiB
Raw Blame History

User Profile Configuration

Copy this file to user-profile.local.md and customize for your needs.

cp config/user-profile.template.md config/user-profile.local.md

PERSONALIZATION SETTINGS

User Profile Context

Name: [Your Name] Current Role: [Your Role] (posting as private individual, not representing employer) Organization: [Not disclosed / Your Company] Industry/Domain: [Your Industry]

Important Disclaimer: All articles and posts are written as a private individual. Views expressed are personal and do not represent any employer.

Core Expertise Areas (5 topics):

  1. [Topic 1]
  2. [Topic 2]
  3. [Topic 3]
  4. [Topic 4]
  5. [Topic 5]

Target Audience:

  • Primary: [Who are you primarily writing for?]
  • Secondary: [Secondary audience]
  • Geographic focus: [Region/Country]

LinkedIn Goals (ranked by priority):

  1. Build authority & influence
  2. Attract speaking opportunities
  3. Network with peers/influencers
  4. Generate qualified leads
  5. Monetization (consulting/courses)
  6. Recruit talent

Voice & Style Profile

Tone Preferences (select what applies):

  • Professional & authoritative
  • Conversational & approachable
  • Storytelling-focused
  • Data-driven & analytical
  • Empathetic & supportive
  • Provocative & contrarian

Content Style Mix:

  • Story-based posts
  • Framework/how-to posts
  • Data/research posts
  • Opinion/commentary posts
  • Case study posts
  • Personal reflection posts

Signature Elements:

  • Key frameworks you've developed: [Your frameworks, or "None yet"]
  • Recurring themes/angles: [Your themes]
  • Phrases you commonly use: [Your phrases]
  • Topics to AVOID: [Topics you never discuss]

Writing Quirks & Preferences:

  • Preferred post length: [Short 150-500 / Medium 1,200-1,800 / Long 2,000+]
  • Emoji usage: [None / Minimal 1-2 / Moderate 3-5]
  • Question style CTAs: [Always / Sometimes / Never]
  • Use of personal anecdotes: [Always / Sometimes / Rarely]
  • Technical depth: [Beginner / Intermediate / Advanced / Adaptive]

Voice Profile Summary

[Your Name] writes with:

  1. [Quality 1]: [Description]
  2. [Quality 2]: [Description]
  3. [Quality 3]: [Description]
  4. [Quality 4]: [Description]
  5. [Quality 5]: [Description]

DO:

  • [What you always do in your writing]
  • [Another thing you do]

DON'T:

  • [What you never do]
  • [Another thing to avoid]

Universal anti-patterns (keep these — they hold for every author):

  • Modell-/navne-katalog. Do not reel off product names, model names, or benchmarks for completeness. Pick ONE concrete, verifiable (preferably local) case over a list — a name-dump is a jargon wall to a non-technical reader.
  • Fullstendighet over leser-handling. Serve what the primary reader can DO from their chair, not everything the author knows. Completeness is not a virtue.
  • Selvrefererende overhead-åpning. No meta-commentary about what the text will or will not do, no warm-ups. Start on the reader's problem.
  • «ikke bare X, men Y», reflex rule-of-three, tacked-on summaries, hedging.

Language: [English / Norwegian / Other]

Strategic Context

Current LinkedIn Status:

  • Follower count: [Your current count]
  • 90-day growth goal: [Your goal]
  • Posting frequency: [Daily / 3x week / 2x week]
  • Optimal posting times: [Your best times, or "To be determined"]

Research Tooling

Which research MCPs do you have available? The trend / research engine routes to a declared MCP first and falls back to the always-available floor when none is declared. Check what you actually have connected — leave the rest unchecked. (Names are examples; add whatever you have under "Other" — nothing here is hard-coded downstream.)

  • Tavily (tavily_search / tavily_research)
  • Gemini deep research (gemini_deep_research)
  • Perplexity
  • Other: [name the MCP + what it's good for]

Always-available floor (no MCP needed): WebSearch + WebFetch. These are used when no research MCP is declared above. (Note: WebSearch is US-biased — a research MCP gives better coverage for non-US / regional sources.)

Preferred order (optional): [e.g. "Tavily for niche/regional, Gemini for deep dives, WebSearch as fallback" — or leave blank to let the engine pick]

Asset Utilization Preferences

When creating content, Claude should:

  • Check /assets/examples/ for past post patterns
  • Reference frameworks from /assets/frameworks/
  • Pull case studies from /assets/case-studies/
  • Incorporate voice samples from /assets/voice-samples/
  • Use research/data from /assets/research/