feat(linkedin-studio): N7.5 — MR-F9 etterspørsels-sveip (Tier-5-kilder + smertepunkt + vokabular-oversettelse + arc-kontrakt) [skip-docs]

The «innenfra og ut» demand-sweep: the mechanism that FILLS the N6 reader fields
(readerQuestion/painPoint/saturation). Discovery finds "what happened"; this layer
translates it into "the problem the reader is stuck on".

- demand-spotter agent (agents 19->20, inherits session): three passes after
  discovery, before drafting — demand-sweep -> pain-point map -> vocabulary translation.
- Tier 5 demand sources in config/trends-sources.template.md (inverse of Tier 1-4;
  honest blind spots: YouTube API, Reddit approximate, HN/GitHub ground truth).
- demand signal on TrendRecord (strength + answered) — rankable twin of the verbatim
  saturation text; additive-optional, store schema stays v4 (no migration).
- arc.ts (§4 output contract): groupIntoArcs (relatedIds transitive closure),
  rankArcQuestions (etterspørsel x kan-svare x ikke-besvart), classifyMarketGap
  (supply-gap != demand-gap != saturated). Pure + deterministic (TDD).
- arcs CLI verb + /linkedin:trends --demand mode (commands stay 30); morning brief
  shows the per-candidate demand signal.

Suites: trends 276->300, test-runner 139->140, tsc clean. Others unchanged
(brain 134, hooks 140, tests 35, render 60).

MR-F9 built (bygget-men-ubevist) — the (a)/(b)/(c) evidence gate is a runtime
demonstration, proven consumer-side (plugin agents don't resolve in the dev repo).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014bE7VbkmR3cqHFEeGfzgwb
This commit is contained in:
Kjell Tore Guttormsen 2026-07-23 22:26:39 +02:00
commit 9de38c4939
16 changed files with 968 additions and 8 deletions

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| network-builder | Sonnet | Teal | Strategic networking and outreach |
| content-repurposer | Sonnet | Purple | Format conversion and evergreen refresh |
| trend-spotter | (inherits session) | White | Trending topics and opportunity scoring |
| demand-spotter | (inherits session) | Silver | Demand-sweep «innenfra og ut»: reader-side questions, pain-point map, vocabulary translation, §4 arc map |
| voice-trainer | Sonnet | Pink | Voice profile building and drift detection |
| differentiation-checker | Sonnet | Gray | Originality scoring and commodity detection |
| video-scripter | Sonnet | Violet | Video script creation with pacing and visual cues |