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fix(linkedin-studio): S9 — full algorithm-magnitude sweep + lint rebuilt to the criterion

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Closes the S8 re-review (BLOCK 3/4/1). The S8 fix patched only the 2 strings S7 named; the re-review found 6 more same-class survivors. Per the systemic read, this is a comprehensive sweep, not a per-line patch.

Reconciled every retired engagement-coefficient + model-fact survivor against the canonical references/algorithm-signals-reference.md (order, not coefficients; comment ≈ 2x a like; no model name/params):
- glossary.md: coefficient table + Save-Signal '10x weight' → canonical ordering (citation now true)
- engagement-frameworks.md, analytics-interpreter.md, content-optimizer.md, pipeline.md, engagement-coach.md: the 10x/8x/7-9x/2.5x/0.2x system (incl. 4 survivors the re-review did not cite) → ordering
- playbook: '15x more algorithmic boost' + video '5x more conversations' → directional, sourced
- profile.md + linkedin-voice/SKILL.md: '150B parameter foundation model' → '2026 relevance-ranking model'
- quality-scorecard.md: '360Brew Validation' → topic-relevance framing
- setup.md: 'thought leadership plugin' → 'LinkedIn Studio plugin'

Lint (MAJOR 4): rebuilt scripts/test-runner.sh STALE_STATS to forbid EVERY retired-class phrasing (not the 2 S7 strings) + widened scope to assets/checklists/. Targets retired phrasings (7-9x, (10x), '10x weight', '5x more conversations'), NOT bare 10x/15x/5x (legit 5x5x5 / cadence / pixel-dims / '10x your reach' hyperbole). Proven non-vacuous: catches all 10 retired strings, ignores all 10 legit uses.

Tests (MAJOR 7): added no-anchor fall-through tests for recordFirstHourPlan + recordOutreachContact (date scalar not written/reported, section still appended). MINOR 8: reflowed newsletter.md content-repurposer wiring onto one line.

test-runner.sh 66/0/0; node --test 94/94 (was 92, +2). NO push until /trekreview re-clears the gate.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Kjell Tore Guttormsen 2026-05-30 09:56:49 +02:00
commit 0c9c02a2b1
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plugins/linkedin-studio/agents/content-optimizer.md
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@ -70,10 +70,10 @@ When you receive content to optimize, analyze it through these lenses:
### 3. Algorithm Signal Analysis
**Positive signals to maximize:**
- Content that encourages saves (10x weight)
- Content that prompts expert comments (7-9x weight)
- Content that drives 15+ word comments (2.5x weight)
**Positive signals to maximize** (order, not coefficients — see `references/algorithm-signals-reference.md`):
- Content that earns **saves** — top of the engagement order (a save ≈ 5x a like, directional)
- Content that earns **shares** — strong distribution / endorsement signal
- Content that earns **substantive 15+ word comments** — a quality comment ≈ 2x a like; substance over volume
- Dwell time optimization (>30s = +25%)
**Penalties to avoid:**