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

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>

plugins/linkedin-studio/references/engagement-frameworks.md

@@ -335,16 +335,15 @@ The lesson was expensive but clear. [MEDIUM - transition]"
 
 ### Engagement Quality Hierarchy
 
-Not all engagement is equal. LinkedIn's algorithm weights different interactions based on their signal value:
+Not all engagement is equal. The defensible spine is the **order**, not a fixed multiplier — LinkedIn publishes no coefficient table, so trust the order and test the number:
 
-1. **Saves** (Highest signal - indicates content worth returning to)
-2. **Shares** (High signal - amplification and endorsement)
-3. **Comments 15+ words** (2.5x more valuable than short comments)
-4. **Expert comments** (7-9x multiplier - comments from verified experts in your field)
-5. **Comments <15 words** (Moderate signal)
-6. **Reactions** (Lowest signal - minimal effort)
+1. **Saves** (top signal — content worth returning to; a save ≈ 5x a like in single-vendor data)
+2. **Shares** (high signal — amplification and endorsement)
+3. **Comments 15+ words** (substantive comments outweigh short ones; a quality comment ≈ 2x a like)
+4. **Comments <15 words** (moderate signal)
+5. **Reactions** (baseline engagement unit)
 
-**Key insight:** One save or expert comment is worth significantly more than dozens of reactions. Focus on creating content that people want to save and share, and cultivate engagement from recognized experts in your domain.
+**Key insight:** One save or substantive comment is worth more than many reactions. Focus on content people want to save and share, and cultivate genuine substantive comments. See `references/algorithm-signals-reference.md` (cite, don't restate magnitudes).
 
 ### First Hour Critical
 - Aim for 15+ engagements in first 60 minutes

plugins/linkedin-studio/references/glossary.md

@@ -98,7 +98,7 @@ Coordinated group of accounts that artificially boost each other's posts. Active
 **Used in:** `references/linkedin-growth-playbook-2025-2026.md`, `commands/outreach.md` (collab absorbed in v2.0.0), `agents/network-builder.md`
 
 ### Engagement Quality Hierarchy
-Weighted valuation system for different engagement types: Saves (10x) > Shares (8x) > Expert Comments (7-9x) > 15+ word comments (2.5x) > Short comments (1x) > Reactions (0.2x). Quality over quantity.
+The defensible **ordering** of engagement signals — **saves > shares > quality comments (15+ words) > reactions/likes** — not a fixed coefficient table (LinkedIn publishes no such weights). Directional single-vendor estimates: a save ≈ 5x a like (≈ 2x a comment); a quality comment ≈ 2x a like. Trust the order, test the number — these are not hard multipliers to optimize against.
 
 **Used in:** `references/algorithm-signals-reference.md`, `references/engagement-frameworks.md`
 
@@ -213,7 +213,7 @@ Unexpected statement or data point that breaks normal thought patterns and captu
 ## S
 
 ### Save Signal
-Highest-value algorithmic signal (10x weight). Saves indicate content worth returning to; posts with saves get 130% higher follow probability. Only ~3% of posts reach save-worthy status.
+Highest-value engagement signal — top of the engagement order. A save ≈ 5x a like (≈ 2x a comment) in single-vendor data — directional, not a fixed weight. Saves indicate content worth returning to; posts with saves get 130% higher follow probability. Only ~3% of posts reach save-worthy status.
 
 **Used in:** `references/algorithm-signals-reference.md`, `references/linkedin-growth-playbook-2025-2026.md`
 

plugins/linkedin-studio/references/linkedin-growth-playbook-2025-2026.md

@@ -203,7 +203,7 @@ LinkedIn removed hashtag following, hashtag pages, and "Talks About" sections in
 
 **LinkedIn's data:**
 - 1.4x more engagement than other formats
-- Videos inspire 5x more conversations than text
+- #2 format but **declining** in reach; quality of engagement is debated — add captions, most watch muted (see `references/algorithm-signals-reference.md`)
 
 **Successful creator perspective (Lara Acosta, #1 UK female creator):**
 - "Video is overrated for growth on LinkedIn"
@@ -1002,7 +1002,7 @@ Within 20 months: 240,000 followers across LinkedIn, TikTok, Instagram, YouTube.
 - Jasmin Alić mantra: "Share everything you know"
 
 **4. Engagement quality trumps impression quantity**
-- Comments generate 15x more algorithmic boost than likes
+- Substantive comments rank above likes in the engagement order (a quality comment ≈ 2x a like in single-vendor data — directional, not a fixed multiplier; see `references/algorithm-signals-reference.md`)
 - First-hour response rates directly impact distribution
 
 **5. Data-driven iteration**