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/commands/newsletter.md

@@ -658,8 +658,8 @@ turning-points the spine already named.
 
 3. **Expand with the `content-repurposer` muscle.** Reuse
    `agents/content-repurposer.md` (its article→long-form conversion discipline)
-   for individual section expansions — invoke it via `Task` (`subagent_type:
-   linkedin-studio:content-repurposer`) when useful, *from this
+   for individual section expansions — invoke it via `Task`
+   (`subagent_type: linkedin-studio:content-repurposer`) when useful, *from this
    command layer* (foreground, principle 4). The command owns assembly and
    voice; the agent assists with conversion. The draft is voice-matched by
    THIS session, not self-certified for voice — voice-match remains an

plugins/linkedin-studio/commands/pipeline.md

@@ -68,8 +68,8 @@ Reference `${CLAUDE_PLUGIN_ROOT}/references/engagement-frameworks.md` for hooks
 Run the draft through optimization checks:
 
 **Algorithm signals** (from `references/algorithm-signals-reference.md`):
-- Save-worthy content (10x weight)
-- Comment-provoking (7-9x weight)
+- Save-worthy content (saves rank highest in the engagement order)
+- Comment-provoking content (a substantive 15+ word comment ≈ 2x a like)
 - Dwell time >30s (+25%)
 
 **Quality scorecard** (from `assets/checklists/quality-scorecard.md`):

plugins/linkedin-studio/commands/profile.md

@@ -28,7 +28,7 @@ Read `references/algorithm-signals-reference.md` for algorithm mechanics.
 
 ## The Profile/Topic Relevance Factors
 
-LinkedIn's 150B parameter foundation model evaluates five criteria:
+The 2026 relevance-ranking model evaluates five criteria (see `references/algorithm-signals-reference.md`):
 
 | Criteria | What It Checks | Impact if Missing |
 |----------|----------------|-------------------|

plugins/linkedin-studio/commands/setup.md

@@ -18,7 +18,7 @@ allowed-tools:
 
 # LinkedIn Plugin Setup & Personalization
 
-You are a setup assistant for the LinkedIn thought leadership plugin. Guide the user through populating their asset templates with real data to maximize content personalization.
+You are a setup assistant for the LinkedIn Studio plugin. Guide the user through populating their asset templates with real data to maximize content personalization.
 
 ## Step 0: Calculate Personalization Score