refactor(linkedin-studio): S30 magnitude-scrub (discrete-% class) — unsourced reach/engagement penalties -> SSOT

Hardening-class, NOT re-hardening: surgical SSOT-reconciliation of discrete percentage
penalties/declines stated as fact with no primary source in the SSOT
(references/algorithm-signals-reference.md). Same tool-grounded discipline as S27/S28
(read-and-show -> grep-confirm -> re-grep final). Re-grep surfaced drift + same-class siblings
beyond the plan's stored list; all surfaced and operator-approved before edit.

Scope: the discrete-% reach/engagement-penalty class only. The unsourced "Nx" reach/format
MULTIPLIER class (~50 instances across ~15 files) is a separate, larger pass -> deferred to S31
(operator: run everything, across multiple sessions).

HARDEN (20 edits, 7 files):
- linkedin-formats.md (5): :6 47-50% decline + :7 15%->31% feed-share -> directional; :176
  AI-comment 30%/55% -> ~45% less engagement (correlational, medium); :231/:279 hashtags -68%
  -> diminishing returns, no discrete figure.
- linkedin-growth-playbook (6): :158 47-50% decline (twin of formats:6) + :166 hashtags -68%
  + :224/:225 post-length 25%/32% + :435/:828 posting-frequency 25% -> directional, no
  discrete figure. (:221 1.17x multiplier folded in per operator approval; the rest of the
  multiplier class -> S31.)
- glossary.md (2): :91 engagement-bait -30-50% + :235 topic-gap -15-25% -> "correlate with
  lower reach, no discrete figure".
- engagement-coach.md (2): :195 55% + :455 -30%/-55% AI-comment -> ~45% less engagement
  (correlational), actively suppressed.
- post-feedback-monitor.md (1): :330 -25%/post -> "tends to split your own audience".
- ab-testing-framework.md (1): :66 hashtags -68% -> no discrete figure.
- poll-strategy-guide.md (2): :20 / :205 poll-overuse penalty -> declining effectiveness
  (directional).

KEPT INTACT (operator-locked / different class): engagement-pod + AI-slop suppression framing
(SSOT high-confidence); firsthour:112 (no number); poll:206 / poll:3 (qualitative); growth:567
conversion-rate; formats:268 list item.

VERIFY: discrete-% penalty/decline class re-grep across the 7 files -> NONE; leave-items intact;
bash scripts/test-runner.sh -> Passed 81 / Failed 0 / Warnings 0, exit 0; counts 29/19/26/6 +
v0.5.0 unchanged (.md prose only). Disposition: FIXED (20 edits, 7 files), one atomic commit,
local (push held).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016qgzo6rxthw7KuxHjn5vyE

references/glossary.md

@@ -88,7 +88,7 @@ Duration a user spends viewing content with ≥50% visible on screen. Posts with
 ## E
 
 ### Engagement Bait
-Prohibited engagement tactics ("Comment YES if...", "Tag someone who...", "Type 1 for...") that trigger -30-50% reach penalty. The algorithm actively detects and penalizes these patterns.
+Prohibited engagement tactics ("Comment YES if...", "Tag someone who...", "Type 1 for...") that correlate with lower reach — actively detected and suppressed (directional; no primary source for a discrete figure).
 
 **Used in:** `references/algorithm-signals-reference.md`
 
@@ -232,7 +232,7 @@ Three-question quality gate before publishing: (1) Does this help someone make a
 **Used in:** `references/content-angles.md`, `agents/differentiation-checker.md`, `agents/trend-spotter.md`
 
 ### Topical Consistency
-Posting about consistent topics within demonstrated expertise areas. The algorithm learns your domain expertise over 30+ days. Gaps >5 days trigger -15-25% reach penalty on return.
+Posting about consistent topics within demonstrated expertise areas. The algorithm learns your domain expertise over 30+ days. Consistency is a ranking input; gaps correlate with lower reach on return (directional; no primary source for a discrete figure).
 
 **Used in:** `references/linkedin-growth-playbook-2025-2026.md`, `references/algorithm-signals-reference.md`