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refactor(linkedin-studio): wire or delete 11 orphan agents (case-by-case) — 9 here, 2 in Steps 14/16

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Resolves the orphan-agent audit finding by the locked default: wire all, no deletions, so the agent count stays 19. Per agent, added Task to the target command's allowed-tools and a coherent 'subagent_type: linkedin-studio:<name>' delegation at a real point in the command's flow (not a token grep-match).

Wired (agents 1-9 of 11): video-scripter -> video.md (Step 4); content-optimizer -> post.md (Step 7 refinement) + ab-test.md (2a.4 optimized challenger); analytics-interpreter -> report.md (Step 7, report mode) + analyze.md (Step 2, interpret mode); content-planner -> batch.md (Step 2) + pipeline.md (Step 1); trend-spotter -> batch.md (Step 1) + pipeline.md (Step 1); network-builder -> outreach.md (Step 3a); strategy-advisor -> strategy.md (Step 3); voice-trainer -> setup.md (Step 3a); post-feedback-monitor -> calendar.md (publish action, 48h monitor).

Deferred to their dedicated steps: #10 differentiation-checker -> Step 14 (short-form de-AI gate), #11 engagement-coach -> Step 16 (first-hour command). Namespaced subagent_type form requires a session reload before the wired agents are invokable.

Verify: each of the 9 has >=1 invocation in commands/; structural lint 61/61 (counts 19/26/25/6 intact); agent-fixtures 35/35; hook tests 62/62. Three-doc + version reconciliation deferred to Step 21 per the locked plan [skip-docs].

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Kjell Tore Guttormsen 2026-05-30 01:26:36 +02:00
commit 9f65daa288
11 changed files with 28 additions and 5 deletions
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3
plugins/linkedin-studio/commands/setup.md
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@ -13,6 +13,7 @@ allowed-tools:
- Glob
- Write
- AskUserQuestion
- Task
---
# LinkedIn Plugin Setup & Personalization
@ -82,6 +83,8 @@ Based on their answer, run the corresponding sub-workflow below.
**Goal:** Populate `assets/voice-samples/authentic-voice-samples.md` with real voice data.
**Delegate the analysis + profile construction to the `voice-trainer` agent** — invoke it via `Task` with `subagent_type: linkedin-studio:voice-trainer` (foreground, from this command layer). The agent performs the pattern detection and extraction (steps 2–3 below) and returns the structured voice profile; this command owns collecting the samples (step 1) and writing the profile back to disk (steps 4–6).
1. Ask the user to paste 3-5 of their best LinkedIn posts (or any professional writing samples)
2. Analyze the samples for:
- **Sentence structure:** Short/long, simple/complex, varied?