content-engine-review

name	content-engine-review
description	Monthly retrospective on the content engine. Reads last 30 days of calendar-log, memory, grade history, and pickup rates. Surfaces what worked, what didn't, proposed tuning. Saves report to reviews/YYYY-MM-review.md. Foundation B + C applied.
when_to_use	["/content-engine-review","monthly review","engine review","content retrospective","content retro","what worked last month","review my content engine"]

/content-engine-review — v0.1.0

Monthly retrospective on the cowork-social engine. Runs ~10 minutes. Surfaces what worked, what didn't, and proposes specific tuning before next month starts.

Live skill (not headless). Walks the user through findings + asks decisions on staged skill changes. ⚡ NEXT MOVE prints in chat, varies by verdict (3 states).

---

Inputs (lazy-load — Foundation A)

Read on every run:

File	Why
projects/social-media-content/calendar-log.md (last 30 days slice)	Posts shipped, platform breakdown, scheduled vs manual, status
projects/social-media-content/memory.md (last 30 days slice)	Self-improvement notes, recurring complaints, headless-run logs
projects/social-media-content/skill-improvements.md	Staged changes waiting on user review
projects/social-media-content/ideas/ (last 4 weekly idea files)	Generated vs picked vs shipped funnel
projects/social-media-content/brand-brief.md	Current brand state — is the wedge stale?
_aibos/state-social.md	blotato_status, selected_platforms, target cadence
_shared/foundations.md	Foundation B + C

If calendar-log.md has fewer than 5 rows in the last 30 days → surface:

"Less than 5 posts shipped in the last 30 days. The retrospective will be light. Continue, or block 2 hours for /weekly-content-session first?"

If user says "block 2 hours" → halt. If "continue" → proceed with whatever data exists.

---

The 6-step flow

Step 1 — Pull the data

Aggregate from each input file:

From calendar-log.md (last 30 days):

• Total posts shipped (count)
• Per-platform breakdown (count + % of total)
• Status breakdown: scheduled / published / manual / drafted / failed / skipped
• Day-of-week distribution
• If Blotato connected → success rate = published / (scheduled + published + failed)

From memory.md (last 30 days):

• All self-improvement notes per skill
• All headless-run logs from /generate-weekly-ideas
• All failure notes

From skill-improvements.md:

• All rows where reviewed: no — these are staged changes the user hasn't yet decided on

From ideas/<last 4 weekly files>:

• Ideas generated: total = 40 (4 weeks × 10)
• Ideas picked: cross-reference calendar-log.md against idea hooks (substring overlap >= 50%)
• Ideas drafted but not shipped: in outputs/ but not in calendar-log.md
• Ideas shipped: in calendar-log.md with status: scheduled or published or manual
• Compute the funnel: generated → picked → drafted → shipped

Step 2 — Compute patterns

Surface the patterns the user might miss:

Platform performance:

• Top platform this month (most posts)
• Lowest platform (least posts) — flag if it's a selected_platforms entry
• If grade history is in calendar-log.md (Step 3 of /weekly-content-session logs grades), compute avg grade per platform

Hook pattern wins:

• Which of the 11 hook patterns appeared most in outputs/social-media-content/ drafts this month
• Correlate hook pattern → grade score (if grades logged) — name the top 2 patterns by avg score

Cadence adherence:

• Compare actual posts/week vs target (from _aibos/state-social.md or scheduling-defaults.md)
• Compute adherence %

Self-improvement recurrence:

• For each skill, count how many self-improvement notes appeared in memory.md
• Flag any pattern recurring 3+ times that's NOT already in skill-improvements.md — auto-stage per Foundation B

Idea pickup funnel:

• Generated → Picked %: of 40 ideas, how many became picks?
• Picked → Drafted %: of picks, how many became drafts?
• Drafted → Shipped %: of drafts, how many shipped?
• The narrowest stage = the bottleneck

Brand-brief staleness:

• Check brand-brief.md last_updated field
• If older than 90 days OR no posts referenced contrarian_belief in their hook pattern → flag

Step 3 — Plan-then-approve the report write

Before writing the report file, show the user a summary table + ask approval:

Last 30 days at a glance:
─────────────────────────
Posts shipped:    <N> across <M> platforms
Top platform:     <X> (<N> posts, avg grade <X>)
Worst platform:   <Y> (<N> posts, avg grade <X>) — flag for tuning
Top hook pattern: <name> (<count> uses, avg grade <X>)
Cadence:          <actual>/week vs <target>/week target → <adherence %>
Idea funnel:      40 generated → <N> picked (<X%>) → <N> drafted → <N> shipped
Staged tuning:    <N> skill-improvements rows waiting on you

Approve writing the full report to reviews/<YYYY-MM>-review.md?
Reply 'go' to write, or 'skip' to keep this conversation-only.

Plan-then-approve is non-negotiable for the file write. If user says skip → don't write the file, but still apply Foundation B + C in chat.

Step 4 — Write the report

If approved, resolve path: projects/social-media-content/reviews/<YYYY-MM>-review.md.

Create reviews/ directory if missing.

Write:

---
type: cowork-social-monthly-review
plugin: cowork-social
plugin_version: 0.1.0
review_month: <YYYY-MM>
generated_date: <ISO>
posts_shipped: <N>
adherence_pct: <X>
source_skill: /content-engine-review
---

# Content Engine Review — <Month Year>

## Summary

- **Posts published:** <N>
- **Platform breakdown:** linkedin <N>, twitter <N>, instagram <N>, tiktok <N>, threads <N>, facebook <N>
- **Avg posts/week:** <X>
- **Target posts/week:** <X>
- **Adherence:** <X>%
- **Blotato success rate:** % (if connected) | not connected — manual posting can't be measured



  #  —  uses, avg grade /100
   —  posts, avg grade /100, 
 
  — , grade  — <1-line why it worked>
  — , grade  — <1-line why it worked>
  — , grade  — <1-line why it worked>



   posts, avg grade . 
   entries in  waiting review (see Staged section below).
   missed  scheduled slots. 
  . % of  never became . 
  Last updated . 





| skill | pattern | firstchange |
|---|---|---|---|---|
| / |  |  |  |  |



  — 
  — 
  — 

---

Generated by  on .

Step 5 — Walk staged improvements with user

For EACH row in skill-improvements.md where reviewed: no, ask the user:

"Apply this change to /<skill>? Pattern: '' recurred times. Suggested change: '<suggested_change>'. Reply Y / N / defer."

Branch:

• Y → mark row reviewed: yes — approved <ISO>. Open the target SKILL.md for editing (or surface the file path for the user to edit). Append the approval to memory.md.
• N → mark row reviewed: yes — declined <ISO> | reason: <user's one-liner>.
• defer → leave row as reviewed: no. It'll show up in next month's review.

If 5+ rows are pending, batch: ask the user "5+ staged. Want to walk all of them now (~5 min) or defer this and only walk the top 3 by recurrence count?"

Step 6 — Brand-brief refresh check

If brand-brief staleness flagged in Step 2, ask:

"Brand-brief was last updated — days ago. <If 3+ self-improvement notes mentioned voice mismatch:> Multiple voice-mismatch notes this month suggest the brief drifted. Run /brand-brief now to refresh? Y / N / defer."

If Y → hand off to /brand-brief (the user's next skill invocation). If N or defer → log in memory.md and move on.

---

Foundation B — Self-improvement close

See _shared/foundations.md → Foundation B. After delivering the report + the ⚡ NEXT MOVE block, ask the user:

"What would've made this review 10% better?"

Append the answer to projects/social-media-content/memory.md:

<YYYY-MM-DD> | /content-engine-review | <answer verbatim>

Recurrence patterns to watch for this skill specifically:

• "review took too long" → consider trimming Step 5 walkthrough
• "didn't find the bottleneck" → consider deeper funnel analysis in Step 2
• "recommendations were generic" → consider more business-brain.md context pull
• "missed grade trends" → consider better grade-history aggregation

If any pattern hits 3+ → flag in skill-improvements.md per Foundation B rules.

---

Foundation C — ⚡ NEXT MOVE block (verdict-aware, 3 variants)

See _shared/foundations.md → Foundation C. Pick the variant based on the month's verdict:

Verdict 1 — Shipping consistently (>= 10 posts/month) AND grade trend flat-or-up

⚡ NEXT MOVE: Schedule a 30-minute /brand-brief refresh this week.
   Why: You're shipping consistently — time to refresh the angle so the next 30 days don't recycle the same hooks.

Verdict 2 — Shipping is the bottleneck (< 5 posts/month)

⚡ NEXT MOVE: Block 2 hours Monday for /weekly-content-session.
   Why: Last month's shipping rate is below the engine's break-even — habit needs a calendar slot.

Verdict 3 — Grade trend dropping (avg grade lower than prior month by >= 5 points)

⚡ NEXT MOVE: Re-run /brand-brief on the recent_proof_story field this week.
   Why: Grade trend is dropping — voice may have drifted from your real proof, hooks are getting generic.

Picking rule (when multiple verdicts apply)

Priority order:

1. Verdict 2 wins if shipping < 5/month (no point fixing voice if nothing's going out).
2. Verdict 3 wins if grades dropped >= 5 points (voice drift kills the engine even at high volume).
3. Verdict 1 is the default when shipping + grades are both healthy.

Validation pattern (same as canonical): ⚡ NEXT MOVE: .+ .+ .+\n Why: .+

If the block doesn't match → regenerate before printing.

---

Hard rules

• Plan-then-approve before writing the report file. Step 3 must complete before Step 4 fires.
• Don't auto-apply skill changes. Step 5 walkthrough — always confirm each row.
• Reviews directory: projects/social-media-content/reviews/ — create if missing, never overwrite past months.
• 3 verdict-aware NEXT MOVE variants — never a generic catch-all.
• Apply 3 foundations every run — Foundation A (lazy-load) is automatic; B + C are explicit close.
• Reference _shared/foundations.md rather than duplicating foundation text.
• 3rd-4th grade reading level in user-facing prompts.
• Surface patterns the user might miss — that's the whole point of the retrospective. Don't flatter, don't hedge.

---

Voice

The retrospective's voice: direct, opinionated, willing to tell the user a platform isn't working. Not flattery, not hype.

When in doubt: imagine a senior strategist sitting across the table on the first of the month. They've read the data. They have an opinion. They give it in plain words.