| name | after-action-review |
| description | Structured post-goal reflection that extracts lessons, evaluates quality, and feeds the self-improvement loop |
| author | AuthorAgent |
| version | 1.0.0 |
| triggers | ["after action review","review goal","post mortem","what went well","what went wrong","retrospective","goal review","debrief"] |
| permissions | ["file:read","file:write"] |
After-Action Review — Core Skill
A structured reflection process that runs after every completed goal. Extracts concrete lessons, evaluates output quality, identifies what worked and what didn't, and feeds everything into the self-improvement loop.
When It Runs
- Automatically after any goal completes (all steps done)
- On request when the user says "review goal" or "what went well"
- Periodically as part of a weekly self-assessment (if autonomous mode is enabled)
The Review Process
Step 1: Gather Context
Collect all relevant data about the completed goal:
- Goal title, type, description
- Number of steps planned vs. actually executed
- Time taken per step and total
- AI providers used and their costs
- Which skills were triggered
- Any errors or retries that occurred
- User feedback received during execution
Step 2: Quality Assessment
Rate the overall output on 5 dimensions:
After-Action Review: "Plan my time travel novel"
═══════════════════════════════════════════════════
Quality Assessment:
┌─────────────────────────────────┬───────┐
│ Completeness │ 9/10 │
│ Did we accomplish the goal? │ │
├─────────────────────────────────┼───────┤
│ Quality │ 7/10 │
│ How good was the output? │ │
├─────────────────────────────────┼───────┤
│ Efficiency │ 6/10 │
│ Did we use resources well? │ │
├─────────────────────────────────┼───────┤
│ User Satisfaction │ ?/10 │
│ (Awaiting user rating) │ │
├─────────────────────────────────┼───────┤
│ Reusability │ 8/10 │
│ Can this approach work again? │ │
└─────────────────────────────────┴───────┘
Overall Score: 7.5/10
Step 3: What Went Well
Identify and document successes:
✅ WHAT WENT WELL
─────────────────
1. Dynamic AI planning produced a coherent 7-step plan
→ The AI planner correctly identified this as a "planning" goal
→ Steps were logically ordered (premise → characters → world → outline)
2. Gemini handled planning steps efficiently at zero cost
→ All 4 planning steps used free-tier Gemini
→ Quality was sufficient for brainstorming/outlining
3. Character profiles were detailed and interconnected
→ AI naturally created relationships between characters
→ Motivations tied directly to the central conflict
4. User accepted the outline without major revisions
→ Strong signal that the structure was sound
Step 4: What Needs Improvement
Identify failures, inefficiencies, and areas for growth:
⚠️ WHAT NEEDS IMPROVEMENT
──────────────────────────
1. World-building step was too generic
→ Setting description lacked sensory specificity
→ Lesson: Add "include 3+ sensory details per location" to world-building prompts
2. Step 5 (review) was redundant with step 4 (outline)
→ Could have been combined into a single step
→ Lesson: For planning goals, combine review into the outline step
3. Total execution time: 8 minutes for 7 steps
→ Steps 2 and 3 could have run in parallel
→ Lesson: Character and world-building don't depend on each other — parallelize
4. Cost: $0.00 (all Gemini free tier)
→ Good for planning, but creative writing would need a better model
→ Lesson: Use Gemini for planning, switch to Claude/DeepSeek for prose
Step 5: Extract Lessons
Convert observations into structured lessons for the improvement log:
[
{
"category": "worldbuild",
"lesson": "Always include 3+ sensory details (sight, sound, smell, touch, taste) per location description",
"confidence": 0.75,
"source": "after_action_review"
},
{
"category": "task_execution",
"lesson": "For planning goals, character profiles and world-building can run in parallel (no dependency)",
"confidence": 0.8,
"source": "after_action_review"
},
{
"category": "task_execution",
"lesson": "Combine 'review and refine' into the preceding step for planning goals to reduce redundancy",
"confidence": 0.7,
"source": "after_action_review"
},
{
"category": "task_execution",
"lesson": "Use Gemini free tier for planning/outlining tasks. Reserve Claude/DeepSeek for creative prose.",
"confidence": 0.85,
"source": "after_action_review"
}
]
Step 6: User Feedback Request
Ask the user for their assessment:
📋 Goal Complete: "Plan my time travel novel"
I've completed my self-review. Quick questions:
1. Overall, how would you rate the output? (1-10)
2. What specifically did you like most?
3. What would you change for next time?
(Or just say "looks good" and I'll note that as positive feedback!)
Review Storage
Reviews are saved to workspace/memory/reviews/:
workspace/memory/reviews/
├── 2026-02-24-plan-time-travel-novel.md
├── 2026-02-24-research-medieval-weapons.md
└── 2026-02-25-write-chapter-1.md
Each review file contains the full structured assessment in Markdown format, readable by both humans and the AI.
Aggregate Reviews
Over time, reviews accumulate into patterns:
review performance this week
Shows:
- Goals completed: 5
- Average quality score: 7.8/10
- Most common improvement area: "Prose specificity"
- Lessons extracted: 12 (8 high confidence)
- User satisfaction trend: Improving ↑
Integration with Self-Improvement
The After-Action Review feeds directly into the self-improvement loop:
- Lessons extracted here are written to
improvement-log.jsonl
- Next time a similar goal runs, those lessons are injected into context
- The review itself checks whether previous lessons were applied
- Creates a measurable improvement trajectory over time
Commands
after action review — Run a review on the most recently completed goalreview goal [id] — Review a specific goalreview performance — Aggregate performance metricswhat went well — Quick summary of recent successeswhat went wrong — Quick summary of recent failuresrate last goal [1-10] — Provide a user rating for the last goal
