Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Ckokoski/AuthorAgent --skill after-action-reviewgit clone --depth 1 https://github.com/Ckokoski/AuthorAgentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ckokoski/authoragent/after-action-review)<a href="https://agentmods.dev/skills/ckokoski/authoragent/after-action-review"><img src="https://agentmods.dev/badge/skills/ckokoski/authoragent/after-action-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ckokoski/authoragent/after-action-review"><img src="https://agentmods.dev/badge/skills/ckokoski/authoragent/after-action-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00023 | $0.01574 |
| Opus 5 | $0.00012 | $0.00787 |
| Sonnet 5 | $0.00005 | $0.00315 |
| Haiku 4.5 | $0.00002 | $0.00157 |
Grade A, and why
after-action-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 207 lines · 23 tokens per session scan A 4535fa9d9cb1
after-action-review is a skill published in the GitHub repository Ckokoski/AuthorAgent (106 stars, last pushed 2mo ago), licensed MIT. It adds 23 tokens to every session and 1,574 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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