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 product-on-purpose/thinking-framework-skills --skill think-after-action-reviewgit clone --depth 1 https://github.com/product-on-purpose/thinking-framework-skillsWrote 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/product-on-purpose/thinking-framework-skills/think-after-action-review)<a href="https://agentmods.dev/skills/product-on-purpose/thinking-framework-skills/think-after-action-review"><img src="https://agentmods.dev/badge/skills/product-on-purpose/thinking-framework-skills/think-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/product-on-purpose/thinking-framework-skills/think-after-action-review"><img src="https://agentmods.dev/badge/skills/product-on-purpose/thinking-framework-skills/think-after-action-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00066 | $0.00855 |
| Opus 5 | $0.00033 | $0.00428 |
| Sonnet 5 | $0.00013 | $0.00171 |
| Haiku 4.5 | $0.00007 | $0.00085 |
Grade A, and why
think-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 12d 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
After Action Review
Most retrospectives are an unstructured "how did it go?" that produces venting and vague lessons. The After Action Review imposes the structure that actually carries the benefit: compare what was expected to what actually happened, diagnose why the gaps occurred (in both directions), and convert that into what to sustain and what to change, specifically and with owners. The expected-vs-actual comparison is load-bearing; without a recorded expectation there is nothing to learn against, only hindsight narrative. The output is an after-action review, and it must be blameless to work.
When to Use
- A project, launch, sprint, experiment, or incident has finished.
- There was a real expectation to compare the outcome against (or you can reconstruct it honestly).
- The team wants to learn, not assign fault.
When NOT to Use
- Before the event (that is a premortem).
- As a status update or a summary of what shipped.
- When it will become blame (it stops working the moment people fear fault).
- When there is genuinely no expectation and none can be honestly reconstructed.
Instructions
When asked to run an after-action review, follow these steps:
- State what was expected. The goal, the plan, and the predicted outcome going in. If it was not recorded, reconstruct it honestly and say you are doing so - do not back-fit it to the result.
- State what actually happened. The real outcome, concretely, including what went better than expected, not only what went worse.
- Diagnose the gaps. For each meaningful difference (both directions), ask why - the actual cause, not the convenient one. Keep it blameless.
- Capture what to sustain. The things that worked and should be repeated. Do not skip this for the failures.
- Specify what to change. Concrete, owned changes for next time - not vague "communicate better."
- Emit the after-action review per
references/TEMPLATE.md.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 63 lines · 66 tokens per session scan A 9bb9d61cf84f
think-after-action-review is a skill published in the GitHub repository product-on-purpose/thinking-framework-skills (15 stars, last pushed today), licensed Apache-2.0. It adds 66 tokens to every session and 855 once invoked, about $0.0003 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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