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 agentmods add commands/neurofoo/agent-skills/aargit clone --depth 1 https://github.com/neurofoo/agent-skillsWhat 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 | $0.00000 | $0.00659 |
| Opus 5 | $0.00000 | $0.00329 |
| Sonnet 5 | $0.00000 | $0.00132 |
| Haiku 4.5 | $0.00000 | $0.00066 |
Grade A, and why
aar 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 3d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- review — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
After-Action Review
Conduct a structured debrief to extract learning from any significant event or experience.
Instructions
Work through the four core questions honestly and specifically. Focus on events and systems, not blaming individuals.
Output Format
Event: What are we reviewing? Date: When did it happen? Participants: Who was involved?
1. What Was Expected?
Before the event, what did we think would happen?
Goals/Objectives
- [What were we trying to achieve?]
Plan
- [What was the plan to achieve it?]
Success Criteria
- [How would we know if we succeeded?]
Assumptions
- [What did we assume would be true?]
2. What Actually Happened?
Facts only—what occurred, not why
Timeline
| Time | Event |
|---|---|
| [When] | [What happened] |
| [When] | [What happened] |
| [When] | [What happened] |
Outcomes
- [What results did we get?]
Compared to Expected
| Expected | Actual | Gap |
|---|---|---|
| [expectation] | [reality] | [+/-] |
3. Why the Difference?
Analysis of the gap between expected and actual
What Went Well (sustain these)
| Success | Contributing Factors |
|---|---|
| [what worked] | [why it worked] |
What Didn't Go Well (improve these)
| Problem | Root Cause |
|---|---|
| [what failed] | [why it failed] |
Surprises
- [Things we didn't anticipate]
Assumptions Tested
| Assumption | Validated? | Learning |
|---|---|---|
| [assumption] | Yes/No/Partial | [what we learned] |
4. What Do We Do Next?
Specific actions to sustain or improve
Sustain (keep doing these)
| Action | Owner | How to Protect It |
|---|---|---|
| [what to continue] | [who] | [mechanism] |
Improve (change these)
| Action | Owner | By When |
|---|---|---|
| [what to change] | [who] | [deadline] |
Stop (eliminate these)
| Action | Owner | Replacement |
|---|---|---|
| [what to stop] | [who] | [alternative] |
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.
- 3d ago First seen · 120 lines · 0 tokens per session scan A 62147e867e8b
aar is a command published in the GitHub repository neurofoo/agent-skills (111 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 659 tokens. 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.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.