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 jellydn/my-ai-tools --skill retrogit clone --depth 1 https://github.com/jellydn/my-ai-toolsWrote 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/jellydn/my-ai-tools/retro)<a href="https://agentmods.dev/skills/jellydn/my-ai-tools/retro"><img src="https://agentmods.dev/badge/skills/jellydn/my-ai-tools/retro/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/jellydn/my-ai-tools/retro"><img src="https://agentmods.dev/badge/skills/jellydn/my-ai-tools/retro.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 | $0.00016 | $0.01426 |
| Opus 5.5 | $0.00006 | $0.00570 |
| Sonnet 5.5 | $0.00003 | $0.00285 |
| Haiku 4.5 | $0.00002 | $0.00143 |
Grade A, and why
retro 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Coding Session Retrospective
Inspect a real coding session and propose improvements to the agent's environment, not the product code. The goal is to make the next run easier to navigate and harder to get wrong.
This is human-in-the-loop. Present findings first; do not edit steering files, standards, hooks, CI, or skills until the user chooses which findings to apply.
When to Use
Use when:
- A coding session was unusually long, confusing, or error-prone.
- The user asks to review recent agent sessions.
- A bug or repeated mistake suggests a missing guardrail.
The default target is the current session. If the user names a count or date range, inspect that exact range. If session history is unavailable, say so and review the repository evidence only.
Sources
Read the real sources before judging:
- Session transcript and tool output, using session search when available.
AGENTS.md,CLAUDE.md,GLOSSARY.md,GLOSSARY-MAP.md, and repository-local steering files.CODING_STANDARDS.mdor equivalent review guidance.- Existing
package.json,pyproject.toml,Makefile, task runner, hooks, and CI workflows. - The final diff, test output, and any unresolved warnings.
Do not infer a recurring mistake from one suspicious line. Tie every finding to concrete evidence from the session or repository.
Review Lens
Rank findings by severity and expected leverage. For each, record:
Finding: what happened
Evidence: session message, command, file, or diff
Prevention: the smallest environment change that would stop it
Owner: repo check | agent skill | steering file | tool | documentation
Cost: maintenance and false-positive risk
Inspect these categories:
Skill learning loop
Treat a human correction as training data for the workflow, not as a one-off preference:
- Save the agent's output before editing it.
- Make and save the human-edited version against the same input.
- State what decision the edit reflects that the skill did not encode.
- Rewrite the rule as a decision procedure, including when it does not apply.
- Rerun the original input and compare the result with the edited version.
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 · 134 lines · 16 tokens per session scan A 4088f7aa151e
retro is a skill published in the GitHub repository jellydn/my-ai-tools (123 stars, last pushed today), licensed MIT. It adds 16 tokens to every session and 1,426 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-07.
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