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 Gerg/ai_agent_skills --skill agent-session-retrogit clone --depth 1 https://github.com/Gerg/ai_agent_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/gerg/ai_agent_skills/agent-session-retro)<a href="https://agentmods.dev/skills/gerg/ai_agent_skills/agent-session-retro"><img src="https://agentmods.dev/badge/skills/gerg/ai_agent_skills/agent-session-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/gerg/ai_agent_skills/agent-session-retro"><img src="https://agentmods.dev/badge/skills/gerg/ai_agent_skills/agent-session-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 | $0.00052 | $0.02055 |
| Opus 5 | $0.00026 | $0.01027 |
| Sonnet 5 | $0.00010 | $0.00411 |
| Haiku 4.5 | $0.00005 | $0.00205 |
Grade A, and why
agent-session-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 9d 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 — 286 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Session Retrospective
Conduct structured retrospectives at session end to continuously improve agent-user collaboration.
When using this skill, begin by stating: "I'm using the agent-session-retro skill to conduct an end-of-session retrospective and identify improvements."
Purpose
End-of-session retrospectives help identify:
- Skill gaps: Missing or incomplete skills that would have helped
- Skill improvements: Updates to existing skills based on learnings
- Process improvements: Changes to AGENTS.md, workflows, or collaboration patterns
- Tooling issues: Problems with tools or their integration
- Communication patterns: What worked well or poorly in agent-user interaction
When to Use
- At the end of significant work sessions
- After completing complex or novel tasks
- When user explicitly requests retrospective
- When you notice recurring inefficiencies or friction points
Retrospective Process
1. Review the Session
Critical: Make sure to review the contents of the session in its entirety. Do NOT rely on summarized context or assumptions!
What was accomplished?
- Main objectives and outcomes
- Tickets/tasks completed
- Artifacts created or updated
- Problems solved
What was the scope?
- How long did it take?
- How many context windows?
- What skills were used?
- What tools were involved?
2. Identify What Worked Well
Effective practices:
- Clear communication patterns
- Useful skills or tools
- Good decision points
- Efficient workflows
Examples to look for:
- User provided clear direction at key decision points
- Existing skills provided good guidance
- Tools worked smoothly together
- Process was efficient and low-friction
3. Identify Inefficiencies and Friction
Common categories:
Skill-related:
- Missing skills that would have helped
- Existing skills that were too verbose or too sparse
- Skills that conflicted or overlapped
- Skills that didn't match actual usage patterns
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.
- 9d ago First seen · 286 lines · 52 tokens per session scan A 933c25a2893f
agent-session-retro is a skill published in the GitHub repository Gerg/ai_agent_skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 52 tokens to every session and 2,055 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 2026-08-31.
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