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 WingedGuardian/GENesis-AGI --skill retrospectivegit clone --depth 1 https://github.com/WingedGuardian/GENesis-AGIWrote 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/wingedguardian/genesis-agi/retrospective)<a href="https://agentmods.dev/skills/wingedguardian/genesis-agi/retrospective"><img src="https://agentmods.dev/badge/skills/wingedguardian/genesis-agi/retrospective/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/wingedguardian/genesis-agi/retrospective"><img src="https://agentmods.dev/badge/skills/wingedguardian/genesis-agi/retrospective.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 | $0.00032 | $0.00372 |
| Opus 5.5 | $0.00013 | $0.00149 |
| Sonnet 5.5 | $0.00006 | $0.00074 |
| Haiku 4.5 | $0.00003 | $0.00037 |
Grade A, and why
retrospective 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 1mo 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.
What it actually says
Retrospective
Purpose
Analyze a completed interaction or task to extract lessons, identify process improvements, and update procedural memory.
When to Use
- After a multi-step task completes (success or failure).
- After a user interaction that revealed a gap or surprise.
- After an obstacle was resolved (capture the resolution pattern).
- Scheduled: end of each active work session.
Workflow
- Reconstruct timeline — What happened, in what order? What was the goal?
- Identify outcomes — Did it succeed? Partially? What was the quality?
- Extract surprises — What was unexpected? What assumptions broke?
- Find patterns — Does this match any existing procedural knowledge? Does it contradict any?
- Derive lessons — Concrete, actionable learnings (not vague platitudes).
- Update memory — Write observations, update procedures if warranted, flag contradictions for user review.
Output Format
subject: <what was analyzed>
date: <YYYY-MM-DD>
outcome: success | partial | failure
surprises:
- <unexpected finding>
lessons:
- <concrete actionable lesson>
procedure_updates:
- procedure: <name>
change: <what to update>
observations:
- <observation to store>
References
src/genesis/learning/procedural/— Procedure CRUD for updatessrc/genesis/learning/observation_writer.py— Writing observations
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.
- 1mo ago First seen · 55 lines · 32 tokens per session scan A 00fafc31c69d
retrospective is a skill published in the GitHub repository WingedGuardian/GENesis-AGI (99 stars, last pushed today), licensed MIT. It adds 32 tokens to every session and 372 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 2026-08-30.
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