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 HoangNguyen0403/agent-skills-standard --skill retro-learngit clone --depth 1 https://github.com/HoangNguyen0403/agent-skills-standardWrote 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/hoangnguyen0403/agent-skills-standard/retro-learn)<a href="https://agentmods.dev/skills/hoangnguyen0403/agent-skills-standard/retro-learn"><img src="https://agentmods.dev/badge/skills/hoangnguyen0403/agent-skills-standard/retro-learn/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/hoangnguyen0403/agent-skills-standard/retro-learn"><img src="https://agentmods.dev/badge/skills/hoangnguyen0403/agent-skills-standard/retro-learn.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 | $0.00018 | $0.00583 |
| Opus 5 | $0.00009 | $0.00292 |
| Sonnet 5 | $0.00004 | $0.00117 |
| Haiku 4.5 | $0.00002 | $0.00058 |
Grade A, and why
retro-learn 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Retro Learn Skill
[!IMPORTANT] Convert delivery findings into skill, eval, workflow, and documentation improvements.
Optional args: slug=, ticket=<id/url>, mode=interactive|autonomous|channel, channel=, auto_continue=true|false, profile=business|hybrid|technical.
Instructions
When the user asks to perform this workflow, execute the following steps:
Retro Learn Workflow
Goal: Turn defects, missed expectations, and delivery friction into durable standards improvements.
Steps
- Gather evidence:
- Review findings
- Bugs found during verification
- Security findings
- User corrections
- Failed or slow checks
- Token or context pain
session-reportartifacts
- Classify:
- Skill rule gap
- Eval coverage gap
- Workflow gap
- Documentation gap
- Tooling gap
- Specialist gap
- Environment-only issue
- Decide action:
- Existing skill should prevent it: update
SKILL.mdandevals/evals.json. - No skill covers it: propose a new skill.
- Workflow caused drift: update
.agents/workflows. - Specialist caused drift: add budget, fallback, or output-format rule.
- Tooling can catch it: add or update an audit script.
- Existing skill should prevent it: update
- Verify learning:
- Run changed skill validation.
- Run eval alignment.
- Record remaining follow-ups.
Runtime Contract
- Use after delivery findings, corrections, or friction need converting into durable standards improvements.
- Required inputs: review findings, verification results, or session-report artifacts to classify.
- Return BLOCKED only when no evidence exists to classify.
Handoff Payload
slug, root causes, skill/eval updates, follow-ups, next workflow.
Blocking Questions
- Ask max 3 at a time with a recommended default and 2-3 options.
Output Template
# Retro: [Name]
## Evidence
## Root Causes
| Finding | Category | Action |
| --- | --- | --- |
| [finding] | [category] | [action] |
## Skill Or Eval Updates
## Outcome Report
feature_status: implemented
requirement_trace: BRD-OBJ-* -> REQ-* -> AC-* -> SRS-* -> evidence
completed_evidence: []; missing_evidence: []; decision_needed: []; recommended_next_workflow: none
## Next Workflow
## Follow-Ups
## Cost Report
Call `get_session_cost(workflow="retro-learn")` before final handoff.
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 · 92 lines · 18 tokens per session scan A c2949aeb2a2b
retro-learn is a skill published in the GitHub repository HoangNguyen0403/agent-skills-standard (565 stars, last pushed 3d ago), licensed MIT. It adds 18 tokens to every session and 583 once invoked, about $0.0001 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 2026-09-03.
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