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/phuoctrung-ppt/ai-sdlc-workflow/skill-updategit clone --depth 1 https://github.com/phuoctrung-ppt/ai-sdlc-workflowWrote 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/commands/phuoctrung-ppt/ai-sdlc-workflow/skill-update)<a href="https://agentmods.dev/commands/phuoctrung-ppt/ai-sdlc-workflow/skill-update"><img src="https://agentmods.dev/badge/commands/phuoctrung-ppt/ai-sdlc-workflow/skill-update.svg" alt="Measured on agentmods" 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 | $0.00035 | $0.00352 |
| Opus 5 | $0.00017 | $0.00176 |
| Sonnet 5 | $0.00007 | $0.00070 |
| Haiku 4.5 | $0.00003 | $0.00035 |
Grade A, and why
skill-update 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 4d 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
Skill Update (Learning Layer)
Act as Orchestrator. Dispatch the learning loop (does not implement product features).
Do not open .cursor/state/** or .aisdlc/*.json.
Steps
-
Ensure Memory layer files exist:
docs/retrospective.mddocs/memory/decisions.md,gotchas.md,shortcuts.md
-
Dispatch
@learning-agentwith an explicit full pass:
python3 .cursor/context/context-builder.py \
--phase review \
--task "skill update full pass from retrospective" \
--agent learning-agent \
--keywords "retrospective,pattern,skill,learning,gotcha,shortcut" \
--budget 5000
-
Agent must follow skill
skill-updater:- Identify patterns from retrospective (≥2 signals)
- Write proposal to
docs/reviews/YYYY-MM-DD-skill-update-proposal.mdor report no pattern - Update memory files when facts are durable
- Do not apply SKILL.md patches until you approve
-
Present proposal to human/orchestrator:
- Approve → agent applies patch, marks APPLIED
- Reject → leave PENDING or close with reason
Notes
- This command is the explicit full Learning entrypoint.
/dev-modulePhase 6 only does lightweight distillation + a short learning-agent scan.
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.
- 4d ago First seen · 43 lines · 35 tokens per session scan A 7896bb447704
skill-update is a command published in the GitHub repository phuoctrung-ppt/ai-sdlc-workflow (2 stars, last pushed 18d ago), licensed MIT. It adds 35 tokens to every session and 352 once invoked, about $0.0002 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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