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/dev-modulegit clone --depth 1 https://github.com/phuoctrung-ppt/ai-sdlc-workflowWhat 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 | $0.00026 | $0.00704 |
| Opus 5 | $0.00013 | $0.00352 |
| Sonnet 5 | $0.00005 | $0.00141 |
| Haiku 4.5 | $0.00003 | $0.00070 |
Grade A, and why
dev-module 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 yesterday.
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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Module Development Loop
Act as Orchestrator for module: {feature_name}
If
PLAN_APPROVEDexists for this module → skip Phase 1–2.
Context hygiene
Do not open into context:
.cursor/state/workflow-state.json(use CLI below for learning counter only).cursor/state/module-*-loop.json.aisdlc/*
Resume from docs/plans/, docs/module-deps.md, docs/memory/*.
Learning counter (write-only side effect, stdout is one compact JSON line):
python3 .cursor/scripts/learning-counter.py get
python3 .cursor/scripts/learning-counter.py inc --module {feature_name}
Office UI (when .aisdlc/ exists):
python3 .cursor/scripts/office-event.py --agent <id> --status working|done|idle|waiting|error --task "…" --phase <phase> --workflow {feature_name}
Phase 0 — Orient
cat docs/memory/decisions.md docs/memory/gotchas.md docs/memory/shortcuts.md 2>/dev/null || true
cat docs/module-deps.md 2>/dev/null || true
python3 .cursor/scripts/office-event.py --agent architect-planner --status working --task "Orient {feature_name}" --phase restore --workflow {feature_name}
Phases 1–5
Same as before: brainstorm → plan → scaffold → execute → test → judge/fix.
Emit office-event.py on each phase enter/exit. Use context-builder.py per agent.
Hard-gate execute on docs/module-deps.md.
Phase 6 — Done + Memory + Learning counter
- Append
docs/retrospective.md - Facts →
docs/memory/*(1–5) docs/module-deps.md→ moduledone- Increment counter (do not edit state JSON by hand):
python3 .cursor/scripts/learning-counter.py inc --module {feature_name}
# stdout e.g. {"modulesSinceLastProposal": 3, "fullPassRecommended": false}
- Dispatch
@learning-agent:- If stdout
fullPassRecommended: true→ task text includesfull pass - Else → lightweight scan
- If stdout
python3 .cursor/scripts/office-event.py --agent learning-agent --status working --task "Skill scan after {feature_name}" --phase review --workflow {feature_name}
python3 .cursor/context/context-builder.py \
--phase review \
--task "post-module skill scan for {feature_name}" \
--agent learning-agent \
--keywords "retrospective,pattern,skill,learning,gotcha,shortcut" \
--budget 5000
python3 .cursor/scripts/office-event.py --agent learning-agent --status done --task "Learning finished" --phase review --workflow {feature_name}
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.
- yesterday First seen · 85 lines · 26 tokens per session scan A 24e46cf6544a
dev-module is a command published in the GitHub repository phuoctrung-ppt/ai-sdlc-workflow (2 stars, last pushed 16d ago), licensed MIT. It adds 26 tokens to every session and 704 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 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.