dev-module

A repeatable workflow for developing one application module, from orientation and planning through implementation and testing. It also records learning counts and, when enabled, sends progress updates to an office-style status display.

In plain words
What is it for?
Use it to start or resume feature development, read relevant decisions and dependencies, coordinate planning and execution, update agent status, and finish with tests.
Why use it?
It gives agents a consistent way to resume module work without loading raw workflow state or losing project memory. This makes the work easier to follow across phases.

Command for Cursor

Install

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.

agentmods
npx agentmods add commands/phuoctrung-ppt/ai-sdlc-workflow/dev-module
Clone the repo
git clone --depth 1 https://github.com/phuoctrung-ppt/ai-sdlc-workflow

Made for: Cursor.

Per session 26 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 704 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured yesterday against content hash 24e46cf6544a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

.cursor/commands/dev-module.md · 85 lines

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_APPROVED exists 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

  1. Append docs/retrospective.md
  2. Facts → docs/memory/* (1–5)
  3. docs/module-deps.md → module done
  4. 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}
  1. Dispatch @learning-agent:
    • If stdout fullPassRecommended: true → task text includes full pass
    • Else → lightweight scan
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}

Read the full file on GitHub · 85 lines

Changes

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.

  1. yesterday First seen · 85 lines · 26 tokens per session scan A 24e46cf6544a

Subscribe to this mod's changes

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.