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/generate-modulegit 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/generate-module)<a href="https://agentmods.dev/commands/phuoctrung-ppt/ai-sdlc-workflow/generate-module"><img src="https://agentmods.dev/badge/commands/phuoctrung-ppt/ai-sdlc-workflow/generate-module.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 | $0.00032 | $0.00473 |
| Opus 5 | $0.00016 | $0.00236 |
| Sonnet 5 | $0.00006 | $0.00095 |
| Haiku 4.5 | $0.00003 | $0.00047 |
Grade A, and why
generate-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 3d 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
Act as Backend Worker. Scaffold a complete feature module for: {feature_name}
- Run context-builder first:
python3 .cursor/context/context-builder.py \ --phase implement-backend --task "scaffold {feature_name} module" --agent backend-worker \ --keywords "module,service,controller,scaffold" - Obey Context Packet tiers — read tier2 backend/framework skill SKILL.md
- Read
.memory/constraints.mdandAGENTS.md §3for backend app path
Module Location
Determined by AGENTS.md §3 — typically:
<backend_app_path>/src/modules/{feature_name}/
Files to Generate
Scaffold these files using your framework's conventions (names and structure from AGENTS.md §2):
| File | Purpose |
|---|---|
{feature_name}.module.* |
Module/router registration |
{feature_name}.controller.* |
Route handlers / endpoints |
{feature_name}.service.* |
Business logic |
dto/create-{feature_name}.* |
Create input schema/DTO |
dto/update-{feature_name}.* |
Update input schema/DTO |
entities/{feature_name}.* |
DB model / entity |
{feature_name}.service.spec.* |
Unit tests for service layer |
index.* |
Barrel export |
Requirements
- Input validation at boundary (framework's schema/pipe system from
AGENTS.md §2) - Shared type contracts in the shared package (see
AGENTS.md §3) - Auth guard on all routes — public routes explicitly marked (auth method from
AGENTS.md §2) - Structured logger — no
console.log - Unit tests with 70%+ service layer coverage
- Compliance items from
AGENTS.md §5applied (multi-tenancy, GDPR, RBAC, etc.) - API docs annotations if the project uses them (check
AGENTS.md §2)
Output complete file contents for each file.
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.
- 3d ago First seen · 50 lines · 32 tokens per session scan A e3a90f95b1db
generate-module is a command published in the GitHub repository phuoctrung-ppt/ai-sdlc-workflow (2 stars, last pushed 17d ago), licensed MIT. It adds 32 tokens to every session and 473 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.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
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.