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/plan-featuregit 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/plan-feature)<a href="https://agentmods.dev/commands/phuoctrung-ppt/ai-sdlc-workflow/plan-feature"><img src="https://agentmods.dev/badge/commands/phuoctrung-ppt/ai-sdlc-workflow/plan-feature.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 | $0.00024 | $0.00459 |
| Opus 5 | $0.00012 | $0.00230 |
| Sonnet 5 | $0.00005 | $0.00092 |
| Haiku 4.5 | $0.00002 | $0.00046 |
Grade A, and why
plan-feature 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 Architect Planner (.cursor/agents/architect-planner.md).
Feature: {feature_description}
- Run context-builder:
python3 .cursor/context/context-builder.py --phase plan --task "{feature_description}" --agent architect-planner --handoff docs/plans/.active-plan - Phase 0 BRAINSTORM (HARD-GATE): explore codebase, propose 2–3 options, wait for approval — do not write the plan file yet
- Read
.memory/architecture.mdandAGENTS.md §2–§3 for stack and structure - Explore codebase for related modules
- Write plan to
docs/plans/YYYY-MM-DD-{slug}.mdusing the architect-planner template (include Domain Config Sync section) - Include:
- Acceptance criteria
- Database migration needs
- Shared type/contract definitions (schema tool from
AGENTS.md §2) - File list with owner agents
- Task table with agent assignments and dependencies
- Security / compliance notes from
AGENTS.md §6 - Risks and scope boundaries
- Phase 1.5 SYNC DOMAIN CONFIG (before workers):
- ADR →
docs/adr/NNNN-short-title.mdwhen architecture/stack/pattern decisions changed - Create/update living overview →
docs/architecture.md - Update
AGENTS.mdsections that changed (§2 stack, §3 structure, §4 tenancy, §5 roster/scopes, §6+ rules) - Update
.cursor/config/worker-scopes.json/protected-paths.jsonif scopes or protected areas changed (orchestrator approval for scope expansion) - Point
docs/plans/.active-planat this plan - Record N/A + reason in the plan for any skipped sync item
- ADR →
- Wait for orchestrator approval of plan + sync diffs before dispatching any workers (use
@scaffold-agentfirst if new module shells are required)
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 · 31 lines · 24 tokens per session scan A 71252d51d5ba
plan-feature is a command published in the GitHub repository phuoctrung-ppt/ai-sdlc-workflow (2 stars, last pushed 18d ago), licensed MIT. It adds 24 tokens to every session and 459 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
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.