Borrowing it
Nothing to install: this file belongs to phuoctrung-ppt/ai-sdlc-workflow. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/phuoctrung-ppt/ai-sdlc-workflow/master/.cursor/commands/architecture-plan.mdgit 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/architecture-plan)<a href="https://agentmods.dev/commands/phuoctrung-ppt/ai-sdlc-workflow/architecture-plan"><img src="https://agentmods.dev/badge/commands/phuoctrung-ppt/ai-sdlc-workflow/architecture-plan/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/phuoctrung-ppt/ai-sdlc-workflow/architecture-plan"><img src="https://agentmods.dev/badge/commands/phuoctrung-ppt/ai-sdlc-workflow/architecture-plan.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00033 | $0.01032 |
| Opus 5.5 | $0.00013 | $0.00413 |
| Sonnet 5.5 | $0.00007 | $0.00206 |
| Haiku 4.5 | $0.00003 | $0.00103 |
Grade A, and why
architecture-plan 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Act as Orchestrator driving @architect-planner and @judge-agent. Stay inside the current .cursor workflow.
Mode selection
- GENESIS —
/architecture-plan brainstorming {idea} - BREAKDOWN —
/architecture-plan(active plan exists)
GENESIS mode — /architecture-plan brainstorming {idea}
Input: {idea}
Step 0 — Build context
python3 .cursor/context/context-builder.py --phase brainstorm --task "{idea}" --agent architect-planner
python3 .cursor/context/context-builder.py --phase plan --task "{idea}" --agent architect-planner
Step 1 — BRAINSTORM (HARD-GATE, chat only)
Concept brief (chat):
- What it does — job-to-be-done
- Target users
- Core features — MVP vs Later
- Suggested profile —
frontend|backend|fullstack|library|cli - Suggested layers on/off — frontend, backend, database, multi-tenancy, queue, auth, devops, ai-llm
- Design/UX needs — only if frontend layer likely on
- Domain & data — only entities/tenancy if database or multi-tenancy likely on
- Constraints
- 2–3 architecture approaches + recommendation
⏸️ STOP — wait for explicit approval of concept + profile/layers + MVP boundary.
Step 2 — STANDARDIZE DOMAIN (fill AGENTS.md)
After approval:
- Write §0 Project Profile first (profile, layout, layers table).
- Fill §1–§3 from the approved concept.
- Fill optional sections only when layer is on:
- multi-tenancy on → §4; else
N/A — layer multi-tenancy off - database on → §9 entities; else N/A
- queue on → §14; else N/A
- ai-llm on → AI rules in §6; else skip
- multi-tenancy on → §4; else
- §5 roster: list agents that match active layers (+ always-on planner/judge/qa).
- Align config:
python3 .cursor/context/profile-sync.py --from-agents
Optionally tighten worker-scopes.json to §3 paths (orchestrator approval for expansion).
Step 3 — CREATE ARCHITECTURE + ADRs
docs/architecture.md— only containers/flows for active layers- ADRs for foundational decisions that apply (do not invent tenancy/DB ADRs if those layers are off)
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.
- 1mo ago Changed · -13 lines · -44 tokens per session c2c7d05cb2a3
- 1mo ago First seen · 122 lines · 77 tokens per session scan A 0c38338c0cae
architecture-plan is a command published in the GitHub repository phuoctrung-ppt/ai-sdlc-workflow (2 stars, last pushed 1mo ago), licensed MIT. It adds 33 tokens to every session and 1,032 once invoked, about $0.0001 per session on Opus 5.5. 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.
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.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.