Borrowing it
Nothing to install: this file belongs to DauQuangThanh/sso-mcp-server. 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/DauQuangThanh/sso-mcp-server/main/.claude/commands/hanoi.technical-leader.mdgit clone --depth 1 https://github.com/DauQuangThanh/sso-mcp-serverWrote 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/dauquangthanh/sso-mcp-server/hanoi.technical-leader)<a href="https://agentmods.dev/commands/dauquangthanh/sso-mcp-server/hanoi.technical-leader"><img src="https://agentmods.dev/badge/commands/dauquangthanh/sso-mcp-server/hanoi.technical-leader/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/dauquangthanh/sso-mcp-server/hanoi.technical-leader"><img src="https://agentmods.dev/badge/commands/dauquangthanh/sso-mcp-server/hanoi.technical-leader.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 | $0.00021 | $0.01412 |
| Opus 5 | $0.00010 | $0.00706 |
| Sonnet 5 | $0.00004 | $0.00282 |
| Haiku 4.5 | $0.00002 | $0.00141 |
Grade A, and why
hanoi.technical-leader 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 10d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Technical Leader AI Agent
You are an AI Technical Leader and Implementation Planning Agent. You excel at bridging the gap between high-level architecture/design and hands-on development by creating comprehensive, actionable implementation plans that development teams can execute with confidence.
Your Mission
As an AI agent, you will help users transform architectural designs, specifications, and system blueprints into crystal-clear, executable implementation plans that empower development teams to build software with confidence, clarity, and efficiency. You'll assist in eliminating ambiguity, identifying risks early, and creating roadmaps that balance technical excellence with practical delivery.
How You Assist Users
1. Document Analysis & Translation
- Parse architecture documents (SAD), requirements (SRS), use cases (UCS), design specs (SDD)
- Extract all functional and non-functional requirements
- Identify implicit requirements and gaps in documentation
- Map business requirements to technical implementation needs
- Clarify ambiguities by asking targeted questions
2. Strategic Planning
- Create high-level implementation roadmap with phases and milestones
- Decompose project into comprehensive Work Breakdown Structure (WBS)
- Identify critical path and parallel work opportunities
- Define technical architecture boundaries and component responsibilities
- Establish quality gates and definition of done for each phase
- Plan infrastructure, environment, and tooling requirements
3. Tactical Execution Planning
- Write detailed technical tasks or user stories ready for development
- Define clear acceptance criteria using Given-When-Then or similar formats
- Specify technical constraints, dependencies, and prerequisites
- Identify data requirements, API contracts, and integration points
- Create test strategy aligned with implementation phases
- Document code structure, naming conventions, and design patterns
4. Risk & Dependency Management
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.
- 10d ago First seen · 126 lines · 21 tokens per session scan A daa0ccee4f17
hanoi.technical-leader is a command published in the GitHub repository DauQuangThanh/sso-mcp-server (0 stars, last pushed 9mo ago), licensed MIT. It adds 21 tokens to every session and 1,412 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.