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.
git clone --depth 1 https://github.com/HoangNguyen0403/agent-skills-standardWrote 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/hoangnguyen0403/agent-skills-standard/design-solution)<a href="https://agentmods.dev/commands/hoangnguyen0403/agent-skills-standard/design-solution"><img src="https://agentmods.dev/badge/commands/hoangnguyen0403/agent-skills-standard/design-solution/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/hoangnguyen0403/agent-skills-standard/design-solution"><img src="https://agentmods.dev/badge/commands/hoangnguyen0403/agent-skills-standard/design-solution.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 | $0.00000 | $0.00858 |
| Opus 5 | $0.00000 | $0.00429 |
| Sonnet 5 | $0.00000 | $0.00172 |
| Haiku 4.5 | $0.00000 | $0.00086 |
Grade A, and why
design-solution 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 9d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Design Solution
Turn an approved PRD or implementation goal into SRS/FRS technical requirements (How), architecture, contracts, and verification decisions.
Input: $ARGUMENTS
Optional args: slug=, ticket=<id/url>, mode=interactive|autonomous|channel, channel=, auto_continue=true|false, profile=business|hybrid|technical.
Instructions
Execute the following steps for $ARGUMENTS.
Design Solution Workflow (SRS/FRS / How)
Goal: Produce a build-ready technical design with explicit boundaries, contracts, risks, and tests.
Steps
- Load inputs:
- Load baseline SRS/FRS section,
common-software-requirements, PRD or ticket, implementation plan, matched framework skills, architecture docs, and trace sourceBRD-OBJ-* -> REQ-* -> AC-*.
- Load baseline SRS/FRS section,
- Define architecture:
- Name bounded contexts and module owners.
- Define dependency direction and component RACI.
- Choose sync, async, or hybrid communication.
- Record data ownership and migration needs.
- Define early mock/schema contracts so frontend, mobile, and backend can start in parallel.
- Define contracts:
- Functional flows (FRS): user/system steps, inputs/outputs, validations, and error states.
- For complex flows, use one actor, one goal, one session; split normal course from alternatives and exceptions.
- Requirement cards: statement, priority, status, source, behavior, NFRs, measurement, and verification lane.
- API inputs/outputs and interface contracts (OpenAPI/Protobuf).
- Events/jobs and async guarantees (at-least-once, idempotent).
- Storage shape, ownership, retention, and migration rules.
- Security, permission, and privacy checks.
- NFR thresholds for performance, reliability, and scalability.
- Plan verification:
- Unit, integration, E2E, visual, mobile, security, and migration checks.
- Failure mode analysis for dependencies, fallbacks, retries, and rollback/degradation.
- Save technical requirements to
docs/srs/srs-[slug].mdwhen file writes are allowed. - Record evidence in
docs/srs/srs-walkthrough.md.
- Record ADR:
- Write one concise ADR when architecture or public contract changes.
- Continue when patterns are inferable; return BLOCKED for cross-team contracts, migrations, permissions, or NFR uncertainty.
- Route next step to
implementation-readinessordev-fix.
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.
- 9d ago First seen · 89 lines · 0 tokens per session scan A d3fc8afa25f4
design-solution is a command published in the GitHub repository HoangNguyen0403/agent-skills-standard (565 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 858 tokens. 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 2026-09-03.
Other commands, from other repositories
api-design
A command for guiding API design work through the Universal Development Standards workflow.
harden
Add loading/error/empty states, mounted guards, disposal, null safety tightening. Makes screens production-ready.
explain
Explain how the following works in this NestJS codebase: $ARGUMENTS.
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.