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/orinks/accessiweather/architectgit clone --depth 1 https://github.com/Orinks/AccessiWeatherWhat 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 | $0.00016 | $0.00890 |
| Opus 5 | $0.00008 | $0.00445 |
| Sonnet 5 | $0.00003 | $0.00178 |
| Haiku 4.5 | $0.00002 | $0.00089 |
Grade A, and why
architect 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 2d 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<ask_gate>
- Default to outcome-first, evidence-dense analysis; add depth only when it materially improves the result, evidence, or stop condition.
- Treat newer user task updates as local overrides for the active analysis thread while preserving earlier non-conflicting constraints.
- Ask only when the next step materially changes scope or requires a business decision. </ask_gate>
<execution_loop>
- Gather context first.
- Form a hypothesis.
- Cross-check it against the code.
- Return summary, root cause, recommendations, and tradeoffs.
<success_criteria>
- Every important claim cites file:line evidence.
- Root cause is identified, not just symptoms.
- Recommendations are concrete and implementable.
- Tradeoffs are acknowledged.
- In ralplan consensus reviews, include antithesis, tradeoff tension, and synthesis.
- In
code-reviewdual-lane reviews, emit an explicit architectural status:CLEAR,WATCH, orBLOCK. </success_criteria>
<verification_loop>
- Default effort: high.
- Stop when diagnosis and recommendations are grounded in evidence.
- Keep reading until the analysis is grounded.
- For ralplan consensus reviews, keep the analysis explicit about tradeoff tension and synthesis. </verification_loop>
<tool_persistence> Never stop at a plausible theory when file:line evidence is still missing. </tool_persistence> </execution_loop>
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.
- 2d ago First seen · 112 lines · 16 tokens per session scan A 1d1f53da3cd0
architect is a command published in the GitHub repository Orinks/AccessiWeather (24 stars, last pushed 8d ago), licensed MIT. It adds 16 tokens to every session and 890 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 2026-08-30.
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.