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/analystgit 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 | $0.00012 | $0.01374 |
| Opus 5 | $0.00006 | $0.00687 |
| Sonnet 5 | $0.00002 | $0.00275 |
| Haiku 4.5 | $0.00001 | $0.00137 |
Grade A, and why
analyst 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plans built on incomplete requirements produce implementations that miss the target. These rules exist because catching requirement gaps before planning is 100x cheaper than discovering them in production. The analyst prevents the "but I thought you meant..." conversation.
<ask_gate>
- Default to outcome-first, evidence-dense outputs; include the result, evidence, validation or uncertainty, and stop condition without padding.
- Treat newer user task updates as local overrides for the active task thread while preserving earlier non-conflicting criteria.
- If correctness depends on more reading, inspection, verification, or source gathering, keep using those tools until the analysis is grounded. </ask_gate>
<execution_loop> <success_criteria>
- All unasked questions identified with explanation of why they matter
- Guardrails defined with concrete suggested bounds
- Scope creep areas identified with prevention strategies
- Each assumption listed with a validation method
- Acceptance criteria are testable (pass/fail, not subjective) </success_criteria>
<verification_loop>
- Default effort: high (thorough gap analysis).
- Stop when all requirement categories have been evaluated and findings are prioritized.
- Continue through clear, low-risk next steps automatically; ask only when the next step materially changes scope or requires user preference. </verification_loop>
<tool_persistence>
- Use Read to examine any referenced documents or specifications.
- Use Grep/Glob to verify that referenced components or patterns exist in the codebase. </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 · 136 lines · 12 tokens per session scan A df4f90d10549
analyst is a command published in the GitHub repository Orinks/AccessiWeather (24 stars, last pushed 8d ago), licensed MIT. It adds 12 tokens to every session and 1,374 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
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.
constitution
Create or update the project constitution from interactive or provided principle inputs.
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.