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/exploregit 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 | $0.00010 | $0.00816 |
| Opus 5 | $0.00005 | $0.00408 |
| Sonnet 5 | $0.00002 | $0.00163 |
| Haiku 4.5 | $0.00001 | $0.00082 |
Grade A, and why
explore 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.
What it actually says
<ask_gate> Search first, ask never by default. For ambiguous queries, search multiple plausible names and report assumptions. </ask_gate>
<context_budget>
-
Check size before reading large files; for files over 200 lines, inspect symbols/outline first and read targeted ranges.
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For files over 500 lines, prefer symbol/structural search unless full content is explicitly required.
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Batch no more than 5 file reads at once; prefer structural/search tools over full-file reads. </context_budget>
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Default final-output shape: outcome-first and evidence-dense, with enough relationship detail, evidence boundaries, and stop condition for safe next action.
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Treat newer user task updates as local overrides for the active search thread while preserving earlier non-conflicting search goals.
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Keep searching while correctness depends on more passes, symbol lookups, or targeted reads.
<execution_loop>
- Identify the underlying need, not only the literal query.
- Start broad with multiple naming/search angles; use at least 3 searches for non-trivial lookups.
- Cross-check results across file, text, structural, and symbol searches where useful.
- Read only the relevant sections needed to explain relationships.
- Stop when the caller can proceed without asking “where exactly?” or “what about X?”. </execution_loop>
<success_criteria>
- Relevant matches are found, not just the first match.
- All reported paths are absolute.
- Relationships between files/patterns explained when relevant, including data/control flow.
- Boundary crossings to researcher/dependency-expert are called out instead of guessed. </success_criteria>
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 · 86 lines · 10 tokens per session scan A aa81173bf39a
explore is a command published in the GitHub repository Orinks/AccessiWeather (24 stars, last pushed 8d ago), licensed MIT. It adds 10 tokens to every session and 816 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.
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.