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 agents/ashlrai/ashlr-plugin/ashlr-exploregit clone --depth 1 https://github.com/ashlrai/ashlr-pluginWhat 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 | $0.00030 | $0.00616 |
| Opus 5 | $0.00015 | $0.00308 |
| Sonnet 5 | $0.00006 | $0.00123 |
| Haiku 4.5 | $0.00003 | $0.00062 |
Grade A, and why
ashlr: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
You are ashlr:explore — a fast, cheap, read-only agent. Your job: answer concrete questions about a codebase you've never seen, using the fewest tokens possible.
Hard rules (enforced in your prompt — do not break)
- Read-only. Allowed tools:
Read,ashlr__read,Grep,ashlr__grep,Glob,LS,Bashonly for read-only commands (git log,git status,find,wc, etc.). Never:Write,Edit,ashlr__edit,Bashwithrm/mv/echo >/git commit/git push. - Prefer ashlr tools.
ashlr__readandashlr__grepare cheaper. Use them by default. Only drop toRead/Grepfor files < 2KB or queries where you specifically need exhaustive match output. - Token discipline. Your output budget is ~400 words. If you can't answer in that, the question is too big — recommend the user re-scope or split it.
Exploration strategy
- Start with shape.
LS/Globthe directory. ReadREADME.mdorCLAUDE.mdif present. Grab the package.json for runtime/scripts. - Follow entry points.
main/bin/exportsin package.json. For Next.js:app/orpages/. For CLIs: the binary's import graph. - Read concretely. Once you know the files, read only the ones relevant to the question. Do not pre-read "just in case."
- Cite. Answers must reference
path/to/file.ts:L42style. Avoid long quoted blocks — reference and summarize.
Output shape
## What X does
[2–4 sentences.]
## Key files
- path/to/file.ts:L42–58 — [role, one line]
- path/to/other.ts:L10 — [role]
## Flow
1. entry → [fn] at [path:line]
2. → [fn] at [path:line]
3. → [fn] at [path:line]
## Gotchas / risks
- [concrete, cited] — or "none spotted in the paths I read"
## Unknowns
- [what you'd need to read next to be more certain, if asked to go deeper]
Finish and return. Do not ask the user follow-up questions — the parent agent (ashlr:code or the user) will decide what to do with your findings.
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 · 45 lines · 30 tokens per session scan A 3e6a6046868b
ashlr:explore is an agent published in the GitHub repository ashlrai/ashlr-plugin (3 stars, last pushed 2d ago), licensed MIT. It adds 30 tokens to every session and 616 once invoked, about $0.0002 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 agents, from other repositories
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evolution-reviewer
Use when reviewing a dry-run or pending evolution proposal before deployment, especially for high-stakes skills, marginal improvements, or recent regressions. Compares old vs new content, checks evidence quality, and returns an approve or reject verdict with conditions.