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 skills add chrisvoncsefalvay/autostar --skill autostar-claude-ai-skillgit clone --depth 1 https://github.com/chrisvoncsefalvay/autostarWrote 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/skills/chrisvoncsefalvay/autostar/autostar-claude-ai-skill)<a href="https://agentmods.dev/skills/chrisvoncsefalvay/autostar/autostar-claude-ai-skill"><img src="https://agentmods.dev/badge/skills/chrisvoncsefalvay/autostar/autostar-claude-ai-skill/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/skills/chrisvoncsefalvay/autostar/autostar-claude-ai-skill"><img src="https://agentmods.dev/badge/skills/chrisvoncsefalvay/autostar/autostar-claude-ai-skill.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 | $0.00144 | $0.03943 |
| Opus 5 | $0.00072 | $0.01972 |
| Sonnet 5 | $0.00029 | $0.00789 |
| Haiku 4.5 | $0.00014 | $0.00394 |
Grade A, and why
autostar-web 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 13d 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 — 445 lines — stays where its author put it; the contents beside it link to each section on GitHub.
a* (autostar) — web runtime
A generalised autonomous optimisation loop — soft RLVR for the masses. The user defines a goal; the system runs structured experiments, evaluates progress across independent tracks, reflects at strategic checkpoints, and learns from every attempt — including learning how to learn better the next time.
If you can measure it, you can improve it.
Web runtime constraints
This package runs inside a web chat runtime with reduced capabilities:
- No subprocess access —
external_toolverifiers are unavailable - No unrestricted local files — file read/write is limited
- Memory: connector-backed > project-pack > none (see
references/memory.md)
Do not silently downgrade external_tool verifiers to llm_judge. If the user
requests a verifier type that requires subprocess access, explain the limitation
and ask them to choose an alternative.
Experimental-first principle
a* is an experimental optimisation loop. Do not reach for external mathematical
optimisers or solvers (e.g. scipy.optimize, cvxpy, linear/quadratic
programming solvers, evolutionary algorithm libraries, Bayesian optimisation
frameworks, or any other off-the-shelf optimisation package) as a shortcut to
improving the artifact. The value of a* is in the structured
explore-evaluate-reflect cycle, not in delegating the search to a solver.
If at any point during onboarding, pre-run analysis, or execution you believe the problem is well-suited to a closed-form or mathematical optimisation approach, you must ask the user first before pursuing it. Present it as an alternative:
"This problem looks like it could be approached with a mathematical optimiser (e.g. [specific method]). Would you like me to try that instead of running the experimental loop, or would you prefer to proceed with a*?"
Do not silently install, import, or invoke an external optimiser. Do not reframe the a* loop as a wrapper around a solver. If the user explicitly opts for a mathematical approach, that is a different workflow — not an a* run.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 13d ago First seen · 445 lines · 144 tokens per session scan A 7858f65cf8bd
autostar-web is a skill published in the GitHub repository chrisvoncsefalvay/autostar (39 stars, last pushed 5mo ago), licensed MIT. It adds 144 tokens to every session and 3,943 once invoked, about $0.0007 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.
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