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 wanghao9610/STAR --skill star-autogit clone --depth 1 https://github.com/wanghao9610/STARWrote 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/wanghao9610/star/star-auto)<a href="https://agentmods.dev/skills/wanghao9610/star/star-auto"><img src="https://agentmods.dev/badge/skills/wanghao9610/star/star-auto/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/wanghao9610/star/star-auto"><img src="https://agentmods.dev/badge/skills/wanghao9610/star/star-auto.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00041 | $0.00741 |
| Opus 5 | $0.00020 | $0.00370 |
| Sonnet 5 | $0.00008 | $0.00148 |
| Haiku 4.5 | $0.00004 | $0.00074 |
Grade A, and why
star-auto 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
Drive the workflow toward a goal
Read .agents/commands/star-auto.md from the current project root and follow it as the authoritative procedure.
When .env sets STAR_LANG=zh, or it is unset and the conversation is in Chinese, use .agents/commands/star-auto.zh-CN.md for the user-facing wording while keeping the English file's decisions.
Adapt its invocation spelling and model routing for Codex:
$star-auto <goal> [stop=<stop line>] [involve=<level>]is this command.$star-<name> <argument>is the spelling where the shared file writes/star-<name> <argument>.
For every run that this command starts, resolve its tier and any mode exception under conventions §10.8. Read the corresponding STAR_PLAN_MODEL, STAR_EXEC_MODEL, or STAR_READ_MODEL value once with the opening .env load. In a comma-separated value, take codex:<model> first, otherwise an untagged model; ignore entries tagged for another harness. Split a recognized final @<depth> from the model, retaining an unrecognized @ suffix as part of the model name. An empty result names no model or depth.
When the resolved model is non-empty, this Codex runtime can name a subagent model, and either that model is not an alias of the active session's actual model or the entry carries a supported per-dispatch depth, start the run with spawn_agent: pass the model explicitly and, when the tool and selected model accept it, pass the depth as reasoning_effort. A usable explicit depth triggers this fresh run even when the model is unchanged. Pass fork_turns: "none" whenever the runtime exposes that field, including for blind reads: this runtime does not allow a model or effort override with a full fork. Use the same rule for both unmarked and † skills. If a dispatch rejects the requested depth, first confirm it started no work; then retry without reasoning_effort only when the model itself differs, otherwise retain the shared local route, and state once that the depth was not applied. With no routing difference, retain the shared procedure: load and follow an unmarked project star-* skill here, or dispatch the † skill's subagent as the shared file requires, without a model or effort override.
Every model- or effort-routed subagent brief is self-contained: tell it to read the selected skill's project SKILL.md in full; include the original skill invocation, its resolved tier=<name> and involve=<level> tokens, auto=unattended when this invocation carries it, and the language resolved from STAR_LANG or the dialogue. Include the active session's actual provenance as a model id only. Never pass the parent session's model-resolver command or treat its output as the child's model provenance: the child resolves and records its own provenance from its own session context. Record the requested depth, or default, beside the tier and actual child model. These routing rules inherit the shared procedure's confirmation, STOP-line, sandbox, and approval limits; they authorize no additional action.
If .agents/commands/star-auto.md is missing, report that the project does not contain the STAR auto procedure instead of guessing from the plugin package.
What ships with it
2 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.
- 2d ago Changed · +4 lines ec290b4f3c5e
- 10d ago First seen · 19 lines · 41 tokens per session scan A 6798e9d99361
star-auto is a skill published in the GitHub repository wanghao9610/STAR (52 stars, last pushed 2d ago), licensed MIT. It adds 41 tokens to every session and 741 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 2026-08-30.
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