Borrowing it
Nothing to install: this file belongs to wanghao9610/STAR. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/wanghao9610/STAR/main/.agents/skills/star-plan-reviser/SKILL.mdgit 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-plan-reviser)<a href="https://agentmods.dev/skills/wanghao9610/star/star-plan-reviser"><img src="https://agentmods.dev/badge/skills/wanghao9610/star/star-plan-reviser/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-plan-reviser"><img src="https://agentmods.dev/badge/skills/wanghao9610/star/star-plan-reviser.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 | $0.00051 | $0.03574 |
| Opus 5 | $0.00026 | $0.01787 |
| Sonnet 5 | $0.00010 | $0.00715 |
| Haiku 4.5 | $0.00005 | $0.00357 |
Grade A, and why
star-plan-reviser 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 4d 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Plan Reviser
Invocation: star-plan-reviser PLAN_NAME [DESCRIPTION]. Resolve the plan first. Natural language that clearly gives up or restores the direction selects drop or restore and supplies its reason; otherwise run the evidence review. It may also approve named revision items. A request to review, audit, or read the report without changes stops after the report and does not enter revision Q&A. With no settled target, list candidates and ask.
Shared conventions. Resolve the invocation target and mode first. Then read only the sections of docs/mds/star-workflow/research-workflow-conventions.md that the selected goal uses; load cited references/ and assets/ only when entering their branch or mode. Read .env once for the needed STAR_LANG, INVOLVE, STAR_*_MODEL, and runtime values; reuse values and convention text still visible verbatim. Resolve language under conventions §7.6: an explicit user request first, then a valid STAR_LANG, then the dialogue or invocation language; use the corresponding localized resources. SKILL_zh.md is for human readers and is never loaded at runtime. Preserve an existing document's frontmatter language. Clear natural-language instructions may select the target and scope and authorize the corresponding action; do not ask again for work already authorized.
After resolving the target and the drop, restore, or review path, run scripts/scan.sh --slim; use its plan frontmatter, sub-plan indexes, and run-log frontmatter as raw scope input, then read the target and governing references at the evidence step. If it fails, read the plans directly and report the fallback.
Passing a tier model. Resolve the selected mode's PLAN, EXEC, or READ model for the current harness under conventions §10.8. Pass the resolved value when delegation accepts a per-dispatch model; use a fresh, self-contained context if model selection conflicts with full context inheritance. READ-tier collection and blind review do not inherit the producer's conversation. An empty value omits the model override and preserves the host or session default; if a configured value cannot be selected, keep the work here and state why. Never translate provider model names, invent parameters, or launch another CLI. A delegate carrying tier= does not relocate the whole run again and records its actual model from its own session provenance.
What ships with it
11 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.
- agents/openai.yaml 62 B
- assets/review_report_template_zh.md 2.3 KB
- assets/review_report_template.md 2.3 KB
- references/drop_rules_zh.md 7.3 KB
- references/drop_rules.md 7.3 KB
- references/review_spec_zh.md 5.6 KB
- references/review_spec.md 5.8 KB
- references/revision_rules_zh.md 5.9 KB
- references/revision_rules.md 6.1 KB
- scripts/scan.sh 24 KB runs code
- SKILL_zh.md 15 KB
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.
- 4d ago Changed · -24 lines · -123 tokens per session 15f6b6142205
- 11d ago First seen · 133 lines · 174 tokens per session scan A 8aa6650683ee
star-plan-reviser is a skill published in the GitHub repository wanghao9610/STAR (52 stars, last pushed 4d ago), licensed MIT. It adds 51 tokens to every session and 3,574 once invoked, about $0.0003 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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