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-decomposer/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-decomposer)<a href="https://agentmods.dev/skills/wanghao9610/star/star-plan-decomposer"><img src="https://agentmods.dev/badge/skills/wanghao9610/star/star-plan-decomposer/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-decomposer"><img src="https://agentmods.dev/badge/skills/wanghao9610/star/star-plan-decomposer.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 | $0.00054 | $0.05822 |
| Opus 5 | $0.00027 | $0.02911 |
| Sonnet 5 | $0.00011 | $0.01164 |
| Haiku 4.5 | $0.00005 | $0.00582 |
Grade A, and why
star-plan-decomposer 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 5d 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Plan Analyse — plan decomposer
Invocation: star-plan-decomposer PLAN_NAME [DESCRIPTION]. Resolve the slug, numeric prefix, or filename before scanning. Remaining natural language may choose an axis, units, depth, or expansion scope and counts as authorization for those choices; ask only when a material decomposition choice remains unsettled.
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, run scripts/scan.sh --slim and treat its plan frontmatter, sub-plan indexes, placeholder counts, run-log frontmatter, and directory listings as raw input to Steps 0–1; still read the target plan in full. If it fails, read the plan files 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 65 B
- assets/subplan_template_zh.md 4.7 KB
- assets/subplan_template.md 5.1 KB
- references/decomposition_axes_zh.md 9.0 KB
- references/decomposition_axes.md 9.5 KB
- references/naming_convention_zh.md 3.6 KB
- references/naming_convention.md 3.7 KB
- references/subplan_rubric_zh.md 4.1 KB
- references/subplan_rubric.md 4.6 KB
- scripts/scan.sh 24 KB runs code
- SKILL_zh.md 23 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.
- 5d ago Changed · -27 lines · -148 tokens per session 82c7c7d5d2dd
- 12d ago First seen · 188 lines · 202 tokens per session scan A e362c8053409
star-plan-decomposer is a skill published in the GitHub repository wanghao9610/STAR (52 stars, last pushed today), licensed MIT. It adds 54 tokens to every session and 5,822 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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