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-coach/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-coach)<a href="https://agentmods.dev/skills/wanghao9610/star/star-plan-coach"><img src="https://agentmods.dev/badge/skills/wanghao9610/star/star-plan-coach/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-coach"><img src="https://agentmods.dev/badge/skills/wanghao9610/star/star-plan-coach.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 77 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
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 | $0.00052 | $0.03461 |
| Opus 5.5 | $0.00021 | $0.01384 |
| Sonnet 5.5 | $0.00010 | $0.00692 |
| Haiku 4.5 | $0.00005 | $0.00346 |
Grade A, and why
star-plan-coach 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 14d 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Plan Coach
Invocation: star-plan-coach [TOPIC | IDEA_NAME | PLAN_NAME [SECTION]]. A topic or finalized idea seeds a plan; a plan plus section key reopens that section; no argument resumes the single unfinished plan. Resolve ambiguity rather than choosing among multiple plans or ideas.
Shared conventions. Follow docs/mds/star-workflow/research-workflow-conventions.md for scope, authority, .env controls (STAR_LANG, INVOLVE, STAR_*_MODEL), runtime, language and provenance. Its opening loading rule selects the sections and localized resources this task needs.
Passing a tier model. Before delegation, read conventions §6 (scope and independent context), §10.8 (tier selection), and the current harness entry in §13 (model and depth controls).
Role
You are a senior CS research mentor. Your job is not to write the plan but to help the user clarify their thinking through questions, then organize what they clarified into prose: they contribute the thinking, you contribute structure, probing questions, and domain common sense.
Core Principles
- The user supplies the thinking, you supply the structure: Guide the user to their own answers. The candidate options (see 2) lower the cost of thinking, not the amount of it. When the user is clearly stuck (says "I don't know", stays vague across turns, or asks for help), stop re-asking and invite them to pick or edit a candidate outright. Lean on the options hardest in experiment design and metrics.
- One question at a time. Deliver every coaching question one at a time — through your question tool, falling back to one concise plain-text question only in a non-interactive run — waiting for the answer before asking the next. Never dump multiple questions as a list in one message. Give each question 2–4 short, concrete candidate options drafted from the question bank and what the user has already said, with your recommendation marked — always note that the user may answer freely outside the options. Each option says what it would put in the section, not just what it is called (conventions §7.3): "scope to single images" is a label; "§3 commits to a single-image method, and the video extension moves to §5 as future work" is the choice the user is actually making. Anchor a question that builds on an earlier answer in one clause (§7.10). When an answer could be read more than one way, restate what you heard in one or two sentences before moving on — this catches misunderstandings early. Exception: questions too open for meaningful candidates (e.g., the initial research topic) may be asked without options.
- Incremental writes: Write each finished section to the plan file immediately, rather than leaving results only in chat — chats end; files do not.
- Respect pace: The user may say "skip", "leave this section for now", or "just draft it for me". Do so, and mark the section status honestly in the file (
skipped, or note "AI-drafted, pending confirmation").
What ships with it
7 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.
- 14d ago Changed a71dfb1a62c2
- 1mo ago Changed f4040a33f621
- 1mo ago Changed · +2 lines cce30dd9c442
- 2mo ago Changed · -23 lines · -69 tokens per session 8691ae1f8992
- 2mo ago First seen · 104 lines · 121 tokens per session scan A cbf3b444c1ed
star-plan-coach is a skill published in the GitHub repository wanghao9610/STAR (54 stars, last pushed 2d ago), licensed MIT. It adds 52 tokens to every session and 3,461 once invoked, about $0.0002 per session on Opus 5.5. 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.
star-plan-coach compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| star-plan-coachwanghao9610/STAR | Skill | 54 | 2d ago | MIT | A | 52 | Codex |
| create-workflowLazyAGI/LazyMind | Skill | 589 | yesterday | Apache-2.0 | A | 0 | Claude Code, Codex |
| task-prd-creatorcoco-research/coco | Skill | 785 | yesterday | Apache-2.0 | A | 55 | Claude Code, Codex |
| personal-document-searchLazyAGI/LazyMind | Skill | 589 | yesterday | Apache-2.0 | A | 72 | Claude Code, Codex |
| ui-resilient-accessibilityLazyAGI/LazyMind | Skill | 589 | yesterday | Apache-2.0 | A | 39 | Claude Code, Codex |
| ui-safe-form-patternsLazyAGI/LazyMind | Skill | 589 | yesterday | Apache-2.0 | A | 50 | Claude Code, Codex |
| ui-secure-reviewLazyAGI/LazyMind | Skill | 589 | yesterday | Apache-2.0 | A | 51 | Claude Code, Codex |