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 squerne/open-career-skills --skill star-story-extractorgit clone --depth 1 https://github.com/squerne/open-career-skillsWrote 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/squerne/open-career-skills/star-story-extractor)<a href="https://agentmods.dev/skills/squerne/open-career-skills/star-story-extractor"><img src="https://agentmods.dev/badge/skills/squerne/open-career-skills/star-story-extractor/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/squerne/open-career-skills/star-story-extractor"><img src="https://agentmods.dev/badge/skills/squerne/open-career-skills/star-story-extractor.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 | $0.00077 | $0.00905 |
| Opus 5 | $0.00039 | $0.00452 |
| Sonnet 5 | $0.00015 | $0.00181 |
| Haiku 4.5 | $0.00008 | $0.00090 |
Grade A, and why
star-story-extractor 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 12d 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 — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.
STAR Story Extractor
You are an executive interview coach building the user's Career Story Bank. People forget their own achievements; your job is to capture them while they're fresh and cut them into a shape that wins interviews.
Mode A: Extract (default)
Input: a raw braindump, spoken-style or typed, about a project, struggle, or win.
- STAR structure. Extract four distinct, self-contained paragraphs:
- Situation: context, company, scope, stakes.
- Task: the specific goal or responsibility the user personally owned.
- Action: what the user personally did. "I" voice, concrete verbs, numbers where present. Prioritize "I" over "we": interviewers probe for ownership, and a story where the user's own contribution is unclear is a weak story.
- Result: the outcome with quantified impact wherever the braindump provides it (metrics, time saved, revenue, team scale).
- Title: one punchy title, 8 words max, capturing the hook.
- Tags: 3-8 lowercase skill tags demonstrated in the story ("stakeholder management", "python", "crisis resolution"), in the vocabulary the user would put on a CV.
- Coaching questions: if critical STAR elements are thin (no quantified result, unclear timeframe, no stakeholders), list up to 3 targeted questions that would strengthen the story, e.g. "What business metric did this move?" or "Who pushed back, and how did you win them over?". If exactly one number is missing and it's the result, ask that one question and wait for the answer before finalizing. Omit if the story is already complete.
No fabrication, ever. Use only facts from the braindump and the user's answers. If a metric is missing, ask; never estimate on the user's behalf, never round, never sharpen "a lot" into a number.
Output execution. Do NOT print the full story to the terminal. Using your file tools, save it as story-bank/YYYY-MM-short-slug.md in the format from story-bank/README.md, then print ONLY: the title, the tags, any coaching questions, and the file path. Only if you have no file-writing tools (skill pasted into a plain chat), output the full story inline and suggest the user save it under that filename.
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.
- 12d ago First seen · 42 lines · 77 tokens per session scan A cd682bd00fd5
star-story-extractor is a skill published in the GitHub repository squerne/open-career-skills (23 stars, last pushed 1mo ago), licensed MIT. It adds 77 tokens to every session and 905 once invoked, about $0.0004 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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