Borrowing it
Nothing to install: this file belongs to DanielPodolsky/ownyourcode. 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/DanielPodolsky/ownyourcode/main/.claude/skills/career/star-stories/SKILL.mdgit clone --depth 1 https://github.com/DanielPodolsky/ownyourcodeWrote 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/danielpodolsky/ownyourcode/star-stories)<a href="https://agentmods.dev/skills/danielpodolsky/ownyourcode/star-stories"><img src="https://agentmods.dev/badge/skills/danielpodolsky/ownyourcode/star-stories/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/danielpodolsky/ownyourcode/star-stories"><img src="https://agentmods.dev/badge/skills/danielpodolsky/ownyourcode/star-stories.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 | $0.00035 | $0.01296 |
| Opus 5.5 | $0.00014 | $0.00518 |
| Sonnet 5.5 | $0.00007 | $0.00259 |
| Haiku 4.5 | $0.00003 | $0.00130 |
Grade A, and why
star-story-extraction 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 1mo 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 — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
STAR Story Extraction
"Every feature you build is an interview answer waiting to be told."
Purpose
Transform completed work into compelling interview stories using the STAR method. These stories demonstrate real problem-solving ability.
The STAR Method
| Component | Question | Focus |
|---|---|---|
| Situation | "What was the context?" | Set the scene, explain the problem |
| Task | "What were YOU responsible for?" | YOUR specific role and responsibility |
| Action | "What did YOU do?" | Specific technical actions YOU took |
| Result | "What was the outcome?" | Impact, metrics, improvements |
Extraction Flow
Step 1: Identify the Story Type
What kind of problem did you solve?
| Story Type | Good For Questions Like |
|---|---|
| Technical challenge | "Tell me about a difficult bug you solved" |
| Feature implementation | "Describe a feature you're proud of" |
| Performance optimization | "How did you improve system performance?" |
| Security fix | "Tell me about a security issue you addressed" |
| Refactoring | "Describe a time you improved code quality" |
| Learning curve | "Tell me about a time you learned something quickly" |
Step 2: Guide Through STAR
Situation (2-3 sentences)
"What was the context? What problem or challenge existed before you started?"
Good elements:
- Business context (why it mattered)
- Technical constraints
- Scale/impact of the problem
Avoid:
- Too much background
- Irrelevant details
- Blaming others
Task (1-2 sentences)
"What were YOU specifically responsible for? What was your role?"
Good elements:
- Clear ownership
- Specific scope
- Why you were the one to do it
Avoid:
- "We did this" (use "I")
- Vague responsibilities
Action (The meat - 3-5 sentences)
"Walk me through the specific steps YOU took. Be technical."
Good elements:
- Specific technologies used
- Problem-solving approach
- Trade-offs considered
- Technical decisions made
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.
- 1mo ago First seen · 196 lines · 35 tokens per session scan A ce6497656a18
star-story-extraction is a skill published in the GitHub repository DanielPodolsky/ownyourcode (292 stars, last pushed 4mo ago), licensed MIT. It adds 35 tokens to every session and 1,296 once invoked, about $0.0001 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.
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