star-story-extraction

star-story-extraction is a skill for Claude Code, Codex from DanielPodolsky/ownyourcode. It costs 35 tokens per session (1,296 once invoked), scanned A, original, MIT.

A guide for turning completed work into interview stories using the STAR method: Situation, Task, Action, and Result. STAR is a common structure for answering behavioral job-interview questions.

In plain words
What is it for?
Use it to prepare stories about technical challenges, feature work, performance improvements, security fixes, refactoring, or learning something quickly.
Why use it?
It helps explain the context, your personal responsibility, the steps you took, and the outcome clearly. This is more useful in an interview than a bare list of technologies.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/danielpodolsky/ownyourcode/star-stories
Any agent
npx skills add DanielPodolsky/ownyourcode --skill star-stories
Clone the repo
git clone --depth 1 https://github.com/DanielPodolsky/ownyourcode

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for star-story-extraction

README.md
[![agentmods](https://agentmods.dev/badge/skills/danielpodolsky/ownyourcode/star-stories.svg)](https://agentmods.dev/skills/danielpodolsky/ownyourcode/star-stories)
Your own site
<a href="https://agentmods.dev/skills/danielpodolsky/ownyourcode/star-stories"><img src="https://agentmods.dev/badge/skills/danielpodolsky/ownyourcode/star-stories.svg" alt="Measured on agentmods" height="20"></a>
Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,296 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00035 $0.01296
Opus 5 $0.00017 $0.00648
Sonnet 5 $0.00007 $0.00259
Haiku 4.5 $0.00003 $0.00130

Measured 5d ago against content hash ce6497656a18, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 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.

.claude/skills/career/star-stories/SKILL.md · 196 lines

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

Read the full file on GitHub · 196 lines

Changes

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.

  1. 5d ago First seen · 196 lines · 35 tokens per session scan A ce6497656a18

Subscribe to this mod's changes

star-story-extraction is a skill published in the GitHub repository DanielPodolsky/ownyourcode (281 stars, last pushed 2mo ago), licensed MIT. It adds 35 tokens to every session and 1,296 once invoked, about $0.0002 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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