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 tmargolis/career-navigator --skill mine-storiesgit clone --depth 1 https://github.com/tmargolis/career-navigatorWrote 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/tmargolis/career-navigator/mine-stories)<a href="https://agentmods.dev/skills/tmargolis/career-navigator/mine-stories"><img src="https://agentmods.dev/badge/skills/tmargolis/career-navigator/mine-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/tmargolis/career-navigator/mine-stories"><img src="https://agentmods.dev/badge/skills/tmargolis/career-navigator/mine-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 | $0.00066 | $0.01042 |
| Opus 5 | $0.00033 | $0.00521 |
| Sonnet 5 | $0.00013 | $0.00208 |
| Haiku 4.5 | $0.00007 | $0.00104 |
Grade A, and why
mine-stories 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 10d 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create and maintain a persistent interview story corpus so downstream interview skills never need to read full raw journals repeatedly.
Workflow
1. Resolve {user_dir} and required paths
Use:
{user_dir}/CareerNavigator/StoryCorpus.json(target corpus){user_dir}(raw source discovery root)
If StoryCorpus.json is missing, create it using the schema in step 5.
2. Discover source candidates
Scan {user_dir} recursively for likely story-bearing files, prioritizing:
- Journal and weekly logs
- Interview debrief notes
- PKM exports/notes (including Notion and Capacities content when available via connector/MCP or local export)
- Resume/CV/cover-letter prose
- Plain text/markdown notes with dated entries
Exclude:
{user_dir}/CareerNavigator/*.json- Generated artifacts that are not user-authored source evidence
- Binary/media files that cannot be parsed
3. Detect incremental work vs full build
If StoryCorpus.json already exists:
- Build a file fingerprint map using source path + modified time.
- Skip files already processed with unchanged modified time.
- Process only new/changed files.
If no prior corpus metadata exists, run full build once.
4. Extract story candidates (Layer 1)
For each new/changed source:
- Chunk by natural entry boundary (date, heading, section, paragraph blocks).
- Run a cheap extraction pass with this intent:
"Extract any anecdote, decision, challenge, outcome, or project detail from this entry. Output structured JSON."
- Keep extracted candidates, not raw source chunks.
Each candidate should include:
- short narrative summary
- date (explicit or inferred if strongly supported)
- competency/theme tags
- ownership/result signals
- confidence score for extraction quality
5. Write / merge StoryCorpus.json (Layer 2)
Use this top-level shape:
{
"meta": {
"created": "YYYY-MM-DD",
"updated": "YYYY-MM-DD",
"version": "1.0",
"description": "Interview story corpus extracted from user-owned sources for prep and mock interview retrieval."
},
"stories": [
{
"story_id": "story-uuid",
"source": "journal | pkm | debrief | resume | other",
"source_path": "relative/path/to/file",
"source_entry_ref": "date heading or chunk id",
"date": "YYYY-MM-DD",
"raw_summary": "Concise evidence summary from extraction.",
"themes": ["technical_leadership", "crisis_management"],
"competencies": ["problem_solving", "ownership", "cross_functional"],
"result_signal": true,
"ownership_signal": true,
"star_ready": false,
"star": {
"situation": "",
"task": "",
"action": "",
"result": ""
},
"quality": {
"clarity": "low | medium | high",
"specificity": "low | medium | high",
"credibility": "low | medium | high"
},
"embedding": [],
"score_hint": 0.0,
"last_refreshed": "YYYY-MM-DD"
}
],
"source_index": [
{
"path": "relative/path",
"mtime": "ISO-8601",
"status": "processed | skipped",
"last_processed": "ISO-8601"
}
]
}
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.
- 10d ago First seen · 142 lines · 66 tokens per session scan A 8ee3240b89aa
mine-stories is a skill published in the GitHub repository tmargolis/career-navigator (13 stars, last pushed 11d ago), licensed Apache-2.0. It adds 66 tokens to every session and 1,042 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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