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 suggest-rolesgit 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/suggest-roles)<a href="https://agentmods.dev/skills/tmargolis/career-navigator/suggest-roles"><img src="https://agentmods.dev/badge/skills/tmargolis/career-navigator/suggest-roles/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/suggest-roles"><img src="https://agentmods.dev/badge/skills/tmargolis/career-navigator/suggest-roles.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 | $0.00043 | $0.01011 |
| Opus 5 | $0.00022 | $0.00505 |
| Sonnet 5 | $0.00009 | $0.00202 |
| Haiku 4.5 | $0.00004 | $0.00101 |
Grade A, and why
suggest-roles 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Invoke both honest-advisor and market-researcher to generate role suggestions and write actionable signals that improve job-scout ranking.
Important invocation rule:
- Use the exact agent names
honest-advisorandmarket-researcher. - Do not invent or alias agent types (for example, do not call "career-assessment" or "market-analysis" agent types).
- If either agent invocation fails, retry once using the exact names above before returning an error.
Workflow
1. Confirm data exists
Application data uses the split layout defined in references/tracker-schema.md — read it before any read or write.
Read:
{user_dir}/CareerNavigator/profile.md{user_dir}/CareerNavigator/ExperienceLibrary.json
If profile has no target roles:
"I need your current target role(s) first. Run
/career-navigator:launchor updateCareerNavigator/profile.mdbefore running role suggestions."
If ExperienceLibrary units is empty:
"Your ExperienceLibrary is empty. Run
/career-navigator:add-sourceto add a resume before role suggestions."
Optionally read {user_dir}/CareerNavigator/tracker.json for confidence and outcome context. Summary rows answer this — outcome, latest_stage, and latest_stage_date per row are enough; do not open any detail_file.
2. Run advisor pass (competitiveness + transferable fit)
Invoke honest-advisor in assessment mode for the user's primary target role. Ask it to:
- identify under-covered requirements
- identify nearby role variants where the user's strongest signals are more competitive
- return 3-6 candidate roles with rationale
3. Run market pass (demand + displacement + geography)
Invoke market-researcher for the same role set and geography. Ask it to:
- classify demand posture for each candidate role (rising/stable/softening)
- flag displacement risk posture
- identify geography-specific competitiveness constraints/opportunities
4. Synthesize suggestions
Combine both outputs into a ranked role list:
- prioritize roles where transferable fit is strong and demand posture is favorable
- down-rank roles with weak fit, softening demand, or severe geography mismatch
- include at least one "stretch but plausible" option if evidence supports it
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 · 117 lines · 43 tokens per session scan A 1e91afd22e6f
suggest-roles is a skill published in the GitHub repository tmargolis/career-navigator (13 stars, last pushed 11d ago), licensed Apache-2.0. It adds 43 tokens to every session and 1,011 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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