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 assessmentgit 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/assessment)<a href="https://agentmods.dev/skills/tmargolis/career-navigator/assessment"><img src="https://agentmods.dev/badge/skills/tmargolis/career-navigator/assessment/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/assessment"><img src="https://agentmods.dev/badge/skills/tmargolis/career-navigator/assessment.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 | $0.00800 |
| Opus 5.5 | $0.00014 | $0.00320 |
| Sonnet 5.5 | $0.00007 | $0.00160 |
| Haiku 4.5 | $0.00003 | $0.00080 |
Grade A, and why
assessment 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Invoke the honest-advisor agent to run a norm/exception/strategy assessment, then present the result as a gap analysis against the user's target role requirements.
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. If it has no target_roles (or equivalent target-role fields):
"I need your target role(s) to run an assessment. Run
/career-navigator:launch(or update yourCareerNavigator/profile.md)."
Read {user_dir}/CareerNavigator/ExperienceLibrary.json. If the ExperienceLibrary units array is empty:
"Your ExperienceLibrary is empty. Run
/career-navigator:add-sourceto add a resume first."
Optionally read {user_dir}/CareerNavigator/tracker.json to estimate how strong your evidence base is. Count resolved outcomes from the summary rows' outcome field — do not open any detail_file for this. If resolved outcomes are < 5, expect confidence to be Preliminary.
2. Invoke honest-advisor — assessment + gap analysis
For each target role in CareerNavigator/profile.md, hand off to the honest-advisor agent with:
target_role_typefor that roletarget_level(from profile if present; otherwise infer from role title)target_geography(from profile; if remote/national, treat as "Remote (national pool)")- Instruction: "Treat your
EXCEPTIONSas the concrete gaps vs. the target requirements inCareerNavigator/profile.md. Explicitly map each exception back to which requirement(s) are most likely under-covered (ATS relevance, narrative/seniority fit, or targeting)."
If multiple target roles exist, run the roles sequentially and label each section.
3. Present the gap analysis
Render the agent output as:
**Assessment (Gap Analysis)** — {Target Role}
Confidence: {Preliminary/Directional/Moderate/High} ({resolved_outcomes} resolved outcomes)
Target requirements (from profile):
- {requirement 1}
- {requirement 2}
Evidence-based gaps:
1. {exception headline + deviation direction}
- Impacts: {which requirements this likely undermines}
- Evidence: {cite tracker/artifacts/ExperienceLibrary evidence}
2. ...
Repositioning plan:
Immediate fixes:
- ...
Pipeline adjustments:
- ...
Compounding investment:
- ...
What would make this more reliable:
- ...
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 · 90 lines · 35 tokens per session scan A b674664f644a
assessment is a skill published in the GitHub repository tmargolis/career-navigator (16 stars, last pushed 27d ago), licensed Apache-2.0. It adds 35 tokens to every session and 800 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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