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 training-roigit 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/training-roi)<a href="https://agentmods.dev/skills/tmargolis/career-navigator/training-roi"><img src="https://agentmods.dev/badge/skills/tmargolis/career-navigator/training-roi/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/training-roi"><img src="https://agentmods.dev/badge/skills/tmargolis/career-navigator/training-roi.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 | $0.00784 |
| Opus 5 | $0.00022 | $0.00392 |
| Sonnet 5 | $0.00009 | $0.00157 |
| Haiku 4.5 | $0.00004 | $0.00078 |
Grade A, and why
training-roi 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 9d 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Invoke honest-advisor in training-roi mode to compare learning options and recommend the highest-ROI path for the user's target role.
Workflow
1. Confirm baseline context
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 target roles are missing:
"I need your target role(s) to run a training ROI analysis. Run
/career-navigator:launch(or updateCareerNavigator/profile.md) first."
If ExperienceLibrary units are missing/empty:
"I need your ExperienceLibrary to estimate learning ROI. Run
/career-navigator:add-sourceto add a resume first."
Optionally read {user_dir}/CareerNavigator/tracker.json for confidence and bottleneck context. The summary rows carry everything this skill needs — status, outcome, latest_stage, and latest_stage_date show where applications stall, so do not open any detail_file.
2. Gather optional constraints
If not already provided in conversation, ask once:
"Do you want me to optimize for a specific budget and timeline? If yes, share your max budget and ideal timeline (for example: '$3k and 4 months')."
Proceed even if user does not provide constraints.
3. Invoke honest-advisor — training-roi mode
For each target role (or the explicitly requested role), hand off to honest-advisor with:
analysis_mode: training-roitarget_role_typetarget_level(if known/inferred)target_geographytime_horizon_months(if provided; else default 12)budget_range(if provided)
Instruction:
- Compare certifications, degrees, bootcamps, and self-study.
- Produce a cost-benefit-time option matrix with ROI score.
- Recommend a primary path and fallback path.
- Include explicit assumptions and data gaps.
4. Present the recommendation engine output
Render as:
**Training ROI Analysis** — {Target Role}
Time horizon: {n months}
Budget: {value or "not provided"}
Confidence: {Preliminary / Directional / Moderate / High}
Top capability gaps
1. ...
2. ...
Option matrix (cost-benefit-time)
| Path | Type | Estimated cost | Estimated time | Signal strength | ROI score | Key risks |
|---|---|---|---|---|---:|---|
| ... |
Recommended plan
- Primary path: ...
- Fallback path: ...
- First 30 days: ...
- Proof artifacts to create: ...
Assumptions and data gaps
- ...
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.
- 9d ago First seen · 98 lines · 43 tokens per session scan A b94fe3b8950c
training-roi is a skill published in the GitHub repository tmargolis/career-navigator (13 stars, last pushed 10d ago), licensed Apache-2.0. It adds 43 tokens to every session and 784 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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