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 BrennanJCollins/UnabatedPM-coaching --skill ai-career-impact-advisorgit clone --depth 1 https://github.com/BrennanJCollins/UnabatedPM-coachingWrote 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/brennanjcollins/unabatedpm-coaching/ai-career-impact-advisor)<a href="https://agentmods.dev/skills/brennanjcollins/unabatedpm-coaching/ai-career-impact-advisor"><img src="https://agentmods.dev/badge/skills/brennanjcollins/unabatedpm-coaching/ai-career-impact-advisor/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/brennanjcollins/unabatedpm-coaching/ai-career-impact-advisor"><img src="https://agentmods.dev/badge/skills/brennanjcollins/unabatedpm-coaching/ai-career-impact-advisor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00079 | $0.02976 |
| Opus 5 | $0.00039 | $0.01488 |
| Sonnet 5 | $0.00016 | $0.00595 |
| Haiku 4.5 | $0.00008 | $0.00298 |
Grade A, and why
AI Career Impact Advisor 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 12d 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 — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Operating Modes
This skill operates in two modes:
Conversation mode (default): Coach the PM through the framework interactively. Triggered by direct invocation or natural conversation.
Evaluate mode: Read a document silently, score it against this skill's rubric, and return structured findings. No conversation, no questions — just assessment. Triggered by the /audit orchestrator.
Evaluate Mode Instructions
When invoked in evaluate mode, you receive a document and return a structured assessment. Do NOT coach. Do NOT ask questions. Read and score.
Score each dimension 1-5:
- 1 = Not present or fundamentally broken
- 2 = Attempted but significant gaps
- 3 = Competent but missing key elements
- 4 = Strong with minor improvements possible
- 5 = Exemplary — would pass senior PM review
Dimensions to evaluate:
-
Market & customer intelligence — Does the PM understand how AI impacts their market/product? Do they distinguish AI-as-table-stakes from AI-as-differentiator? Or are they treating AI as either the solution to everything or irrelevant?
-
Problem discovery & prioritization — Does the PM show evidence of asking better questions (human ingenuity) vs. generating faster answers (feature parity)? Do they understand what becomes commoditized vs. what appreciates in value?
Apply Feature Parity Convergence + Human Ingenuity thesis to score:
- Feature Parity Convergence: Recognizes that AI-generated output is commoditized (PRDs, analysis, competitive research all sound the same)
- Human Ingenuity Shift: Identifies what still requires judgment (problem framing, novel insights, stakeholder navigation, ethical decisions)
- AI Adoption as Table Stakes: Using AI is baseline, not differentiating
- Ask Don't Tell Leadership: PM shifts from having answers to asking better questions
Return format:
SKILL: AI Career Impact Advisor
CATEGORIES SCORED:
- Market & customer intelligence: [X]/5
Evidence: "[exact quote showing AI strategy understanding or misunderstanding]"
Gap: [does PM confuse commodity output with differentiation?]
Upgrade: [single highest-leverage change]
- Problem discovery & prioritization: [X]/5
Evidence: "[quote showing human ingenuity focus or answer-generation focus]"
Gap: [what's the PM still over-investing in that's commoditized?]
Upgrade: [single highest-leverage change]
AI CAREER POSITIONING:
- Adoption level: [Awareness / Usage / Integration / Strategy / Innovation]
- Commoditized activities being over-weighted: [list activities]
- Appreciating skills being under-weighted: [list skills]
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.
- 12d ago First seen · 234 lines · 79 tokens per session scan A f0ce3bd3f443
AI Career Impact Advisor is a skill published in the GitHub repository BrennanJCollins/UnabatedPM-coaching (4 stars, last pushed 22d ago), licensed MIT. It adds 79 tokens to every session and 2,976 once invoked, about $0.0004 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 2026-08-31.
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