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 onfire7777/universal-ai-skills-library --skill ai-shaped-readiness-advisorgit clone --depth 1 https://github.com/onfire7777/universal-ai-skills-libraryWrote 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/onfire7777/universal-ai-skills-library/ai-shaped-readiness-advisor)<a href="https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/ai-shaped-readiness-advisor"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/ai-shaped-readiness-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/onfire7777/universal-ai-skills-library/ai-shaped-readiness-advisor"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/ai-shaped-readiness-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 | $0.00033 | $0.09771 |
| Opus 5.5 | $0.00013 | $0.03908 |
| Sonnet 5.5 | $0.00007 | $0.01954 |
| Haiku 4.5 | $0.00003 | $0.00977 |
Grade A, and why
ai-shaped-readiness-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 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.
This is a copy
98% identical to ai-shaped-readiness-advisor — 26 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 924 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Assess whether your product work is "AI-first" (using AI to automate existing tasks faster) or "AI-shaped" (fundamentally redesigning how product teams operate around AI capabilities). Use this to evaluate your readiness across 5 essential PM competencies for 2026, identify gaps, and get concrete recommendations on which capability to build first.
Key Distinction: AI-first is cute (using Copilot to write PRDs faster). AI-shaped is survival (building a durable "reality layer" that both humans and AI trust, orchestrating AI workflows, compressing learning cycles).
This is not about AI tools—it's about organizational redesign around AI as co-intelligence. The interactive skill guides you through a maturity assessment, then recommends your next move.
Key Concepts
AI-First vs. AI-Shaped
| Dimension | AI-First (Cute) | AI-Shaped (Survival) |
|---|---|---|
| Mindset | Automate existing tasks | Redesign how work gets done |
| Goal | Speed up artifact creation | Compress learning cycles |
| AI Role | Task assistant | Strategic co-intelligence |
| Advantage | Temporary efficiency gains | Defensible competitive moat |
| Example | "Copilot writes PRDs 2x faster" | "AI agent validates hypotheses in 48 hours instead of 3 weeks" |
Critical Insight: If a competitor can replicate your AI usage by throwing bodies at it, it's not differentiation—it's just efficiency (which becomes table stakes within months).
The 5 Essential PM Competencies (2026)
These competencies define AI-shaped product work. You'll assess your maturity on each.
1. Context Design
Building a durable "reality layer" that both humans and AI can trust—treating AI attention as a scarce resource and allocating it deliberately.
What it includes:
- Documenting what's true vs. assumed
- Immutable constraints (technical, regulatory, strategic)
- Operational glossary (shared definitions)
- Evidence standards (what counts as validation)
- Context boundaries (what to persist vs. retrieve)
- Memory architecture (short-term conversational + long-term persistent)
- Retrieval strategies (semantic search, contextual retrieval)
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 · 924 lines · 33 tokens per session scan A 874d5be16d63
ai-shaped-readiness-advisor is a skill published in the GitHub repository onfire7777/universal-ai-skills-library (16 stars, last pushed 1mo ago), licensed MIT. It adds 33 tokens to every session and 9,771 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 98% identical to ai-shaped-readiness-advisor, differing in 26 lines, and is treated as a copy.
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