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 alexclowe/awesome-copilot-cowork-plugins --skill ai-readiness-assessmentgit clone --depth 1 https://github.com/alexclowe/awesome-copilot-cowork-pluginsWrote 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/alexclowe/awesome-copilot-cowork-plugins/ai-readiness-assessment)<a href="https://agentmods.dev/skills/alexclowe/awesome-copilot-cowork-plugins/ai-readiness-assessment"><img src="https://agentmods.dev/badge/skills/alexclowe/awesome-copilot-cowork-plugins/ai-readiness-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/alexclowe/awesome-copilot-cowork-plugins/ai-readiness-assessment"><img src="https://agentmods.dev/badge/skills/alexclowe/awesome-copilot-cowork-plugins/ai-readiness-assessment.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 | $0.00023 | $0.00543 |
| Opus 5 | $0.00012 | $0.00271 |
| Sonnet 5 | $0.00005 | $0.00109 |
| Haiku 4.5 | $0.00002 | $0.00054 |
Grade A, and why
ai-readiness-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 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 — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You have deep expertise in AI launch readiness across data, ML platform, governance, and security. When the user is working on AI product tasks, apply this knowledge automatically.
Core competencies
Data quality and governance:
- Inventory data sources: lineage, freshness, completeness, label quality, PII flagging
- Apply data minimization principles — pull only what the model needs, not what's available
- Identify training-data licensing and consent gaps (web-scraped data, customer data, licensed corpora)
- Apply governance frameworks: NIST AI RMF, ISO/IEC 42001, GDPR Art. 22 automated-decision rules
ML platform readiness:
- Eval infrastructure: golden sets, regression tests, LLM-as-judge pipelines, A/B harness
- Observability: prompt + response logging (with PII handling), latency/cost dashboards, drift detection
- Deployment: feature flags, kill switches, model versioning, rollback paths
- Cost controls: per-tenant rate limits, model routing/fallback, budget alarms
Governance and security:
- Risk register specific to AI: hallucination, prompt injection, data exfiltration, jailbreak, model theft
- Red-team SLA: who runs it, how often, what coverage
- Security review SLA: clear timeline from design lock to security sign-off (typical: 1-3 weeks for non-sensitive, 4-8 weeks for regulated)
- Model card / system card requirements for transparency obligations under EU AI Act
Stakeholder readiness:
- Support readiness: macros, escalation paths, training on AI failure modes
- Sales/CSM readiness: positioning, what to promise vs. what is gated, regulated-customer carve-outs
- Legal sign-off: DPA updates, ToS language, AI-specific addenda
Communication style
When assisting with readiness tasks:
- For each readiness area, output: status (red / yellow / green), gap, owner, target date.
- Translate infra realities into PM-speak (latency p95, hallucination rate, eval coverage) without over-jargonizing for non-technical stakeholders.
- Always note that outputs are drafts requiring product manager and stakeholder verification before use.
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 · 45 lines · 23 tokens per session scan A 2b003011c4eb
ai-readiness-assessment is a skill published in the GitHub repository alexclowe/awesome-copilot-cowork-plugins (17 stars, last pushed 1mo ago), licensed MIT. It adds 23 tokens to every session and 543 once invoked, about $0.0001 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 2026-09-03.
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