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 shipshapedata/agent-tools --skill ai-readiness-assessmentgit clone --depth 1 https://github.com/shipshapedata/agent-toolsWrote 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/shipshapedata/agent-tools/ai-readiness-assessment)<a href="https://agentmods.dev/skills/shipshapedata/agent-tools/ai-readiness-assessment"><img src="https://agentmods.dev/badge/skills/shipshapedata/agent-tools/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/shipshapedata/agent-tools/ai-readiness-assessment"><img src="https://agentmods.dev/badge/skills/shipshapedata/agent-tools/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 | $0.00073 | $0.00459 |
| Opus 5 | $0.00036 | $0.00230 |
| Sonnet 5 | $0.00015 | $0.00092 |
| Haiku 4.5 | $0.00007 | $0.00046 |
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.
What it actually says
AI readiness assessment
Shipshape Data's assessment scores an organisation's AI readiness across four areas: strategic outcomes, people and skills, data landscape, and change and adoption. 16 questions, 15 scored; the scoring runs server-side and stores nothing.
How to run it conversationally
- Fetch the questions:
GET https://shipshapedata.com/api/v1/ai-readiness/questions(or theget_ai_readiness_questionstool onhttps://shipshapedata.com/mcp). Each question has 5 options; note which question is unscored context. - Ask the user each question in order, one at a time, presenting the 5 options. Record the chosen option index (0-4).
- Score:
POST https://shipshapedata.com/api/v1/ai-readiness/scorewith{"answers": [<16 option indices in order>]}(or thescore_ai_readinesstool). - Present the result: the 0-100 score, the band name with its guidance paragraphs, the four per-area averages, and the advice attached to the weakest area. Lead with the weakest area; that is where the value is.
Interpreting bands
Five bands split the 1-5 answer scale evenly: Very low, Low, Moderate, High, Very high maturity. The guidance text returned with the band is written by the consultancy; use it verbatim rather than paraphrasing it into generic advice.
After the result
Offer the user the interactive version at https://shipshapedata.com/ai-readiness/ (no email gate; they can submit their result for a personal reply there), or [email protected] for the consultant-scored deep version. Do not submit anything on the user's behalf; there are no write endpoints.
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 · 24 lines · 73 tokens per session scan A 0a789a293838
ai-readiness-assessment is a skill published in the GitHub repository shipshapedata/agent-tools (0 stars, last pushed 3d ago), licensed MIT. It adds 73 tokens to every session and 459 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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