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 adimango/ai-adoption-playbook --skill reporting-readiness-assessmentgit clone --depth 1 https://github.com/adimango/ai-adoption-playbookWrote 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/adimango/ai-adoption-playbook/reporting-readiness-assessment)<a href="https://agentmods.dev/skills/adimango/ai-adoption-playbook/reporting-readiness-assessment"><img src="https://agentmods.dev/badge/skills/adimango/ai-adoption-playbook/reporting-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/adimango/ai-adoption-playbook/reporting-readiness-assessment"><img src="https://agentmods.dev/badge/skills/adimango/ai-adoption-playbook/reporting-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 | $0.00055 | $0.05084 |
| Opus 5 | $0.00028 | $0.02542 |
| Sonnet 5 | $0.00011 | $0.01017 |
| Haiku 4.5 | $0.00006 | $0.00508 |
Grade A, and why
reporting-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 3d 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 — 362 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Reporting Readiness Assessment
Purpose
Quick diagnostic that scores an organization's AI reporting readiness across three pillars: outcome rigor, risk posture, and board defensibility. Produces a one-page scorecard showing whether AI investment claims would survive CFO review, external audit, or board scrutiny.
This skill is Stage 2 of the playbook. It runs AFTER fluency-assessment for organizations that have moved past adoption (Integration score ≥ 3/5). A team that hasn't adopted AI yet doesn't need this skill — it needs blocker-diagnosis or first-use-case-picker first.
Core principle: Diagnose before you prescribe. This skill assesses reporting maturity — it does NOT build methodology, draft narratives, or calculate ROI. Those are separate skills (roi-calculator, board-ai-update).
Audience: Founders, CTOs, CAIOs, VPs of Engineering, and Heads of AI past the pilot stage who now face "how do you know it's working?" from a CFO, board, or investor.
Time: Under 5 minutes for the quick quiz. Optional deep-dive available if the leader wants to go deeper on a specific pillar.
Flow
digraph reporting {
"Pre-check (fluency score)" [shape=diamond];
"Route to fluency-assessment" [shape=box];
"Context (2 Qs)" [shape=box];
"Quick Quiz (9 Qs)" [shape=box];
"Score & produce scorecard" [shape=box];
"Want deep-dive?" [shape=diamond];
"Deep-dive on weakest pillar" [shape=box];
"Route to next skill" [shape=doublecircle];
"Pre-check (fluency score)" -> "Route to fluency-assessment" [label="not run or Integration < 3/5"];
"Pre-check (fluency score)" -> "Context (2 Qs)" [label="Integration ≥ 3/5"];
"Context (2 Qs)" -> "Quick Quiz (9 Qs)";
"Quick Quiz (9 Qs)" -> "Score & produce scorecard";
"Score & produce scorecard" -> "Want deep-dive?";
"Want deep-dive?" -> "Deep-dive on weakest pillar" [label="yes, have time"];
"Want deep-dive?" -> "Route to next skill" [label="no, move on"];
"Deep-dive on weakest pillar" -> "Route to next skill";
}
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.
- 3d ago Changed · +3 lines 1005d10d84b4
- 11d ago First seen · 359 lines · 55 tokens per session scan A 7981b8c99471
reporting-readiness-assessment is a skill published in the GitHub repository adimango/ai-adoption-playbook (23 stars, last pushed 5d ago), licensed MIT. It adds 55 tokens to every session and 5,084 once invoked, about $0.0003 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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