Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/adimango/ai-adoption-playbooknpx agentmods add skills/adimango/ai-adoption-playbook/board-ai-updateWrote 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/board-ai-update)<a href="https://agentmods.dev/skills/adimango/ai-adoption-playbook/board-ai-update"><img src="https://agentmods.dev/badge/skills/adimango/ai-adoption-playbook/board-ai-update/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/board-ai-update"><img src="https://agentmods.dev/badge/skills/adimango/ai-adoption-playbook/board-ai-update.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 | $0.00032 | $0.02506 |
| Opus 5 | $0.00016 | $0.01253 |
| Sonnet 5 | $0.00006 | $0.00501 |
| Haiku 4.5 | $0.00003 | $0.00251 |
Grade A, and why
board-ai-update 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 4d 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 — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Board AI Update
Purpose
Template for the AI section of a board update. Takes results data and produces a tight, number-filled narrative. This is the template — board-narrative-coach is the skill that rehearses and pressure-tests before drafting.
Core principle: Every paragraph has a number. No number, no paragraph.
Important: This skill helps leaders calculate and present their own numbers — it does not audit or guarantee them. Figures going to a board, CFO, or investor should be validated by the company's finance owner first.
Context Intake
Unfamiliar
~~categoryplaceholders? See CONNECTORS.md for connected-tool categories.
Accept the input artifact in any form: a file path, pasted text, an attachment, or output from a skill run earlier in this conversation. If ~~cloud storage is connected, offer to fetch it from there.
If no artifact is provided: this skill builds on the fluency scorecard — offer to run fluency-assessment first, or proceed with the leader's verbal answers, clearly marking the output as based on self-reported data.
For Department: and Currency:, use the first available source: the scorecard → adoption.local.md (the department this run covers; by default the one marked (primary) — see CLAUDE.md Local Configuration) → ask the leader (currency defaults to USD). If the config lists multiple departments or whole org and no scorecard pins this run to one, confirm which department (or org-wide/Generic) before producing numbers.
Process
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.
- 4d ago Changed · +39 lines b8a4866bc502
- 12d ago First seen · 160 lines · 32 tokens per session scan A a8104e3a33ba
board-ai-update is a skill published in the GitHub repository adimango/ai-adoption-playbook (23 stars, last pushed 5d ago), licensed MIT. It adds 32 tokens to every session and 2,506 once invoked, about $0.0002 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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