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/quarterly-reviewWrote 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/quarterly-review)<a href="https://agentmods.dev/skills/adimango/ai-adoption-playbook/quarterly-review"><img src="https://agentmods.dev/badge/skills/adimango/ai-adoption-playbook/quarterly-review/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/quarterly-review"><img src="https://agentmods.dev/badge/skills/adimango/ai-adoption-playbook/quarterly-review.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 | $0.00035 | $0.03269 |
| Opus 5 | $0.00017 | $0.01635 |
| Sonnet 5 | $0.00007 | $0.00654 |
| Haiku 4.5 | $0.00003 | $0.00327 |
Grade A, and why
quarterly-review 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 — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quarterly Review
Purpose
Re-runs the fluency assessment, compares to the previous scorecard, identifies what moved and what didn't, and produces the next board update. This is the ongoing cadence skill — run it every quarter to keep adoption on track and the board informed.
Core principle: Measure the delta, not just the current state. The board wants to see trajectory, not a snapshot.
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 previous scorecard is provided, check adoption.local.md for a Previous scorecards: entry matching the department this review covers, before falling back to the existing rule (no previous scorecard → run full-adoption-cycle instead). In multi-department setups, compare like with like — never diff this quarter's Engineering scorecard against last quarter's Sales one.
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.
Flow
digraph review {
"Retrieve previous artifacts" [shape=box];
"Confirm exposure register" [shape=box];
"Re-run fluency-assessment" [shape=box];
"Compare scorecards" [shape=box];
"Identify what moved, what didn't" [shape=box];
"Update blocker report" [shape=box];
"Produce comparison report" [shape=box];
"Draft board update" [shape=box];
"Route to next action" [shape=doublecircle];
"Retrieve previous artifacts" -> "Confirm exposure register";
"Confirm exposure register" -> "Re-run fluency-assessment";
"Re-run fluency-assessment" -> "Compare scorecards";
"Compare scorecards" -> "Identify what moved, what didn't";
"Identify what moved, what didn't" -> "Update blocker report";
"Update blocker report" -> "Produce comparison report";
"Produce comparison report" -> "Draft board update";
"Draft board update" -> "Route to next action";
}
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 · +24 lines 63172703678f
- 12d ago First seen · 219 lines · 35 tokens per session scan A 6cbd2a498622
quarterly-review is a skill published in the GitHub repository adimango/ai-adoption-playbook (23 stars, last pushed 5d ago), licensed MIT. It adds 35 tokens to every session and 3,269 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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