quarterly-review

quarterly-review is a skill for Claude Code from adimango/ai-adoption-playbook. It costs 35 tokens per session (3,269 once invoked), scanned A, original, MIT.

A recurring review process for measuring AI adoption progress against the previous quarter and preparing the next board update.

In plain words
What is it for?
Use it each quarter to rerun the assessment, compare matching departments and scorecards, identify improvements or unchanged areas, and prepare the next board update.
Why use it?
It focuses on what changed over time, so leaders can show direction and progress instead of presenting only a single snapshot.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: mentions CLAUDE.md.

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is Unfamiliar `~~category` placeholders? See [CONNECTORS.md](../../CONNECTORS.md) for connected-tool categories..

Part of the ai-adoption-playbook plugin — 15 skills, 9 MCP servers shipped together

Good fit Use it each quarter to rerun the assessment, compare matching departments and scorecards, identify improvements or unchanged areas, and prepare the next board update.

Compare 6 skills from other repositories ↓
Install

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.

Clone the repo
git clone --depth 1 https://github.com/adimango/ai-adoption-playbook
agentmods
npx agentmods add skills/adimango/ai-adoption-playbook/quarterly-review

Made for: Claude Code.

Or install ai-adoption-playbook, the plugin that ships this one along with the rest of its 15 skills, 9 MCP servers.

Wrote 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.

agentmods badge for quarterly-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/adimango/ai-adoption-playbook/quarterly-review/github.svg)](https://agentmods.dev/skills/adimango/ai-adoption-playbook/quarterly-review)
Your own site
<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.

agentmods 80×15 button for quarterly-review

Your own site · 80×15
<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>
Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,269 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 4d ago against content hash 63172703678f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

skills/quarterly-review/SKILL.md · 243 lines

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 ~~category placeholders? 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";
}

Read the full file on GitHub · 243 lines

Changes

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.

  1. 4d ago Changed · +24 lines 63172703678f
  2. 12d ago First seen · 219 lines · 35 tokens per session scan A 6cbd2a498622

Subscribe to this mod's changes

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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