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/blocker-diagnosisWrote 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/blocker-diagnosis)<a href="https://agentmods.dev/skills/adimango/ai-adoption-playbook/blocker-diagnosis"><img src="https://agentmods.dev/badge/skills/adimango/ai-adoption-playbook/blocker-diagnosis/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/blocker-diagnosis"><img src="https://agentmods.dev/badge/skills/adimango/ai-adoption-playbook/blocker-diagnosis.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 | $0.00037 | $0.04125 |
| Opus 5 | $0.00018 | $0.02063 |
| Sonnet 5 | $0.00007 | $0.00825 |
| Haiku 4.5 | $0.00004 | $0.00413 |
Grade A, and why
blocker-diagnosis 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 — 303 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Blocker Diagnosis
Purpose
Deep-dive diagnostic that identifies the specific blockers preventing AI adoption, maps each to a pillar, and distinguishes surface complaints from root causes. Chains from fluency-assessment when scores are low. Produces a structured blocker report — not an action plan.
Core principle: Name the real blocker, not the excuse. "It doesn't work" is never the real answer. This skill digs until it finds what's actually stuck.
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.
Flow
digraph blocker {
"Review fluency scorecard" [shape=box];
"Pick lowest pillar" [shape=box];
"Deep-dive questions (4-6 Qs)" [shape=box];
"Surface vs root cause?" [shape=diamond];
"Probe deeper" [shape=box];
"Log blocker" [shape=box];
"More pillars to probe?" [shape=diamond];
"Next pillar" [shape=box];
"Produce blocker report" [shape=box];
"Route to next skill" [shape=doublecircle];
"Review fluency scorecard" -> "Pick lowest pillar";
"Pick lowest pillar" -> "Deep-dive questions (4-6 Qs)";
"Deep-dive questions (4-6 Qs)" -> "Surface vs root cause?";
"Surface vs root cause?" -> "Probe deeper" [label="surface"];
"Surface vs root cause?" -> "Log blocker" [label="root cause found"];
"Probe deeper" -> "Deep-dive questions (4-6 Qs)";
"Log blocker" -> "More pillars to probe?";
"More pillars to probe?" -> "Next pillar" [label="yes"];
"More pillars to probe?" -> "Produce blocker report" [label="no"];
"Next pillar" -> "Deep-dive questions (4-6 Qs)";
"Produce blocker report" -> "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 beca6d84d785
- 11d ago First seen · 303 lines · 37 tokens per session scan A 5df052f76116
blocker-diagnosis is a skill published in the GitHub repository adimango/ai-adoption-playbook (23 stars, last pushed 4d ago), licensed MIT. It adds 37 tokens to every session and 4,125 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…