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 aws/tools-for-devops-agent --skill aiml-access-diagnosticsgit clone --depth 1 https://github.com/aws/tools-for-devops-agentWrote 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/aws/tools-for-devops-agent/aiml-access-diagnostics)<a href="https://agentmods.dev/skills/aws/tools-for-devops-agent/aiml-access-diagnostics"><img src="https://agentmods.dev/badge/skills/aws/tools-for-devops-agent/aiml-access-diagnostics/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/aws/tools-for-devops-agent/aiml-access-diagnostics"><img src="https://agentmods.dev/badge/skills/aws/tools-for-devops-agent/aiml-access-diagnostics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00229 | $0.04398 |
| Opus 5 | $0.00114 | $0.02199 |
| Sonnet 5 | $0.00046 | $0.00880 |
| Haiku 4.5 | $0.00023 | $0.00440 |
Grade A, and why
aiml-access-diagnostics 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 7d 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 — 314 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI/ML Access Diagnostics
Diagnose why an AI/ML service call was denied. Walk the authorization chain hop by hop, name the hop that denied the call, and propose a scoped IAM policy for human review. Read-only throughout.
Checklist
Work through these steps in order. Each is detailed in its own section below.
- Step 1 — Classify the request: confirm the service is Bedrock or SageMaker, and that there is an observed failure (not a speculative audit). Stop otherwise.
- Step 2 — Establish identity and scope: record the agent's own identity, extract the principal/action/resource ARNs, and flag cross-account.
- Step 3 — Collect evidence, policy reads first: read the chain's policy documents by hand; use CloudTrail and simulation only as corroboration.
- Step 4 — Walk the chain: traverse the six hops in precedence order; do not stop at hop 1 just because it passed.
- Step 5 — Apply service-specific knowledge: rule out non-IAM denial causes for the service explicitly.
- Step 6 — Assign verdicts: give every hop exactly one token from the closed verdict set.
- Step 7 — Propose a policy: derive a scoped policy for human review; keep observed and commonly-required permissions labelled separately.
- Step 8 — Deliver the report: render per the report format, run the pre-render validation, then deliver.
Output Discipline
The report is the deliverable. Conversation around it is not.
- Do not narrate API calls. No per-call summaries, no interim results, no raw response extracts. A full diagnosis makes many reads; announcing each one buries the finding.
- Do not narrate plans or reasoning. No "Let me check...", "I'll now look at...", "Given the chain, I should...". Execute the step and move on.
- Do not echo raw API responses. Process them silently. Policy documents in particular are long, and pasting them displaces the diagnosis.
- Keep interstitial messages to one line. Speak between steps only at real milestones: starting, asking the user something, delivering, or erroring.
- Do not summarize after delivering. The report already contains the summary; restating it invites a shortened paraphrase to be read instead of the report.
- Never assess your own performance. Do not append a paragraph saying the diagnosis worked, was correct, handled a hard case, or caught something subtle. The reader evaluates the report; the report does not evaluate itself. Self-congratulation also lends unearned confidence to findings whose limitations the report has just carefully enumerated.
- Nothing follows the report except, at most, a single line offering a next action — saving an artifact, or running another failure. No recap, no restatement of the root cause, no commentary on the diagnosis.
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- .skilleval.yaml 77 B
- CHANGELOG.md 14 KB
- evals/eval_queries.json 4.2 KB
- evals/evals.json 9.7 KB
- README.md 12 KB
- references/access-chain-model.md 7.0 KB
- references/data-collection.md 19 KB
- references/finding-logic.md 21 KB
- references/report-format.md 13 KB
- references/svc-bedrock.md 12 KB
- references/svc-sagemaker.md 12 KB
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.
- 7d ago First seen · 314 lines · 229 tokens per session scan A fc7ad81dada0
aiml-access-diagnostics is a skill published in the GitHub repository aws/tools-for-devops-agent (50 stars, last pushed today), licensed Apache-2.0. It adds 229 tokens to every session and 4,398 once invoked, about $0.0011 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 2026-09-04.
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