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 8Dionysus/aoa-skills --skill aoa-verificationgit clone --depth 1 https://github.com/8Dionysus/aoa-skillsWrote 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/8dionysus/aoa-skills/aoa-verification)<a href="https://agentmods.dev/skills/8dionysus/aoa-skills/aoa-verification"><img src="https://agentmods.dev/badge/skills/8dionysus/aoa-skills/aoa-verification/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/8dionysus/aoa-skills/aoa-verification"><img src="https://agentmods.dev/badge/skills/8dionysus/aoa-skills/aoa-verification.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 | $0.00086 | $0.00856 |
| Opus 5 | $0.00043 | $0.00428 |
| Sonnet 5 | $0.00017 | $0.00171 |
| Haiku 4.5 | $0.00009 | $0.00086 |
Grade A, and why
aoa-verification 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 11d 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
aoa-verification
Intent
Start from owner meaning and observed behavior, then choose the smallest evidence form whose green result has an explicit claim limit.
Trigger boundary
Use this skill when:
- a stable producer-consumer seam, an important invariant, or a property across many inputs/states needs evidence that can reveal a meaningful break
Do not use this skill when:
- behavior or ownership is undefined, the change is private, the request is a generic test strategy, or automation would exist only to produce green status
Inputs
- owner rule, system/boundary under review, named consumers where relevant
- manual cases or failures, current checks, and available oracle
Outputs
- exactly one typed mode result: evidence package, gaps, smallest authorized durable check or no-check decision, claim limit, and termination
Procedure
-
Read
references/contract.yamland choose exactly one mode:Mode Select when Required procedure contractA named consumer relies on a stable ABI, receipt, schema, tool result, or handoff. references/contract.mdcoverage-auditChecks exist and the question is what stable invariant they truly constrain. references/coverage-audit.mdpropertyCorrectness must hold across many inputs or states. references/property.md -
Read the selected reference completely. Do not load unrelated mode procedures.
-
Read the authoritative owner rule before the subject implementation, checks, examples, or generated views. Use exact supplied paths directly; search only for a missing required input, not for ritual workspace orientation. Exercise expected, rejected, and motivating failure cases manually and state the oracle. Do not collect repository-wide inventories, hashes, or status unless the claim or effect boundary needs them.
-
When the exact evaluation surface is unknown or must be selected/applied, use a task-local DAG:
aoa-eval.select -> aoa-eval.apply ->the chosen verification mode. A named check may be run directly as evidence inside a verification task; cross-surface discovery and application remainaoa-evalresponsibilities. -
Create durable automation only after manual evidence establishes a repeated or owner-declared long-lived rule and the active task authorizes the write. Remove session-only probes after learning.
What ships with it
5 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.
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.
- 11d ago First seen · 91 lines · 86 tokens per session scan A 77575089f4e3
aoa-verification is a skill published in the GitHub repository 8Dionysus/aoa-skills (4 stars, last pushed 3d ago), licensed Apache-2.0. It adds 86 tokens to every session and 856 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
pptx-html-fidelity-audit
Audit a python-pptx export against its source HTML deck, identify layout/content drift (footer overflow, cropped content, missing italic/em, lost styling, off-rhythm spacing), and re-export with strict footer-rail + cursor-flow layout discipline. Use this skill whenever the user has a .pptx that was generated from an…
build-test
Run the project's build / typecheck / lint / test commands and emit the build.passing + tests.passing signals devloop convergence reads.
nemo-automodel-model-onboarding
Guide for onboarding new model architectures into NeMo AutoModel, including architecture discovery, implementation patterns, registration, and validation.
amc-run-sample-calibration
Run end-to-end calibration on the shipped sample dataset (sdg082sampledata010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.
jetson-video-pipeline
Use when executing and verifying Jetson Video Codec SDK or PyNvVideoCodec encode/decode, transcode, segmentation, container decode, AV1, or acceptance workflows with exact artifact handoffs.
nemo-automodel-recipe-development
Create and modify NeMo AutoModel training and evaluation recipes, including YAML structure, builders, and execution flow.