Borrowing it
Nothing to install: this file belongs to Smart-AI-Memory/attune-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Smart-AI-Memory/attune-ai/main/.agents/skills/verify/SKILL.mdgit clone --depth 1 https://github.com/Smart-AI-Memory/attune-aiWrote 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/smart-ai-memory/attune-ai/verify)<a href="https://agentmods.dev/skills/smart-ai-memory/attune-ai/verify"><img src="https://agentmods.dev/badge/skills/smart-ai-memory/attune-ai/verify.svg" alt="Measured on agentmods" 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 | $0.00054 | $0.01369 |
| Opus 5 | $0.00027 | $0.00685 |
| Sonnet 5 | $0.00011 | $0.00274 |
| Haiku 4.5 | $0.00005 | $0.00137 |
Grade A, and why
verify 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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Verify
IMPORTANT: Start your response by telling the user:
Verify — Fact-checking the named entities in this content against the project as source-of-truth.
What It Does
/verify is the output-side fact-checker for LLM-generated content
(docs, READMEs, tutorials). It catches the hallucination classes that
unit tests never see — an invented CLI flag, an import of a package
that doesn't exist, a dead cross-reference, a wrong count — by
confirming the named entities in the content actually exist.
It runs in two layers:
- Deterministic checkers (
attune_verify, authoritative): the top-level package of each import resolves viafind_spec, CLI flags appear in--help, markdown links resolve under the project root, numeric claims match a declared count source. These are ground truth — no LLM, no guessing. - Ambient semantic cross-check (you, the agent): after the
deterministic pass, read the content against its source and flag
claims the checkers can't see (a security caveat dropped, a route
path that's plausible-but-wrong, a private submodule of an
installed package that doesn't actually exist — the import checker
only validates the top-level package, so
from pkg.fake_sub import Xpasses deterministically whenpkgis installed). This layer is a cross-check only — it over-flags on truncated context, so never let it override a deterministic pass. If the deterministic layer says a top-level import resolves, it resolves.
How To Run It
Shared command workspace (preferred)
Open adapter verify with the validated generated-content path and optional
hard_gate=true for pipeline callers. The invocation authorizes this
read-only run, so there is no confirmation action. Execute
attune_verify.verify exactly as below and publish its authoritative outcome
as deterministic_result, retaining every finding's kind, severity, detail,
evidence, and location.
Perform the ambient semantic pass only after the deterministic result and
publish it as cross_check_result. Ambient findings remain explicitly labeled
warnings and cannot override deterministic entity-existence results. In hard
gate mode, any deterministic error keeps hard_gate_passed=false. Checker or
cross-check failure must render “did not complete,” never a clean report.
Present the terminal widget or Markdown and preserve the full evidence chain
in text fallback.
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 · +17 lines 271d623eb3dc
- 7d ago First seen · 122 lines · 54 tokens per session scan A 88040a629c8a
verify is a skill published in the GitHub repository Smart-AI-Memory/attune-ai (10 stars, last pushed today), licensed Apache-2.0. It adds 54 tokens to every session and 1,369 once invoked, about $0.0003 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
verify
Verify recent code changes work end-to-end by building, running tests, and exercising affected features. Use when the user asks to verify changes, requests "/verify", or wants confirmation a feature actually works.
taiyi-test
A project workflow skill for verifying an implementation and producing a TEST.md record. TDD means writing a failing test, implementing the change, and then making the test pass; this skill checks that process and other regression cases.
reproducibility-validate
Run a workflow multiple times and compare outputs to produce a similarity score and pass/fail verdict.
eval-workflow
Run evaluation tests against a multi-agent workflow to assess orchestration quality and failure archetype resistance.
execute-feedback
Execute tests on generated code and iterate until passing.
misata
Generate realistic multi-table test data, seed a development database, or build fixtures whose joins and totals actually hold. Use when the user needs test data, sample data, demo data, seed data, fixtures, a populated dev/staging database, or a relational dataset shaped to specific numbers (a revenue curve, a churn…