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 itallstartedwithaidea/agent-skills --skill verification-loopsgit clone --depth 1 https://github.com/itallstartedwithaidea/agent-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/itallstartedwithaidea/agent-skills/verification-loops)<a href="https://agentmods.dev/skills/itallstartedwithaidea/agent-skills/verification-loops"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/verification-loops/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/itallstartedwithaidea/agent-skills/verification-loops"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/verification-loops.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 | $0.00021 | $0.02187 |
| Opus 5.5 | $0.00008 | $0.00875 |
| Sonnet 5.5 | $0.00004 | $0.00437 |
| Haiku 4.5 | $0.00002 | $0.00219 |
Grade A, and why
verification-loops 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 1mo 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 — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Verification Loops
Part of Agent Skills™ by googleadsagent.ai™
Description
Verification Loops are systematic evaluation pipelines that validate agent outputs at every stage of execution. The fundamental challenge of autonomous agents is trust — how do you know the agent did the right thing? Verification Loops solve this by embedding checkpoint evaluations, continuous assertions, and multi-stage review gates throughout the agent's execution pipeline. This skill draws from the evaluation methodology used in production at googleadsagent.ai™, where Buddy™ verifies every Google Ads recommendation against historical data, budget constraints, and domain rules before surfacing it to users.
The distinction between checkpoint and continuous verification is critical. Checkpoint verification evaluates outputs at defined stage boundaries (pre-commit, post-analysis, before-deploy). Continuous verification runs assertions in real-time during generation, catching drift and hallucination before they propagate. Both approaches are complemented by pass@k metrics — generating multiple candidate outputs and selecting the best one based on grader consensus.
Production verification systems employ typed graders: deterministic graders for schema and constraint validation, LLM-as-judge graders for semantic quality assessment, and human-in-the-loop graders for high-stakes decisions. The combination creates a layered verification net that catches errors at the earliest and cheapest point in the pipeline.
Use When
- Agent outputs directly influence business decisions or user-facing content
- Regulatory or compliance requirements demand audit trails for AI-generated content
- Multi-step workflows need quality gates between stages
- You need to measure and improve agent accuracy over time (pass@k benchmarking)
- Generated code must pass tests before being committed or deployed
- Analysis results must be validated against ground truth or business rules
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.
- 1mo ago First seen · 213 lines · 21 tokens per session scan A 2c8b7d80d637
verification-loops is a skill published in the GitHub repository itallstartedwithaidea/agent-skills (44 stars, last pushed 6mo ago), licensed MIT. It adds 21 tokens to every session and 2,187 once invoked, about $0.0001 per session on Opus 5.5. 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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