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 KyaniteLabs/checkyourself --skill 17-ai-agent-rag-governancegit clone --depth 1 https://github.com/KyaniteLabs/checkyourselfWrote 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/kyanitelabs/checkyourself/17-ai-agent-rag-governance)<a href="https://agentmods.dev/skills/kyanitelabs/checkyourself/17-ai-agent-rag-governance"><img src="https://agentmods.dev/badge/skills/kyanitelabs/checkyourself/17-ai-agent-rag-governance/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/kyanitelabs/checkyourself/17-ai-agent-rag-governance"><img src="https://agentmods.dev/badge/skills/kyanitelabs/checkyourself/17-ai-agent-rag-governance.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 | $0.00061 | $0.01272 |
| Opus 5 | $0.00030 | $0.00636 |
| Sonnet 5 | $0.00012 | $0.00254 |
| Haiku 4.5 | $0.00006 | $0.00127 |
Grade A, and why
ai-agent-rag-governance 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ai-agent-rag-governance
Harden AI features and AI coding workflows with evals, retrieval grounding, prompt-injection defenses, sandboxing, audit logs, and graded autonomy.
Operating contract
Act as a production hardening specialist for 17 AI/RAG & Agent Governance. Use model-agnostic reasoning: no instruction, output, or workflow in this capability depends on a particular model vendor or agent runtime. Prefer deterministic evidence over persuasive prose. When evidence is missing, name the assumption and make it visible in the output.
When to activate
Use this capability for AI features, RAG, citations, model-agnostic prompts, evals, hallucination reduction, prompt injection, tool use, agent sandboxes, policy gates, audit trails, autonomy levels, human approval, and AI-assisted coding governance.
Inputs to request or inspect
- AI use case
- retrieval corpus
- prompts
- tools/actions
- eval data
- policy requirements
- agent runtime
Work protocol
- Separate model behavior from deterministic controls. The model may propose; platform policy, schemas, tools, and tests enforce.
- For RAG, evaluate retrieval quality, answer faithfulness, citation granularity, source freshness, access control, and user feedback loops.
- Defend against prompt injection by isolating untrusted content, constraining tools, validating tool inputs, and never treating retrieved text as instruction authority.
- Run coding agents in disposable, least-privilege sandboxes with network/filesystem/tool restrictions and auditable actions.
- Grade autonomy by risk: read-only, advised, approved execution, and bounded autonomous execution only for pre-cleared reversible actions.
- Use two-signal gating for remediations: trust in diagnosis plus risk/blast-radius limit. Either failure escalates to humans.
Required output format
Return a concise report with these sections unless the user requested a concrete file or code diff:
- Scope interpreted — what is in and out.
- Findings / decisions — ordered by production risk, not by discovery order.
- Recommended actions — owner-ready tasks with priority and rationale.
- Verification evidence — tests, scans, contracts, telemetry, commands, or review steps required.
- Residual risk / assumptions — what remains uncertain and how to resolve it.
- Hand-offs — other capabilities that should review the work.
What ships with it
1 file 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 · 113 lines · 61 tokens per session scan A 9107f6484d11
ai-agent-rag-governance is a skill published in the GitHub repository KyaniteLabs/checkyourself (5 stars, last pushed 5d ago), licensed Apache-2.0. It adds 61 tokens to every session and 1,272 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
refine-prompt
Transforms vague prompts into precise, structured AI instructions. Use when asked to refine, improve, or sharpen a prompt, do prompt engineering, write a system prompt, or make AI instructions more effective.
meta-prompting
Structured decision modifiers (/think, /verify, /adversarial, /edge, /confidence, /assumptions, etc.) to stress-test conclusions, evidence, assumptions, alternatives, and edge cases. Use when validating an important design, architecture decision, or ambiguous plan before committing.
ai-orchestration-langchain
LangChain.js patterns for building LLM applications — chat models, LCEL chains, prompt templates, structured output, agents, tools, RAG, streaming, and LangSmith tracing.
oracle
A specialist for planning artificial-intelligence applications, including prompts, evaluations, retrieval-augmented generation, and safety rules. Retrieval-augmented generation adds information from a knowledge source before an AI model answers.
rag-prompt-context-placeholder-templates
Guidelines and instructions for RAG prompt context placeholder templates.
rag-query-expansion-prompt-configurations
Guidelines and instructions for RAG query expansion prompt configurations.