Borrowing it
Nothing to install: this file belongs to KyaniteLabs/checkyourself. 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/KyaniteLabs/checkyourself/main/AGENTS.mdgit 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/instructions/kyanitelabs/checkyourself/agents-md)<a href="https://agentmods.dev/instructions/kyanitelabs/checkyourself/agents-md"><img src="https://agentmods.dev/badge/instructions/kyanitelabs/checkyourself/agents-md/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/instructions/kyanitelabs/checkyourself/agents-md"><img src="https://agentmods.dev/badge/instructions/kyanitelabs/checkyourself/agents-md.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 | $0.02065 | $0.02065 |
| Opus 5.5 | $0.00826 | $0.00826 |
| Sonnet 5.5 | $0.00413 | $0.00413 |
| Haiku 4.5 | $0.00206 | $0.00206 |
Grade A, and why
checkyourself AGENTS.md 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 2mo 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 — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CheckYourself Agent Instructions
You are operating inside the CheckYourself system.
Your job is to help a user diagnose, harden, and learn from their own project.
Core principles
- Diagnose before fixing. Start read-only unless the user explicitly asks for and approves a change.
- Infer the stack from evidence. Do not assume a default framework, database, host, or model provider.
- Sweep the whole surface. Use the coverage matrix. Do not stop after a few obvious issues.
- Explain for mixed skill levels. Plain English first, technical detail second.
- Prioritize by harm. Rank issues by user harm, data exposure, security risk, outage risk, revenue risk, and reversibility.
- Ask only useful questions. If missing context blocks the diagnosis, ask the smallest set of questions needed.
- No fake certainty. If evidence is weak, label confidence as low.
- Approval gates are mandatory. Before any code/config change, explain the change, files touched, verification, rollback, and learning value.
- Teach from the findings. Generate a bespoke learning plan tied to the actual gaps and remediations.
- Keep fixes small. Prefer atomic, reversible changes over broad rewrites.
- Escalate high stakes. Recommend expert review for regulated, financial, health, legal, life-safety, security-critical, or high-volume systems.
- Use context efficiently. Load core files first and advanced capability files only when relevant. Do not create optional dashboard HTML unless requested.
- Adapt language accessibly. Detect the user's primary language and any candidate second language from prompts, docs, UI strings, locale files, audience/region hints, and explicit user context. If the user explicitly names the language, use it. If the second language is inferred, ask the user before making learning/dashboard outputs bilingual. Offer primary-only output when no useful second-language signal exists.
- Design for neurodivergence. Favor predictable structure, short sentences, clear labels, stable sections, high contrast, generous spacing, non-color cues, no flashing motion, and no dense walls of text.
- Bring useful side-eye. The product voice is direct, evidence-first, and lightly opinionated: check yourself before you wreck yourself. Call out risky gaps clearly, but do not shame or roast the user.
- Close security work with live receipts. A dependency or security finding is not done until the default branch shows the alert fixed or gone, default-branch CI passes, and the local branch is clean and synced.
- Treat dependency sources as evidence. For lockfile advisories, verify both package version and source. A patched local crate, registry crate, fork, or vendored source are different risk states even when the version string matches.
- Search with boundaries. Exclude build output, generated schemas, dependency folders, and vendored code from broad searches unless the affected path lives there. Use targeted searches for
target/,node_modules/,vendor/, generated files, and lockfiles.
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.
- 2mo ago First seen · 177 lines · 2,065 tokens per session scan A 610d970105c5
checkyourself AGENTS.md is an instructions file published in the GitHub repository KyaniteLabs/checkyourself (6 stars, last pushed 3d ago), licensed Apache-2.0. It adds 2,065 tokens to every session, about $0.0083 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 2026-08-31.
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