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.
git clone --depth 1 https://github.com/hariharapanigrahy/layerkitWrote 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/rules/hariharapanigrahy/layerkit/ai-working-rules)<a href="https://agentmods.dev/rules/hariharapanigrahy/layerkit/ai-working-rules"><img src="https://agentmods.dev/badge/rules/hariharapanigrahy/layerkit/ai-working-rules/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/rules/hariharapanigrahy/layerkit/ai-working-rules"><img src="https://agentmods.dev/badge/rules/hariharapanigrahy/layerkit/ai-working-rules.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 | $0.00097 | $0.00097 |
| Opus 5.5 | $0.00039 | $0.00039 |
| Sonnet 5.5 | $0.00019 | $0.00019 |
| Haiku 4.5 | $0.00010 | $0.00010 |
Grade A, and why
ai-working-rules 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.
What it actually says
Read and follow AI_WORKING_RULES.md.
In short:
- prefer update/delete over adding new files
- define what must pass, proof artifact, and fallback before implementation
- prove strategic redirects before broad rewrites or deletions
- keep test backing proportional to implementation size
- treat hardcoded secrets as release blockers in public/shared repos
- work collaboratively and evidence-first
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 · 16 lines · 97 tokens per session scan A 8b46748b8c61
ai-working-rules is a cursor rule published in the GitHub repository hariharapanigrahy/layerkit (8 stars, last pushed 2mo ago), licensed MIT. It adds 97 tokens to every session, about $0.0004 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.
Other cursor rules, from other repositories
repo-overview
What the AI Skills Library is, its structure, and core principles. Apply when reasoning about the repo as a whole.
skill-authoring
Conventions for writing or editing a SKILL.md skill package.
python-tools
Conventions for the Python automation tools that ship inside skills.
cdv
How to drive the cdv MCP sidecar (observe, verify, remember, resume).
remote-pc
Drive a host running agentic-remote-pc over its MCP runner. Use when the user asks to run commands on a remote PC, drive a coding-agent CLI on a host, or validate a local feature/deployment.
testing-and-safety
TDD bias; no secrets in code; new logic needs unit tests.