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/kunalsuri/ai-fication-kitWrote 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/kunalsuri/ai-fication-kit/ai-knowledge-layer)<a href="https://agentmods.dev/rules/kunalsuri/ai-fication-kit/ai-knowledge-layer"><img src="https://agentmods.dev/badge/rules/kunalsuri/ai-fication-kit/ai-knowledge-layer/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/kunalsuri/ai-fication-kit/ai-knowledge-layer"><img src="https://agentmods.dev/badge/rules/kunalsuri/ai-fication-kit/ai-knowledge-layer.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 | $0.00270 | $0.00270 |
| Opus 5.5 | $0.00108 | $0.00108 |
| Sonnet 5.5 | $0.00054 | $0.00054 |
| Haiku 4.5 | $0.00027 | $0.00027 |
Grade A, and why
ai-knowledge-layer 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
This repo has an AI knowledge layer under ai/. Read ai/INDEX.md first — it is
the role → path manifest for finding the right map for your task — then
ai/guide/MODULE_MAP.md to locate code by directory before you crawl the tree.
Provenance. Anything written into ai/ is [inferred] until a human flips it
to [verified]. Never flip that tag yourself.
Record work. Every unit of work ends with a row appended to
ai/lab/WORKLOG.md — even work done outside these rules. Backtick every
artifact path in the row: verify checks backtick-quoted path claims against
the tree (plain-text links are not validated).
See AGENTS.md (already read by Cursor) for the full rule set. The other rules in
this directory (.cursor/rules/*.mdc) mirror the kit's skills for Claude
Code and prompt files for GitHub Copilot — invoke them the same way you would any
other Cursor rule.
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 · 23 lines · 270 tokens per session scan A ec93676467c0
ai-knowledge-layer is a cursor rule published in the GitHub repository kunalsuri/ai-fication-kit (3 stars, last pushed 3d ago), licensed Apache-2.0. It adds 270 tokens to every session, about $0.0011 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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