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 srtab/daiv --skill initgit clone --depth 1 https://github.com/srtab/daivWrote 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/srtab/daiv/init)<a href="https://agentmods.dev/skills/srtab/daiv/init"><img src="https://agentmods.dev/badge/skills/srtab/daiv/init/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/srtab/daiv/init"><img src="https://agentmods.dev/badge/skills/srtab/daiv/init.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00086 | $0.00681 |
| Opus 5.5 | $0.00034 | $0.00272 |
| Sonnet 5.5 | $0.00017 | $0.00136 |
| Haiku 4.5 | $0.00009 | $0.00068 |
Grade A, and why
init 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 — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze this repository and produce the full contents of AGENTS.md for future coding agents.
Goal (very important):
- Make this file as SHORT as possible while still capturing REPO-SPECIFIC, NON-OBVIOUS, HIGH-LEVERAGE guidance.
- Avoid redundancy: do not restate README/docs/rules unless you are adding a concise delta that changes agent behavior.
- Prefer pointing to existing docs by path (e.g., "See docs/xyz.md") rather than copying text.
Hard constraints:
- Target length: 250–500 words. Only exceed if there is a critical repo-specific reason.
- Only include commands that you can VERIFY exist in the repo (Makefile, scripts, pyproject, CI files, docs). Do not guess.
- Do not add generic engineering advice, generic style guides, or “be careful” warnings.
- Do not invent sections (e.g., “Tips”, “Support”, “Common tasks”) unless you found that exact content in-repo and it is essential.
- Do not include “ask for confirmation” directives; instead, express risk as plan-time checks.
If AGENTS.md already exists:
- Produce an updated version that reduces duplication and removes low-signal text.
- Keep any existing repo-specific invariants and verified commands, but rewrite for brevity and clarity.
Inputs to consult (only extract what is truly needed and not redundant):
- Existing
AGENTS.md(if present) .cursor/rules/,.cursorrules,CLAUDE.md,.github/copilot-instructions.md(if present)README.mdand docs (only for verified commands / invariants)- Build/test/lint definitions: Makefile, pyproject.toml, CI workflow files, scripts directories
Required output format:
- Produce ONE Markdown document: the full contents of
AGENTS.md. - Prefix EXACTLY:
```
AGENTS.md
This file provides guidance to agents when working with code in this repository. ```
See examples/agents-md-sample.md for a reference output demonstrating all four sections done well.
Sections (keep terse; omit any section if you have nothing verified to add):
- Commands (verified): fastest path for test + lint + single-test invocation
- Repo map (only what’s non-obvious): 5–10 bullets max; name directories only if it helps navigation
- Invariants / footguns: only rules that prevent wasted work or incorrect changes (e.g., state update patterns, “don’t edit X directly”)
- Where changes usually go (optional): only if there are non-obvious entry points that save time
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.
- 1mo ago First seen · 54 lines · 86 tokens per session scan A 93cd13552661
init is a skill published in the GitHub repository srtab/daiv (24 stars, last pushed yesterday), licensed Apache-2.0. It adds 86 tokens to every session and 681 once invoked, about $0.0003 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.
Other skills, from other repositories
rust-string-enums
Define or refactor Rust string-valued enums and their Serde adapters in litellm-rust, using Strum and serdewith while preserving parsing, wire values, and schemas.
rust-tracing
Add or change Rust diagnostic tracing in litellm-rust, including route spans, subscriber layers, and Python logger delivery.
url-state
Read or write dashboard URL query state (tabs, filters, pagination, deep links, demo flags) with nuqs instead of raw history or URLSearchParams.
using-superpowers
Use when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions.
cubepi-trace
Use when debugging a cubepi/cubebox agent run — a missing final reply, a tool that misbehaved, a 4xx from the model, wrong token/cache numbers, or "why did the agent do that?". Reads the JSONL traces cubepi writes per run via the cubepi trace CLI (ls / view / follow / stats) to inspect the span tree, errors, tool…
cubepi
Use when building, extending, or debugging agents with the CubePi framework. Covers the Agent API, providers, tools, middleware, checkpointing, MCP integration, and HITL. References the cubepi-trace skill for run debugging.