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 agentmods add instructions/athola/skrills/agents-mdgit clone --depth 1 https://github.com/athola/skrillsWrote 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/athola/skrills/agents-md)<a href="https://agentmods.dev/instructions/athola/skrills/agents-md"><img src="https://agentmods.dev/badge/instructions/athola/skrills/agents-md.svg" alt="Measured on agentmods" 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 | $0.02395 | $0.02395 |
| Opus 5 | $0.01197 | $0.01197 |
| Sonnet 5 | $0.00479 | $0.00479 |
| Haiku 4.5 | $0.00239 | $0.00239 |
Grade B, and why
skrills AGENTS.md scanned grade B with 1 finding 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 6d 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.
Enumerates other installed skillsmediumAgent snooping
Other skills' SKILL.md files reveal prompts, capabilities and secrets that should be invisible to peers.
find ~/.codex/skills -name SKILL.md -type f How it starts
The opening of the file, as written. The whole thing — 324 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Agent Development Guidelines
Use these guidelines to build AI coding agents that produce functional, high-quality code.
Guiding Principles
Prioritize small, functional changes. They are safer and easier to debug than large rewrites. Adapt to each project's conventions rather than applying external rules rigidly.
Balance trade-offs like performance, readability, and security based on evidence (profiling, metrics) rather than intuition. Explore multiple solutions before committing to one to avoid "mode collapse." Favor simple, standard solutions. Code that requires extensive comments often needs refactoring.
Each component should serve one distinct purpose (Single Responsibility Principle). Defer abstraction until a clear pattern emerges (e.g., Rule of Three). Explicitly document assumptions, trade-offs, and confidence levels to maintain transparency.
Development Workflow
Implementation Cycle
- Understand: Read existing code, identify patterns, and review tests.
- Explore: Develop multiple viable approaches and articulate their trade-offs.
- Test: Write a failing test case before implementing new code (when applicable).
- Implement: Write the minimal code necessary to pass the test.
- Refactor: Clean the code while ensuring tests pass.
- Commit: Write a clear commit message explaining the change.
When Stuck
If you fail three times:
- Document failures and error outputs.
- Investigate 2-3 alternative approaches.
- Re-evaluate underlying assumptions.
- Experiment with a simpler approach.
- If the issue persists, ask for help with context from the previous steps.
Session Management
Use session history to analyze errors and monitor progress. For complex tasks, document the current state, then clear the session and restart.
Quality Standards
Commit Requirements
Every commit must:
- Compile or build successfully.
- Pass all existing tests.
- Include tests for new functionality.
- Follow linting rules (no warnings).
- Include a clear message explaining the rationale.
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.
- 6d ago First seen · 324 lines · 2,395 tokens per session scan B a9f50cf0310f
skrills AGENTS.md is an instructions file published in the GitHub repository athola/skrills (69 stars, last pushed yesterday), licensed MIT. It adds 2,395 tokens to every session, about $0.0120 per session on Opus 5. A static security scan graded it B with 1 finding (enumerates other installed skills). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
neo-skills AGENTS.md
AGENTS.md instructions for Benknightdark/neo-skills, covering neo-skills agents.md, 1. project overview, 2. commands, 3. workflow and 4. forbidden antipattern redlines.
codex-specialized-subagents AGENTS.md
Instructions for leonardsellem/codex-specialized-subagents, covering agents.md, project snapshot, root commands (verify these are real), universal conventions and security & secrets.
turbo CLAUDE.md
Claude Code instructions for tobihagemann/turbo, covering turbo, project structure and key files.
ai-toolkit AGENTS.md
AGENTS.md instructions for pipefy/ai-toolkit, covering repository guidelines, documentation map, project structure, import namespace migration: pipefysdk → pipefy and src/pipefysdk/init.py (transitional shim).
lxmusic CLAUDE.md
Instructions for tiancheng91/lxmusic, covering claude.md, 常用命令, 安装依赖, 运行测试 and 运行单个测试.
agent-teacher CLAUDE.md
Claude Code instructions for JackyYang258/agent-teacher, covering agentteacher, before you start, common commands and project-specific hard rules.