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 cboone/agent-harness-plugins --skill clean-up-agent-configgit clone --depth 1 https://github.com/cboone/agent-harness-pluginsWrote 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/cboone/agent-harness-plugins/clean-up-agent-config)<a href="https://agentmods.dev/skills/cboone/agent-harness-plugins/clean-up-agent-config"><img src="https://agentmods.dev/badge/skills/cboone/agent-harness-plugins/clean-up-agent-config.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 | $0.00112 | $0.06014 |
| Opus 5 | $0.00056 | $0.03007 |
| Sonnet 5 | $0.00022 | $0.01203 |
| Haiku 4.5 | $0.00011 | $0.00601 |
Grade A, and why
clean-up-agent-config 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 8d 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 — 507 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Config Cleanup
Review, consolidate, and organize AI coding agent configuration and instruction files across four tools: Claude Code, OpenAI Codex, GitHub Copilot (CLI agent and code review), and OpenCode.
Reference Documents
Consult the reference files in this skill's references/ directory for detailed information about each tool's file formats, precedence rules, and unique capabilities:
references/agent-instruction-files.md-- CLAUDE.md, AGENTS.md, copilot-instructions.md, SKILL.md comparisonreferences/agent-config-files.md-- settings.json, config.toml, opencode.json, VS Code settings comparison
Read these references before starting work. They contain tool-specific details about file precedence, loading behavior, and cross-tool compatibility that inform every decision in this workflow.
Target Structure
The goal is a hub-and-spoke model: shared instructions in one canonical file, tool-specific configuration in each tool's hidden directory.
Instruction files (the hub)
Pick one scoped-instructions layout per repo. The two layouts below are mutually exclusive alternatives, not files that should coexist.
Flat layout (convenient for one or two scoped files):
repo/
+-- AGENTS.md # Single source of truth (all tools)
+-- CLAUDE.md -> AGENTS.md # Symlink for Claude Code
+-- .claude/
| +-- rules/
| +-- *.md # Claude-specific rules (auto-loaded)
+-- .github/
+-- copilot-instructions.md # Copilot repo-wide review rules
+-- *.instructions.md # Copilot path-scoped rules
Nested layout (scales better when there are many scoped files, or to keep .github/ uncluttered):
repo/
+-- AGENTS.md # Single source of truth (all tools)
+-- CLAUDE.md -> AGENTS.md # Symlink for Claude Code
+-- .claude/
| +-- rules/
| +-- *.md # Claude-specific rules (auto-loaded)
+-- .github/
+-- copilot-instructions.md # Copilot repo-wide review rules
+-- instructions/
+-- *.instructions.md # Copilot path-scoped rules
What ships with it
2 files 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.
- 8d ago First seen · 507 lines · 112 tokens per session scan A 24b01668d92f
clean-up-agent-config is a skill published in the GitHub repository cboone/agent-harness-plugins (2 stars, last pushed 1mo ago), licensed MIT. It adds 112 tokens to every session and 6,014 once invoked, about $0.0006 per session on Opus 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 skills, from other repositories
work-pipeline
Triggers the WORK-PIPELINE when a user request starts with a [] tag (e.g., [new-feature], [bugfix], [WORK start]). Use this skill whenever you detect a [] tag at the beginning of a user message.
feature-spec
Creates a complete product feature specification with acceptance criteria, scope, dependencies, and risks. Delegates to the Prometeo (PM) agent.
implement-feature
Implements a feature from its specification. Reads the spec, designs architecture, writes code and tests. Delegates to the Forja (Dev) agent.
sdd-pipeline
A collection of rules and schemas used by a software-development pipeline with separate agents for specifying, planning, scheduling, building, checking, and committing work.
02-user-stories
Produces or refines ordered User Stories from an Epic, Product Brief, PRD, or bounded request. Use when the user wants to slice, write, assess, order, or persist Stories. Not for Epics or implementation.
06-product-brief
Produces a concise Product Brief before requirements. Use when the user wants to frame or revisit a product opportunity and how it will be validated. Not for requirements, technical design, or planning.