init
by mcollinainit helps create or improve AGENTS.md files by keeping only non-discoverable repo rules, workflow gotchas, and tool quirks. Use the init skill when setting up agent instructions, pruning stale guidance, or refining Claude configuration for a repository.
This skill scores 78/100, which means it is a solid directory candidate: users should have enough evidence to install it if they need help producing concise, repo-specific AGENTS.md instructions. The repository shows a real, specialized workflow for initialization and pruning, with enough operational detail to reduce guesswork compared with a generic prompt, though it is lighter on executable examples and install affordances.
- Strong triggerability: clearly scoped to creating or updating AGENTS.md, especially when existing instructions are long, generic, stale, or need pruning.
- Good operational clarity: the discoverability filter and quality gate give agents concrete rules for what belongs in AGENTS.md and what should be omitted.
- Useful install decision value: the description and body focus on a specific maintenance workflow for non-discoverable repository guidance, which is materially helpful for agent setup.
- No install command or support files are present, so users must infer how to adopt it from SKILL.md alone.
- Limited repository scaffolding around the skill means fewer examples, references, or automated checks to validate edge cases.
Overview of init skill
What init skill does
init creates or improves an AGENTS.md file for a repository. The goal is not to summarize the codebase, but to capture the few instructions an agent cannot reliably infer from the repo itself: hidden workflow preferences, tool quirks, non-obvious conventions, and repo-specific traps.
Who should use it
Use the init skill if you are setting up AI agent instructions for a new repo, replacing a bloated AGENTS.md, or fixing repeated mistakes that generic prompts keep missing. It is especially useful for teams doing Claude configuration or any agent workflow that depends on concise, repo-specific guidance.
Why it is different
The init skill is built around a discoverability filter: if an agent can learn it from README, code, config, or file structure, it should not go into AGENTS.md. That makes the output narrower and more actionable than a normal “project notes” prompt.
How to Use init skill
Install init skill
Install the skill into your environment first, then run it against the target repository. A typical install looks like:
npx skills add mcollina/skills --skill init
Give it the right input
The init skill works best when you provide a repository path or a clear target and enough context to identify the real gotchas. Strong inputs mention the repo’s actual workflow, the kinds of agent mistakes you want prevented, and any constraints that are not obvious from the tree.
Start with the right files
Read SKILL.md first, then inspect README.md, AGENTS.md, metadata.json, and any rules/, resources/, references/, or scripts/ folders if they exist. In this repo, the file tree is intentionally small, so SKILL.md and tile.json are the main starting points.
Turn a rough goal into a better prompt
Instead of asking for “an AGENTS.md,” ask for a focused repo instruction file that captures only non-discoverable rules. For example: “Use init to produce a minimal AGENTS.md for this repo, keeping only instructions that agents cannot infer from code, and remove anything redundant with the README or config.”
init skill FAQ
What problem does init skill solve?
It solves the common failure mode where agent instructions become too long, too generic, or outdated. The init skill helps produce a shorter AGENTS.md that improves agent behavior without duplicating obvious repository facts.
Is init for Skill Authoring or general repo setup?
It is primarily for repository setup and maintenance around AGENTS.md. If you are authoring skills, the same discipline helps, but the direct job is generating or pruning agent instructions for a codebase.
When should I not use init?
Do not use it if you want a broad project summary, onboarding guide, or architecture overview. It is also a poor fit when the repo has little hidden workflow knowledge and most guidance is already obvious from the files.
How to Improve init skill
Give concrete failure cases
The best init usage includes examples of what agents keep getting wrong: formatting drift, unsafe commands, missed build steps, or wrong file targets. Specific failure cases help the skill decide what belongs in AGENTS.md and what should be left out.
Provide stronger source material
If you want higher-quality output, supply the real AGENTS.md, a recent README, and any CI or tool config that reveals constraints. The more the repo already documents its visible conventions, the easier it is for init skill to strip noise and preserve only what matters.
Review for non-discoverable value
After the first pass, check each line against the discoverability filter: can an agent infer it from the repo itself? If yes, remove it. If no, keep it only when it changes task success, cost, or safety.
Iterate after one agent run
Treat the first AGENTS.md as a draft. If agents still make the same mistake, add one narrowly scoped line that addresses that failure and delete anything that merely restates the repository. That is the fastest way to keep init useful over time.