.claude/skills/init/SKILL.md
Auto-evolution skill to initialize a new repository with AGENTS.md localized context.
npx skillsauth add oimiragieo/agent-studio initInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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NOTE: This is an auto-evolution skill designed to run when Agent Studio is first deployed into a new repository (e.g., when AGENTS.md does not yet exist).
Use this skill when you first enter a new codebase, or when the user explicitly asks to "initialize the repository" or "generate AGENTS.md".
This skill executes a rigorous 3-stage "Deep Ecosystem Evolution" pipeline.
Analyze the repository root to understand the technology stack and architecture. Specifically, look for and read:
README.md.cursorrules or .cursor/rules/.github/copilot-instructions.mdpackage.json, requirements.txt, Cargo.toml, go.mod, etc.)tsconfig.json, next.config.js, vite.config.ts, etc.)Generate AGENTS.md
Create or update a centralized AGENTS.md file in the repository root containing explicit, localized instruction sets for future Agents working in this codebase.
Include: exact test/build CLI commands, architecture notes, and environment quirks. Do not hallucinate support links. Do not include generic fluff. Show the proposed content to the user for confirmation if AGENTS.md already existed but is stale.
Cross-reference the discovered tech stack and repository complexity against the current available global tools (using the .claude/CLAUDE.md matrix).
agent-creator or skill-creator to generate bespoke, hyper-localized expert components (e.g., tensor-grep-rust-worker).Identify ONLY the subset of pre-existing agents and skills that are mathematically applicable to this repository's stack (e.g., if it's a TS web app, target typescript, react, jest, frontend). Do not evaluate all 200+ unrelated framework assets.
For each applicable asset:
node .claude/tools/cli/skill-freshness-report.cjs --name [asset-name] (or manually inspect its YAML frontmatter / git logs) to check its lastUpdated or createdAt timestamp.agent-updater or skill-updater to refresh its instruction context against modern best practices.{
"salientSummary": "Initialized the repository by scanning package.json and README.md. Detected a Next.js frontend with a Go backend. Generated AGENTS.md with explicit pnpm and Go build commands, testing strategies, and a high-level component map.",
"whatWasImplemented": "Created AGENTS.md in the repository root. Extracted 4 core architecture rules from .cursorrules. Verified that the test commands provided in AGENTS.md actually execute cleanly.",
"verification": {
"commandsRun": [
{
"command": "cat package.json | grep 'test'",
"exitCode": 0,
"observation": "Found vitest testing configuration."
}
]
}
}
tools
Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.
tools
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
data-ai
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
development
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.