By sneg55
Bootstraps and maintains AI-agent-friendly projects by scaffolding new projects with structure and config, auditing existing codebases to incrementally adopt agent patterns, managing memory files, and running a self-improvement loop that proposes rule changes from past mistakes.
Apply agent-starter patterns to an EXISTING project - audits the codebase, proposes components by invasiveness tier (hooks, skills, CLAUDE.md, lint configs, patterns), and applies only what the developer approves. Use when the user says adopt, retrofit, "apply agent-starter to this project", or wants starter patterns in an existing codebase.
Full git workflow - creates branch, commits, pushes, and creates or updates a PR with summary and test plan.
Create a single well-crafted git commit from current changes. Analyzes diff, follows repo's commit style, and writes a concise "why not what" message.
Memory consolidation - review, merge, prune, and index memory files. Run periodically to keep memories organized and up-to-date.
Full project bootstrap - interviews the developer (name, description, stack, components), then scaffolds directory structure, CLAUDE.md, config files, hooks, skills, and first commit.
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A toolkit of skills, templates, and guides for bootstrapping AI-agent-friendly projects.
It started as Anthropic's own engineering patterns - extracted from the Claude Code CLI source and packaged into reusable form. It has since been extended with additional best practices, tooling, and original ideas that go beyond the source material. The flagship extension is a per-project self-improvement loop: scaffolded projects capture signal from their own usage and turn it into better rules over time (see below).
So the patterns here come from two places: those marked "derived from Anthropic's Claude Code source" are reverse-engineered from the real thing; everything else is added on top.
Point an AI agent at this repo and say "read this repo and set up my project" - the agent reads AGENT.md and scaffolds a complete project interactively:
read this repo and set up my project https://github.com/sneg55/agent-starter/
Or install the /new-project skill once and run it in any session:
npx skills add sneg55/agent-starter -a claude-code -g
Then: /new-project
For an existing project, say "read this repo and apply it to my project" - the agent reads ADOPT.md and runs an audit-first, opt-in adoption (nothing overwritten, everything on a branch). Or install the skills the same way and run /adopt-project.
For a whole team - making everyone get the same setup from a git pull (vendored hooks, shared config, plugin defaults) - see TEAM.md. It's the companion to ADOPT.md: where ADOPT.md installs into one developer's ~/.claude, TEAM.md ships the setup with the repo.
To install just the hooks (with their settings.json wiring merged idempotently via jq):
git clone https://github.com/sneg55/agent-starter && cd agent-starter
./install.sh # add --with-read-guard for the read-before-edit pair
Or install every skill at once as a Claude Code plugin - this repo doubles as a plugin marketplace:
/plugin marketplace add sneg55/agent-starter
/plugin install agent-starter@agent-starter
That loads all the skills (/new-project, /adopt-project, /commit, /simplify, /reflect, /remember, /dream). Hooks are still wired via install.sh above.
guides/large-codebase-best-practices.mdComprehensive best practices for setting up and scaling a large codebase with Claude Code - directory structure, file size targets, naming conventions, error handling, CLAUDE.md hierarchy, and more. All derived from analyzing Anthropic's own Claude Code CLI source.
guides/lint-rules-for-ai.mdTiered Biome + ESLint ruleset tuned for AI-agent-driven TypeScript codebases. Blocks the specific mistakes LLMs make (dropped awaits, any escape hatches, hallucinated imports, half-finished functions) rather than enforcing arbitrary line caps. Biome handles fast syntactic rules + formatting; ESLint keeps type-aware and plugin-specific rules. Pairs with templates/biome.json, templates/eslint.config.mjs, and hooks/lint-on-edit.sh. Includes the Python half: the same tiers via ruff + pyright, pairing with templates/ruff.toml + templates/pyrightconfig.json.
guides/hooks-reference.mdComplete reference for Claude Code's hook system - all 4 hook types, all 27 events, exit code behavior, configuration format, and 10 practical examples (auto-lint, block dangerous commands, agent verification, security review, Slack notifications, and more).
guides/tool-authoring-pattern.mdThe BashTool/-style directory-per-tool layout extracted from Claude Code's own source. Each tool is a handful of single-purpose files (toolName.ts, schema.ts, prompt.ts, validation.ts, permissions.ts, security.ts, execute.ts, result.ts). Gives an agent a predictable place to look when editing any tool and breaks whole classes of import cycles.
guides/error-id-registry.mdEvery thrown error carries a stable ID (E_CFG_003) from a central registry. Logs, telemetry, docs, and agents all reference the same ID, so error-message rewordings don't break grep or alerting. Pairs with templates/errorIds.ts (TS) and templates/error_ids.py (Python).
guides/discriminated-union-results.mdResult<Ok, Err> as the one shape every fallible function returns. Exhaustiveness-checked via the compiler; no ad-hoc {success, data} / {ok: 1} drift across edits. Reserves throws for programmer errors only.
guides/abort-signal-threading.mdThread AbortSignal through every long-running call so Ctrl+C, timeouts, and obsoleted work actually stop. Covers the canonical entry-point shape, AbortSignal.any for composed timeouts, the AbortError swallow anti-pattern, and an ESLint rule idea for bare fetch().
npx claudepluginhub sneg55/agent-starter --plugin agent-starterComplete project development toolkit: 23 agents, 23 slash commands, 29 lifecycle hooks, and 69 reusable skills for Claude Code workflows
HelloAGENTS — The orchestration kernel that makes any AI CLI smarter. Adds intelligent routing, unified QA gates, safety guards, and notifications.
Unified capability management center for Skills, Agents, and Commands.
Analyze and enforce best practices for AI coding agent projects. Assess codebase readiness across 8 pillars with /readiness, then scaffold enforcement with /setup: TDD, secret scanning, file size limits, auto-generated docs, and git hooks.
Harness for Claude Code — skills, /harness:* slash commands, persona subagents, lifecycle hooks, and MCP tools without per-repo `harness setup`. Sibling plugins exist for Cursor, Gemini CLI, and Codex.
Persistent memory, shared standards, and structured workflows for AI coding agents. Detects project setup and injects agent context automatically.