By iusztinpaul
Run an AI engineering agent team for Python/TypeScript/Go monorepos that plans, implements, tests, reviews, and deploys features through a progressive-disclosure pipeline — from spec grooming and architectural analysis to CI-validated PRs and on-call failure remediation.
Monitors CI/CD after `git push`. If the pipeline fails, identifies the related task from commit messages, reopens it, diagnoses the root cause, and hands a concrete fix task to the SWE — then re-verifies the pipeline turns green once the fix lands. Owns pipeline health; does not change application code itself. Use after any push performed by the SWE or by the orchestrator.
Reads the git diff after a feature is pushed. Tags every finding as Blocker or Nit. Produces ONE rollup task containing all findings. Does NOT read CI, does NOT comment on the PR, does NOT merge. Use after the SWE has pushed the feature branch and before the orchestrator hands the PR back to the human for squash-merge.
Grooms raw tasks into agent-ready specs (acceptance criteria + BDD scenarios) AND does final user-perspective acceptance review after the Tester passes. Use whenever a task needs to be turned into something the SWE can build, or whenever a task needs the final "is this actually right for users?" review before commit.
Implements a single groomed task assigned by the orchestrator. Writes code and tests locally. Does NOT commit until the Tester has reviewed and approved. Use when a task is groomed and ready for implementation, or when the Tester has returned feedback that needs to be addressed.
Reviews the software-engineer's uncommitted work against the spec and acceptance criteria. Runs the full test suite, verifies every acceptance criterion with evidence, and gives PASS or FAIL. Use after the SWE reports an implementation is done, or after the SWE applies fixes for previously-flagged issues.
Periodic architectural sweep of a codebase — reads existing ADRs to avoid re-litigating settled decisions, maps the current module/dependency graph and layering, surfaces 5–10 architectural smells with severity, and emits each finding as a refactor proposal that `/refactor` can consume directly. Trigger when the user says "/architecture-review", "audit the architecture", "what's wrong with this codebase", asks before a major version bump, or when long-horizon tech debt feels untracked.
Interview the user relentlessly about a plan or design. Use when the user wants to stress-test a plan before building, or uses any 'grill' trigger phrases.
Run the full agent-team pipeline end-to-end for one feature whose Tasks Plan is already approved (by /plan). A thin orchestrator — runs INSIDE the feature worktree and invokes /implement-task (build + commit every task), then /review (push + PA acceptance + PR-Reviewer), then /review-ci (CI), then a human-gated self-improve, and hands the validated PR to the human to squash-merge. Trigger after /plan, or say "/implement-night".
Implement one task — or work through a whole list of tasks / an approved Tasks Plan — via the inner SWE↔Tester loop, committing each task once it passes. Autonomous: SWE implements, Tester runs the suite + e2e adversarial pass, retry up to 5×, commit on PASS, move to the next task. Use when you have one or more GROOMED tasks ready to build, or say "/implement-task". To plan a feature first use /plan; for the full end-to-end pipeline use /implement-night.
Turn a raw feature spec into an approved Tasks Plan — one markdown file per atomic task under `tasks/` (or one GitHub Issue per task) — plus an optional ADR, glossary additions, branch, and worktree. Grills the human to sharpen the spec, has the Product Architect groom it into draft tasks (reading docs/glossary, proposing an ADR, using context7), offers another grilling round and shows the final plan before any task is created, then runs ONE human gate that decides everything touching the repo — approve the tasks and where to store them (local files vs GitHub Issues), create the ADR, apply the glossary additions, new worktree vs current tree, and which build to run (/implement-night, an /implement-task loop, or stop). Trigger when the user wants to plan a feature before building, or says "/plan".
Uses power tools
Uses Bash, Write, or Edit tools
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Claude Code writes code fast. It's worse at writing the code your team would actually ship — code that follows your conventions, has tests you trust, and survives review.
Squid is a Claude Code plugin that turns a feature spec into a reviewed PR through a 5-agent pipeline — PA → SWE → Tester → PR Reviewer → On-Call — with exactly two human gates: plan approval and final merge. No file templates, no render step: just markdown specs and agent contracts, and every file in your project gets written by an agent that reads them.
Run /plan <feature-spec> then /implement-night, and Squid drives this end-to-end:
feature spec
│
▼ /plan
┌──────────────────────────────────────────────────────────────┐
│ grill → PA grooms Tasks Plan (+ADR) → HUMAN approves (1/2) │
│ → branch + worktree │
└──────────────────────────────────────────────────────────────┘
│ /implement-night (runs end-to-end in the worktree)
▼
┌─────────────────┐ ┌──────────────────────┐ ┌─────────────────┐
│ /implement-task │──▶ │ /review │──▶ │ /review-ci │
│ SWE ↔ Tester │ │ push → PA accept → │ │ On-Call drives │
│ commit each task│ │ PR-Reviewer │ │ CI to green │
└─────────────────┘ └──────────────────────┘ └─────────────────┘
│
▼
HUMAN squash-merges (2/2)
Branch + worktree, grooming, the per-task implement/verify loop, push, diff review, and CI are all automated — you only show up for the two gates. For a quick single change, run /implement-task <task> (the same SWE ↔ Tester loop, no planning or review pipeline). Starting from an empty repo? Run /scaffold first — it interviews you about the stack and writes a tailored AGENTS.md plus a folder skeleton (no application source).
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/plugin marketplace add iusztinpaul/squid
/plugin install squid@iusztinpaul
That's it. Open any repo in Claude Code; the agents and skills appear in /agents and /help. Run /plugin marketplace update iusztinpaul later to pull fresh changes.
Installing Squid also pulls in three plugins the agent team relies on, all from Anthropic's official claude-plugins-official marketplace — context7 (live library docs via MCP), code-review, and commit-commands. That marketplace ships with Claude Code, so these resolve and enable on their own. (Requires Claude Code v2.1.143+ for auto-enable; v2.1.110+ for the dependency mechanism. If a dependency fails to resolve, run /plugin marketplace update claude-plugins-official.)
Commit this into the target repo's .claude/settings.json:
{
"extraKnownMarketplaces": {
"iusztinpaul": {
"source": {
"source": "github",
"repo": "iusztinpaul/squid"
}
}
},
"enabledPlugins": {
"squid@iusztinpaul": true
}
}
When a teammate (or future-you on a fresh machine) opens that repo and trusts the folder, Claude Code prompts them to add the marketplace and install in one step. enabledPlugins alone isn't enough — extraKnownMarketplaces is what tells Claude Code where squid@iusztinpaul resolves to.
claude --plugin-dir /path/to/squid
Launches Claude Code with the plugin loaded for the session. No marketplace, no install, no cache. Re-run after edits. This is the only path that exercises your local working tree directly on Claude Code v2.1+.
Research skills for an Obsidian vault — build, query, lint, distill, and render a persistent LLM-maintained research wiki over your vault, Readwise, NotebookLM, GitHub repos, and the web. Bundles the source CLIs' usage skills.
npx claudepluginhub iusztinpaul/squid --plugin squidPROJECT.md-first autonomous development with hybrid auto-fix documentation. 8-agent pipeline, auto-orchestration, docs auto-update on commit (true vibe coding). Knowledge base system with 90% faster repeat research. Strict mode enforces SDLC best practices automatically. Works for ANY Python/JavaScript/TypeScript/Go project.
AI-First SDLC — zero-debt development with validators, enforcement, and workflows
Virtual development team: TDD, debugging, code review, backlog management, and proven workflow patterns
Multi-agent orchestration for code that matters.
AI-powered agents for specialized development tasks
Implementation planning, execution, and PR creation workflows with multi-agent collaboration