existential-birds
OrganizationClaude Code plugin marketplace: 145 framework-aware code-review skills plus AI-writing detection, doc and test-plan generation, architectural analysis, and git workflows — for Python, Go, Rust, Elixir, React, Remix, iOS/Swift, and AI frameworks. Installable for Codex and other agents too.
Categories
Indexed Skills (50)
explanation-docs
Explanation documentation patterns for understanding-oriented content - conceptual guides that explain why things work the way they do
llm-judge
Use when comparing two or more code implementations against a spec or requirements doc. Triggers on "which repo is better", "compare these implementations", "evaluate both solutions", "rank these codebases", or "judge which approach wins". Also covers choosing between competing PRs or vendor submissions solving the same problem. Does NOT review a single codebase for quality — use code review skills instead. Does NOT evaluate strategy docs — use strategy-review. Requires a spec file and 2+ repo paths.
prfaq-beagle
Use when the user wants to pressure-test a product, internal-tool, or OSS concept against Amazon's Working Backwards PRFAQ gauntlet before committing to a spec. Triggers on: "work backwards", "write a PRFAQ", "press release first", "is this idea worth building", "pressure-test this concept", "filter this before brainstorm", "is this a real product". Also catches solution-first pitches ("I want to build X that does Y") and technology-first pitches ("use AI to...") that need customer-first filtering. Produces a binary pass/fail verdict, not a polished doc. Hardcore coaching — direct, skeptical, concrete. On pass, hands off to brainstorm-beagle with a concept brief. Does NOT write code, plan implementation, scaffold projects, or draft specs.
strategy-interview
Use when the user wants to build or think through a strategy via guided conversation — for a company, product, team, career, or initiative. Triggers on "help me figure out our direction", "what should we focus on", strategic planning, competitive positioning, go-to-market strategy. Also catches indirect requests like prioritization struggles or "we have too many priorities". Does NOT review existing strategy documents (use strategy-review) or brainstorm project features (use brainstorm-beagle).
strategy-review
Use when reviewing, critiquing, or stress-testing an existing strategy document. Evaluates seven dimensions — diagnosis quality, guiding policy strength, action coherence, assumption exposure, falsifiability — with optional 7S, Five Forces, Balanced Scorecard, and Hoshin Kanri lenses. Triggers on: review my strategy, poke holes in this plan, what's weak here, strategy audit, red team this. Does NOT build strategy (use strategy-interview) or brainstorm project ideas (use brainstorm-beagle).
write-adr
Use when you want to generate Architecture Decision Records from this session. Triggers on "write ADRs", "document our decisions", "create decision records", "record the choices we made". Also useful after design discussions where decisions were reached but not documented. Does NOT extract decisions alone (use adr-decision-extraction) or provide MADR template (use adr-writing). Orchestrates the full workflow: subagent extraction, user confirmation, parallel generation, and verification.
write-plan
Use when you have a finalized `beagle-analysis:brainstorm-beagle` spec at `.beagle/concepts/<slug>/spec.md` and need a bite-sized, TDD-driven implementation plan before any code is written. Triggers on: "write a plan", "plan this spec", "turn the spec into a plan", "now plan the implementation", "/write-plan". Reads the spec, designs the file structure, decomposes work into 2-5 minute TDD steps with exact paths and commands, self-reviews against the spec, gets user approval, then writes to `.beagle/concepts/<slug>/plan.md`. Does NOT brainstorm specs, write code, or execute the plan — produces the plan document only.
create-pr
create a pull request with standardized description template
fetch-pr-feedback
Fetch unresolved review comments from a PR and evaluate with receive-feedback skill
fix-llm-artifacts
Applies fixes from a prior review-llm-artifacts run, with safe/risky classification. Respects verify-llm-artifacts output when present to skip false positives.
gen-release-notes
generate release notes for changes since a given tag
llm-artifacts-detection
Detects common LLM coding agent artifacts in codebases. Identifies test quality issues, dead code, over-abstraction, and verbose LLM style patterns. Use when cleaning up AI-generated code or reviewing for agent-introduced cruft.
prompt-improver
Optimize prompts for code-related tasks following Claude best practices. Use when refining prompts for implementation, debugging, refactoring, code review, or testing.
receive-feedback
Process external code review feedback with technical rigor. Use when receiving feedback from another LLM, human reviewer, or CI tool. Verifies claims before implementing, tracks disposition.
respond-pr-feedback
Respond to review comments on a PR after evaluation and fixes
review-feedback-schema
Schema for tracking code review outcomes to enable feedback-driven skill improvement. Use when logging review results or analyzing review quality.
review-llm-artifacts
Detects common LLM coding agent artifacts by spawning four parallel subagents over the project or changed files. Scans files changed since main by default; use --all for full-project scan. Triggers on LLM cruft cleanup, agent-generated code review, dead code sweeps, test-quality passes, or when the user asks to scan the whole repo.
review-plan
Review implementation plans for parallelization, TDD, types, libraries, and security before execution
review-skill-improver
Analyzes feedback logs to identify patterns and suggest improvements to review skills. Use when you have accumulated feedback data and want to improve review accuracy.
review-structure
Repo-wide structural-maintainability review — code-judo restructurings, 1k-line file guard, anti-spaghetti branching, canonical-layer enforcement, anti-magic abstractions, explicit type/boundary contracts.
review-verification-protocol
Mandatory verification steps for all code reviews to reduce false positives. Load this skill before reporting ANY code review findings.
subagent-prompt
Produce a comprehensive prompt that hands off the current session's work to a fresh session for sub-agent-orchestrated execution. Use when the user wants to execute discussed/planned work in a new session, run a job to completion via sub-agents, or generate a portable handoff prompt with per-task verification. Triggers on "/subagent-prompt", "give me a prompt to run this in a new session", "hand this off to sub-agents", "execute this with sub-agents".
verify-llm-artifacts
Confirms or rejects findings from review-llm-artifacts before deletes or risky refactors. Loads review-verification-protocol-style checks per finding. Use after a review run, when the user wants to reduce false positives, before fix-llm-artifacts on dead code, or when validating a full-project scan.
docs-style
Core technical documentation writing principles for voice, tone, structure, and LLM-friendly patterns. Use when writing or reviewing any documentation.
draft-docs
Generate first-draft technical documentation from code analysis
ensure-docs
Verify documentation coverage and generate missing docs interactively
commit-push
commit and push all local changes to remote repo
deepagents-architecture
Guides architectural decisions for Deep Agents applications. Use when deciding between Deep Agents vs alternatives, choosing backend strategies, designing subagent systems, or selecting middleware approaches.
deepagents-code-review
Reviews Deep Agents code for bugs, anti-patterns, and improvements. Use when reviewing code that uses create_deep_agent, backends, subagents, middleware, or human-in-the-loop patterns. Catches common configuration and usage mistakes.
deepagents-implementation
Implements agents using Deep Agents. Use when building agents with create_deep_agent, configuring backends, defining subagents, adding middleware, or setting up human-in-the-loop workflows.
langgraph-architecture
Guides architectural decisions for LangGraph applications. Use when deciding between LangGraph vs alternatives, choosing state management strategies, designing multi-agent systems, or selecting persistence and streaming approaches.
langgraph-code-review
Reviews LangGraph code for bugs, anti-patterns, and improvements. Use when reviewing code that uses StateGraph, nodes, edges, checkpointing, or other LangGraph features. Catches common mistakes in state management, graph structure, and async patterns.
langgraph-implementation
Implements stateful agent graphs using LangGraph. Use when building graphs, adding nodes/edges, defining state schemas, implementing checkpointing, handling interrupts, or creating multi-agent systems with LangGraph.
pydantic-ai-agent-creation
Create PydanticAI agents with type-safe dependencies, structured outputs, and proper configuration. Use when building AI agents, creating chat systems, or integrating LLMs with Pydantic validation.
pydantic-ai-common-pitfalls
Avoid common mistakes and debug issues in PydanticAI agents. Use when encountering errors, unexpected behavior, or when reviewing agent implementations.
pydantic-ai-dependency-injection
Implement dependency injection in PydanticAI agents using RunContext and deps_type. Use when agents need database connections, API clients, user context, or any external resources.
pydantic-ai-model-integration
Configure LLM providers, use fallback models, handle streaming, and manage model settings in PydanticAI. Use when selecting models, implementing resilience, or optimizing API calls.
pydantic-ai-testing
Test PydanticAI agents using TestModel, FunctionModel, VCR cassettes, and inline snapshots. Use when writing unit tests, mocking LLM responses, or recording API interactions.
pydantic-ai-tool-system
Register and implement PydanticAI tools with proper context handling, type annotations, and docstrings. Use when adding tool capabilities to agents, implementing function calling, or creating agent actions.
vercel-ai-sdk
Vercel AI SDK for building chat interfaces with streaming. Use when implementing useChat hook, handling tool calls, streaming responses, or building chat UI. Triggers on useChat, @ai-sdk/react, UIMessage, ChatStatus, streamText, toUIMessageStreamResponse, addToolOutput, onToolCall, sendMessage.
adr-decision-extraction
Use when you need to mine a conversation, session transcript, or design discussion for architectural decisions before writing ADRs. Identifies problem-solution pairs, trade-off debates, technology choices, and explicit "[ADR]" tags. Triggers on "what decisions did we make", "extract decisions from this chat", "find the choices in our discussion", or "summarize architectural decisions". Also useful after long planning sessions to capture decisions that were made implicitly. Does NOT write ADR documents — use adr-writing or write-adr for that.
adr-writing
Use when writing or formatting an ADR document using the MADR template, applying Definition of Done (E.C.A.D.R.) criteria, or verifying ADR completeness. Triggers on "write the ADR", "format as MADR", "check ADR quality", "mark gaps in ADR". Also triggers when a decision has been extracted and needs to become a document. Does NOT extract decisions from conversations (use adr-decision-extraction) or orchestrate the full extract-confirm-write workflow (use write-adr).
agent-architecture-analysis
Use when auditing an agent codebase against the 12-Factor Agents methodology, reviewing LLM-powered system architecture, or assessing agentic app compliance. Triggers on "analyze agent architecture", "12-factor audit", "how compliant is this agent", or "evaluate this LLM app". Also applies when comparing frameworks or planning agent improvements. Not for quick checklists — this performs deep per-factor codebase analysis with file-level evidence.
artifact-analysis
Use when the user wants a cited, structured read of local documents and project knowledge. Triggers on: "analyze these docs", "scan my project for context", "read the docs folder", "summarize what's in .beagle/concepts/", "extract context from docs/", "what's in this folder", "go read everything in X and tell me what's there". Also invoked programmatically by other beagle skills (prfaq-beagle Ignition, brainstorm-beagle reference points, strategy-interview context grounding) via the companion contract. Does NOT trigger on codebase lookups ("find this function", "search the repo"), web research (use web-research), LLM-as-judge evaluation (use llm-judge), or document editing (use humanize-beagle). Produces a written scan plan, parallel-subagent findings, and a cited synthesis report on disk — never inline prose, never unsourced claims.
brainstorm-beagle
Use when the user has a fuzzy idea and wants to shape it into a concrete project spec before planning or building. Triggers on: "brainstorm this", "I have an idea for...", "help me think through this project", "what should I build", "spec this out". Also catches vague feature descriptions needing structured questioning to clarify scope. Does NOT write code, plan implementation, review strategy docs, or run strategy interviews — produces a WHAT/WHY spec through dialogue, not a HOW plan.
resolve-beagle
Use as the follow-up to brainstorm-beagle when a spec has an Open Questions section (or quietly carries latent gaps) that need closing before planning or implementation can begin. Triggers on: "resolve the open questions", "close the gaps in this spec", "research the open items", "finalize my spec", "make this spec implementation-ready", "answer the TBDs". Also triggers whenever the user points at a brainstorm-beagle spec and asks for research, proposals, or answers to unresolved items. Orchestrates parallel research subagents when available (falls back to inline sequential research otherwise), proposes answers one at a time for user approval, then rewrites the spec in place so it arrives at planning with no known gaps. Does NOT write code, design implementation, or create plans — it only produces a complete spec.
web-research
Use when the user wants web research: gathering cited, multi-angle evidence on a specific question. Triggers on: "research X for me", "do web research on", "look up sources for", "find citations for", "gather evidence on", "what does the web say about X". Also invoked programmatically by other beagle skills (prfaq-beagle Ignition, brainstorm-beagle reference points, strategy-interview context grounding) via the companion contract. Does NOT trigger on codebase lookups ("find this function", "search the repo"), local file search, LLM-as-judge evaluation, or paywalled/auth-gated scraping. Produces a written plan, parallel-subagent findings, and a cited synthesis report on disk — never inline prose, never unsourced claims.
docling
Docling document parser for PDF, DOCX, PPTX, HTML, images, and 15+ formats. Use when parsing documents, extracting text, converting to Markdown/HTML/JSON, chunking for RAG pipelines, or batch processing files. Triggers on DocumentConverter, convert, convert_all, export_to_markdown, HierarchicalChunker, HybridChunker, ConversionResult.
github-projects
GitHub Projects management via gh CLI for creating projects, managing items, fields, and workflows. Use when working with GitHub Projects (v2), adding issues/PRs to projects, creating custom fields, tracking project items, or automating project workflows. Triggers on gh project, project board, kanban, GitHub project, project items.
sqlite-vec
sqlite-vec extension for vector similarity search in SQLite. Use when storing embeddings, performing KNN queries, or building semantic search features. Triggers on sqlite-vec, vec0, MATCH, vec_distance, partition key, float[N], int8[N], bit[N], serialize_float32, serialize_int8, vec_f32, vec_int8, vec_bit, vec_normalize, vec_quantize_binary, distance_metric, metadata columns, auxiliary columns.
Bio shown is the top-scored skill's repo description as a fallback — real GitHub bios land in a future update.