Whamp

CodeGraph scout before broad grep/read. Use for repo explanation, navigation, diagnosis, runtime/reconnect flow, contract/RPC/schema tracing, refactor/cycle seams, dead-code cleanup, test targeting, and code review.

CodeGraph scout before broad grep/read. Use for repo explanation, navigation, diagnosis, runtime/reconnect flow, contract/RPC/schema tracing, refactor/cycle seams, dead-code cleanup, test targeting, and code review.

Paired trajectory analysis for benchmark churn. Use when comparing two configs on matched task/rep cells, explaining solve flips, diagnosing why one config solved and another failed, separating net score from churn, or preparing evidence to improve a skill/prompt/tool from trajectory differences.

Prompt embedding analysis. Use when clustering benchmark prompt/config text, comparing semantic neighbors, or separating prompt-shaped effects from behavioral wrappers in deep-swe-bench results.

Use before launching harness/run_batch.py for a benchmark, especially when configs use advisor, observational-memory workers, subagents, local-vllm shims, or any model beyond the main executor; use before claiming a benchmark launch is working.

Use before adding or changing a deep-swe-bench config, model leaf, provider/model API path, usage parser, smoke contract, or extension/subagent worker usage accounting.

Open a Herdr tail tab for a harness run when the user explicitly asks for a runboard, tail, Herdr tab, or raw log view. For new harness/run_batch.py monitoring, prefer the structured dashboard at scripts/run_dashboard.py reading results/_runs/<run_id>; this skill is the compatibility/tail workflow.

Local symbol-and-relationship map of the repo. Use to see who calls what (blast radius) before editing a function, class, or method. The binary is at /arm/bin/cg.

Combine findings from parallel intelligence gathering into unified execution plan. Synthesize predictions, web research, deep strategy, and environment observations into optimal approach. Use when multiple intelligence sources need integration before execution.

Generate comprehensive multi-approach strategies before execution. Extract domain knowledge, create alternative approaches, identify failure modes, and develop risk-aware plans. Use proactively for complex tasks requiring strategic thinking or when multiple approaches might succeed.

Perform heuristic environment discovery beyond basic directory listing. Check installed packages, analyze folder structure, inspect running processes, and assess system state. Use proactively when starting new tasks or when environment context is unclear.

Handle common execution failures with specialized recovery strategies. Fix syntax errors, import/dependency issues, path/file problems, permission denial, and connection timeouts. Use proactively when encountering errors or as automatic recovery mechanism.

Analyze tasks upfront before execution. Predict task category, identify key files, assess risk level, and detect high-consequence operations. Use proactively when any task description is provided to guide execution strategy.

Prevent premature task completion by checking for parsing errors, execution failures, incomplete test results, and missing expected outputs. Acts as final quality gate before declaring tasks complete. Use proactively after major operations or when task success needs verification.

Multi-round web search with low-frequency query generation. Prioritize GitHub/StackOverflow for actionable code, extract AI Overview insights, and perform deep link analysis. Use proactively for complex problems requiring external knowledge or when stuck on implementation challenges.

This skill should be used when the user asks to "add fields to PocketBase collection", "modify PocketBase schema", "add new collection fields", "update PocketBase collection", "PocketBase JavaScript SDK API", "programmatically add PocketBase fields", or mentions modifying PocketBase collection schemas via API. Provides comprehensive guidance for adding fields to existing PocketBase collections using the JavaScript SDK API.

This skill should be used when the user asks to "set up MailHog", "test email functionality", "configure MailHog", "send test emails", "check MailHog messages", "configure SMTP testing", or "manage email capture". Provides comprehensive MailHog email testing server setup and management.

Use the gemini-cli-integration skill PROACTIVELY when analyzing large codebases, multiple files, or directories. Leverages Google Gemini's massive context window with @ syntax for file inclusion to handle comprehensive codebase analysis, implementation verification, and architectural understanding.

Lightweight Chrome automation toolkit with shared configuration, JSON-first output, and six focused scripts for starting, navigating, inspecting, capturing, evaluating, and cleaning up browser sessions.

Comprehensive PocketBase development and deployment skill providing setup guides, schema templates, security patterns, API examples, data management scripts, and real-time integration patterns for building backend services with PocketBase.

Use when the user asks for a workflow, dynamic workflow, fan-out, multi-agent orchestration, ultracode-style work, large-scale verification, adversarial review, tournament selection, or decomposable research/refactor tasks. Teaches when to use Pi's workflow tool and which orchestration pattern to choose.

Converts grill-with-docs output — CONTEXT.md glossaries, ADRs, and approved domain decisions — into typed architecture. Use after grill-with-docs when the user wants domain types, seams, adapters, errors, call stacks, and dependency rules expressed in code before business behavior.

Use when writing tests for serialization pairs, parsers, validators, normalizers, pure functions, or data structures — or when reviewing code where property-based testing would catch edge cases that example tests miss

Use when the user wants to find, evaluate, and combine agent skills on a topic into a single best-of-breed version. Triggers on requests to search skills.sh, improve an existing skill by finding alternatives, or build a concentrated skill from multiple sources.

Use when the user explicitly wants first-principles thinking, wants to escape inherited assumptions, or asks to decompose a strategy/product/design/problem from fundamentals. Runs an interactive one-question-at-a-time grill that separates facts, assumptions, constraints, analogies, and unknowns, then rebuilds options from irreducible truths.

Use playwright-cli-axi when controlling Playwright from an agent shell and when video recording, TOON output, or AXI-friendly browser automation matters.

Agent eXperience Interface (AXI) — ergonomic standards for building CLI tools that agents use via shell execution. Use when building, modifying, or reviewing any agent-facing CLI.

Fake alpha skill for e2e testing.

Fake beta skill for e2e testing.

Compare and judge file summaries from pi-read-map vs standard read tool. Use when evaluating summary quality on large files (>2k lines). Trigger with "/skill:summary-judge <file-path>" to run full automated comparison and update demo/README.md.

Process files too large to fit in context (>100KB, >2000 lines). Uses Python REPL for structural analysis, LLM queries for semantic reasoning, and subagents for final synthesis. Triggers - large file, big document, massive log, full codebase, entire repo, long transcript, context window exceeded.

Use when working on a project with Brain agent memory management. Triggers on memory_commit, memory_branch tool usage, or when the project has a .memory/ directory.

A skill for interacting with a simple key-value store. Use this skill to get, set, and delete values from the store.

Context-driven development with spec-driven workflows, TDD-based task execution, track management, and phase checkpointing