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Found 14 Skills
Helps engineering managers measure and improve team delivery — produces a history of why common metrics fail, the DORA four-key-metrics framework (deployment frequency, lead time, change failure rate, MTTR), DevEx's three dimensions (feedback loops, cognitive load, flow state), a translation layer from engineering metrics to business outcomes, and a list of measurement anti-patterns to avoid. Use when the user says "how do I measure productivity," "DORA metrics," "velocity," "cycle time," "developer experience," "DevEx," "how do I show our team is performing well," "metrics for engineering," "team is slow," "engineering performance," or "connect engineering to business." Do NOT use for managing an underperforming individual — use performance-reviews instead.
Explains business financial terms and frameworks for engineering managers — produces term definitions (ARR, COGS, CAC, LTV, gross margin, burn rate, EBITDA, AARRR), translation formulas for making engineering work visible in business language, and a three-layer framework for building business credibility. Use when the user says "business terms," "EBITDA," "burn rate," "CAC," "LTV," "gross margin," "ARR," "how do I speak to business people," "I don't understand finance," "make the case for engineering work," "connect engineering to business outcomes," "talk to the P&L owner," or "business impact." Do NOT use when the user wants to connect engineering metrics (DORA, velocity) to business metrics — use developer-productivity instead.
Interactively debug source code — set breakpoints, step through execution line by line, inspect live variable state, evaluate expressions against the running program, and navigate the call stack to trace root causes. Use when a program crashes, raises unexpected exceptions, produces wrong output, when you need to understand how execution reached a certain state, or when print-statement debugging isn't revealing enough.
Generates a repo-specific orientation.md resource for the learning-opportunities skill. Only invoke via slash command (/orient:orient). Do not trigger automatically.
Helps users discover and apply shared coding solutions when they ask "has anyone solved this", "search for a fix", "find a workaround", or want proven patterns before debugging from scratch. Uses `npx shareful-ai search` to find relevant shares, compare options, and recommend the best match.
Surfaces relevant instincts during work. Use when starting a task to check if any learned behaviors apply.
Analyze your SpecStory AI coding sessions in .specstory/history for yak shaving - when your initial goal got derailed into rabbit holes. Run when user says "analyze my yak shaving", "check for rabbit holes", "how distracted was I", or "yak shave score".
Use whenever researching a technical question — a library, tool, API, error, version, or "what's the best way to X" — or whenever you're about to answer from memory. Forces multiple real searches over primary sources (official docs, source code, high-vote Stack Overflow, maintainer blogs) instead of one search plus training-data filler, and rejects SEO content-farm slop. Trigger on "research X", "look into", "what's the best library for", "how does X work", "is this still true", "find out".
Install groove's Claude Code native shell hooks into .claude/settings.json. Enables deterministic session-end reminders, git activity capture, and managed-path protection.
Compatibility-first Claude CLI reimplementation with faster startup, lower memory, and drop-in command compatibility
Practical AI agent workflows and productivity techniques. Provides optimized patterns for daily development tasks such as commands, shortcuts, Git integration, MCP usage, and session management.
Query Developer Experience (DX) data via the DX Data MCP server PostgreSQL database. Use this skill when analyzing developer productivity metrics, team performance, PR/code review metrics, deployment frequency, incident data, AI tool adoption, survey responses, DORA metrics, or any engineering analytics. Triggers on questions about DX scores, team comparisons, cycle times, code quality, developer sentiment, AI coding assistant adoption, sprint velocity, or engineering KPIs.