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Found 1,895 Skills
When the user needs to design an interview process, create interview questions, build scorecards, calibrate interviewers, or evaluate candidates for a role.
Use when a developer wants to iterate on ONE specific Agent Observability / LLM Obs trace whose output they didn't like — re-running that trace against their LOCAL code, seeing a concise diff of the old vs new output, and looping (change code → replay → diff) until satisfied. Invoked as /agent-observability-replay-trace <trace-id> [changes to test]. Signals: "replay this trace"; "iterate on a trace"; "this trace's output is wrong, fix it and re-run"; "re-run trace <id> with <change>"; pasting a trace id from the Agent Observability UI with a description of what to fix. It fetches the trace via the datadog-llmo MCP or the pup CLI, edits code, re-runs the app to emit a NEW trace, and diffs the two — no local server, no browser. For agents traced with ddtrace / LLM Obs (Python first-class), with JSON-serializable entry input. Do NOT use for: scored Experiments or the browser "Replay" button (that's agent-observability-replay-experiment), building an experiment from a dataset/CSV, writing evaluators, root-causing failed traces, or RUM/HTTP session replay.
Use when a claim depends on an agent taking a real action.
UserInputService, ContextActionService, keyboard/mouse/gamepad/touch, cross-platform input binding.
peer-review a plan, design, or implementation, find the one critical risk, list other gaps, propose a fix for the critical risk, and update the plan. Triggers: peer review a plan, review an implementation, assess a design, check a proposal for gaps.
Iterate on RAG systems with structured evals instead of eyeballing. This skill should be used when the user is tuning a RAG pipeline — changing retrieval prompts, swapping models, adjusting chunking, or debugging poor answers — and wants a cheap, ranked set of experiments with cost tracking and structured feedback on the stack. Also use when the user asks "how do I know if my RAG is working?", "this RAG eval is burning money", or "what should I try next on retrieval?".
Write professional investment memorandums for VC, PE, or public market investments. Structure thesis, risks, and recommendations clearly.
Expert in designing high-level enterprise solutions. Specializes in TOGAF adaptation, trade-off analysis, and aligning technology with business strategy.
Analyze story genre types, extract creative elements and story features. Suitable for identifying genre positioning, refining creativity, and analyzing market audiences
PostHog feature flags for Elixir applications
Value investing screen via Longbridge — scan A-share / HK / US stocks for fundamentally strong but undervalued companies based on PE, PB, dividend yield, ROE, and margin of safety. Suitable for value investing strategy. Triggers: "低估值", "价值投资", "低PE", "低PB", "便宜股票", "安全边际", "高股息低估值", "被低估", "低估值", "價值投資", "低PE", "低PB", "便宜股票", "安全邊際", "高股息低估值", "value investing", "undervalued stocks", "low PE", "low PB", "margin of safety", "value screen", "cheap stocks", "bargain stocks".
Review designs, products, and features with Steve Jobs' standards: ruthless simplicity, focus, and end-to-end excellence. Use when the user mentions "Steve Jobs review", "design review", "product review", "what would Steve do", "insanely great", "this feels too complicated", "too many features", "product taste", "saying no", or "is this good enough to ship". Also trigger when critiquing a UI, feature, or roadmap for focus and simplicity, cutting scope to the essential, or pressure-testing the whole experience from first run to daily use. Covers the simplicity audit, the no list, design-is-how-it-works, end-to-end ownership, demo culture, and a Jobs-style review protocol with binary verdicts. For visual design fundamentals, see refactoring-ui. For usability audits, see ux-heuristics. For detail polish, see microinteractions.