Diagnose website and API performance using httpstat — a curl wrapper that visualizes HTTP timing breakdowns (DNS, TCP, TLS, server processing, content transfer). Use this skill whenever the user wants to debug slow websites, analyze HTTP/HTTPS latency, profile API response times, understand curl timing output, find…
Build ONE reusable chart visualization component that receives its data and its settings from the host application instead of fetching them, and declares the fields and config options the host exposes to viewers. Use this whenever a single chart component is reused across many different queries rather than built for…
MCP server "sentry" as configured in ChrisWiles/claude-code-showcase. Runs locally from the @anthropic/mcp-sentry npm package. Needs 2 environment variables to run.
Analyze a chart — set up symbol/timeframe, add indicators, scroll to key dates, annotate, and screenshot. Use when the user wants technical analysis or chart review.
Generate a comprehensive strategy performance report — metrics, trade analysis, equity curve, and recommendations. Use after backtesting a Pine Script strategy.
This skill should be used when the user wants to "run an evaluation", "evaluate my agent", "evaluate my ADK agent", "write an eval dataset", "analyze eval failures", "compare eval results", "optimize agent", or needs guidance on the Agent Platform eval methodology and the Quality Flywheel. Covers eval metrics, dataset…
Claude Code instructions for kenn-io/agentsview, a project described as: Local-first session search, analytics, insights, and token use statistics for coding agents, supporting Claude Code, Codex, and more than 20 other agents.
Agents emit structured events for every significant operation — RPC calls, state changes, schedule execution, workflow transitions, MCP connections, and more. These events are published to diagnostics channels and are silent by default (zero overhead when nobody is listening).
Use this skill when the user wants to benchmark an MLX-VLM change and present the numbers in a PR — fork-vs-main A/B comparisons, isolated-module micro-benchmarks, median-of-N timing with warmup, peak-memory reporting, correctness checks, parameter sweeps, and self-contained reproducible bench scripts to paste into a…
Use when a LoopX-managed goal runs, tracks, scores, or analyzes a benchmark experiment through benchmark-toolkit, including experiment-board rows, solver arms, integrity qualification, matched comparisons, or case insights. Do not use for casual benchmark discussion, ordinary software microbenchmarks, or eval mentions…
Running the Kokoro-FastAPI benchmark + transcription-roundtrip suites and regenerating the README plots. Use when asked to run/refresh benchmarks, RTF/first-token plots, transcription sanity checks, or the long-form baseline.
Visualise the result of an analysis as a chart (line, bar, area, scatter, etc.). Use when the user asks to "plot...", "chart...", "show me the trend of...", "visualise...", or when a numerical result has more than 10 rows and would be easier to read as a picture. Produces an image file plus the script that generated…
Answer a quantitative business question by writing a SQL query against the data warehouse, validating it, and presenting the result. Use when the user asks "how many...", "what's the trend of...", "compare X vs Y over...", "what's our top N...", or anything that resolves to a query against tabular data. Produces a…
Run a blocker-first post-experiment workflow: validate evidence, produce strict statistical analysis when possible, and generate a decision-oriented results report only when the analysis bundle is sufficient. Uses results-analysis + results-report as a gated two-stage workflow.
A step-by-step workflow for analysing Excel workbooks with multiple sheets. It covers reading data, cleaning it, filtering and combining results, and exporting reports, with a separate strategy for large files.
A workflow for analyzing very large Excel datasets, especially files with at least 10,000 rows. It uses lower-memory reading, streaming through rows, splitting work into chunks, and converting data to Parquet, a column-based data format suited to repeated analysis.
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