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Found 137 Skills
Write the listing-content rulebook for the operator of a B2B SaaS app marketplace - the standard every third-party listing must meet, covering required fields with objective reject criteria, screenshot and video specs, description quality bars, category taxonomy design, localization posture, badge display rules, and staleness/decay enforcement. Use whenever the user mentions listing standards, a listing rubric or marketplace quality bar, screenshot or media requirements, marketplace categories, rejection criteria, or delisting stale listings - even if they never say "listing standards". Operator side only. Do NOT use for optimizing a vendor's own listing - use samber/developer-platform-skills@integration-listing-optimization instead.
Filing gate for ALL brain writes. Consulted before creating any new brain page to determine the correct path. Reads the ACTIVE schema pack via `gbrain schema show --json` — no hardcoded directory table. Also runs periodic taxonomy drift detection via `gbrain schema review-orphans`.
Apply when writing or reviewing a spec, requirements, acceptance criteria, user stories, or tickets, before any code exists. Two passes: an edge probe over a closed taxonomy of data-shape boundaries, and a prohibition probe for what the feature must never silently become. Every raised item is resolved, dismissed with a reason, or carried as an explicit gap.
Systematic web application QA testing with structured issue taxonomy, health scoring, and regression tracking. Use this skill when the user asks for QA testing, systematic testing, smoke testing, regression testing, web app testing, browser testing, or says "QA this", "test the app", "smoke test", "run QA", "systematic test", "check the site", "regression test", "full QA", "/qa-systematic". Supports full, quick, and regression modes.
Guide a focused CS or AI literature review sprint that turns a topic, idea, claim, or project direction into a ranked paper map, closest-work risk assessment, method taxonomy, novelty implications, baseline implications, and next actions. Use this skill whenever the user needs to survey a topic, check novelty, map related work, prepare a project, find canonical or recent papers, decide read/skim/ignore priority, or turn papers into a research direction.
Use when planning, running, or learning from chaos engineering experiments. Triggers on "chaos experiment", "fault injection", "gameday", "resilience test", "blast radius", "steady state", "abort criteria", "Chaos Toolkit", "Chaos Mesh", "Litmus", "Gremlin", "AWS FIS", or any deliberate failure-injection question. Ships experiment designer, blast-radius calculator, and postmortem generator (all stdlib Python), 4 references on chaos principles + experiment design + attack taxonomy + tooling landscape, and a /chaos-experiment slash command. Composes with feature-flags-architect (kill switches as abort triggers) and kubernetes-operator (common chaos targets).
Read an academic paper end to end and extract professional research insights, figures, metadata, and critique. Use this skill whenever the user shares a scientific paper, review paper, survey paper, systematic review, meta-analysis, scoping review, arXiv link, DOI, PDF, or pasted paper text and asks to read, summarize, analyze, extract, digest, review, critique, or explain it. For original research papers, produce a modified Heilmeier analysis. For review literature, produce a field-map extraction covering scope, taxonomy, evidence quality, consensus, controversies, gaps, and future directions. Do NOT use this skill for non-academic articles, blog posts, or news.
Audit a skill repository or installed skill collection for global consistency, lifecycle coverage, routing quality, documentation drift, memory writeback coverage, stale future-skill references, broken helper paths, and validation readiness. Use this skill whenever the user asks for a global consistency audit, skill taxonomy review, lifecycle audit, cross-skill routing audit, README or AGENTS inventory consistency check, or maintenance pass over a collection of agent skills.
Deep formal test smell audit based on academic research taxonomy (testsmells.org). Detects 19 categorized smell types — conditional logic, mystery guests, sensitive equality, eager tests, and more — with calibrated severity and research-backed remediation. Use for comprehensive test suite health assessments. For a quick pragmatic review, use test-anti-patterns instead. DO NOT USE FOR: writing new tests (use writing-mstest-tests), evaluating assertion quality specifically (use assertion-quality), or finding test duplication and boilerplate (use exp-test-maintainability).
Analyze a user's Plannotator plan archive to extract denial patterns, feedback taxonomy, evolution over time, and actionable prompt improvements — then produce a polished HTML dashboard report. Falls back to Claude Code ExitPlanMode denial reasons when Plannotator data is unavailable.
Analyze user/customer feedback and produce a User Feedback Analysis Pack (source inventory, normalized feedback table, taxonomy/codebook, themes + evidence, recommendations, and feedback loop). Use for voice of customer, feature request analysis, support ticket synthesis, churn reason synthesis, and survey open-ends.
Generate a custom trace annotation web app for open coding during LLM error analysis. Use when the user wants to review LLM traces, annotate failures with freeform comments, and do first-pass qualitative labeling (open coding). Also use when the user mentions "annotate traces", "trace review tool", "open coding tool", "label traces", "build an annotation interface", "review LLM outputs", or wants to manually inspect pipeline traces before building a failure taxonomy. This skill produces a tailored Python web application using FastHTML, TailwindCSS, and HTMX.