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Found 202 Skills
Explore candidate solutions before committing. Use when you have a problem statement and need to evaluate approaches - band-aid, optimize, reframe, or redesign.
Audit your LaunchDarkly feature flags to understand the landscape, find stale or launched flags, and assess removal readiness. Use when the user asks about flag debt, stale flags, cleanup candidates, flag health, or wants to understand their flag inventory.
Generate ultra-compact commit messages. Follows the Conventional Commits format with subject ≤50 characters, prioritizing "why" over "what". Supports both Japanese and English. Trigger with "Make a commit message", "/commit", or "/genshijin-commit". Auto-trigger candidate when staging changes.
Executes full-project QA like a real user by discovering the repository verification contract, running build, lint, test, and startup commands, exercising core workflows end-to-end, creating realistic fixtures when needed, fixing root-cause regressions, and rerunning the full gate. Use when validating a branch, release candidate, migration, refactor, or risky commit. Do not use for static code review only, one-off unit test edits, or architecture brainstorming without execution.
Use this skill to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables. **Trigger when user asks to:** - Analyze database tables for hypertable conversion potential - Identify time-series or event tables in an existing schema - Evaluate if a table would benefit from Timescale/TimescaleDB - Audit PostgreSQL tables for migration to Timescale/TimescaleDB/TigerData - Score or rank tables for hypertable candidacy **Keywords:** hypertable candidate, table analysis, migration assessment, Timescale, TimescaleDB, time-series detection, insert-heavy tables, event logs, audit tables Provides SQL queries to analyze table statistics, index patterns, and query patterns. Includes scoring criteria (8+ points = good candidate) and pattern recognition for IoT, events, transactions, and sequential data.
Analyze, prioritize, and document test cases in TMS (Jira/Xray) -- the bridge between manual QA and test automation. Use when creating Test/ATP/ATR artifacts, calculating ROI to choose which tests to automate, maintaining US-ATP-ATR-TC traceability, or repairing broken TMS links. Supports four scopes: module-driven (exhaustive module exploration), ticket-driven (QA-approved user story), bug-driven (regression TC for a closed bug), and ad-hoc/exploratory. Produces three outcomes per TC: Candidate (feeds test-automation), Manual (terminal), Deferred (terminal). Triggers on: document tests, create test cases in Jira/Xray, prioritize for automation, ROI analysis, which tests to automate, Candidate vs Manual, link ATP to ATR, fix TMS traceability, stage 4, turn this bug into a regression test. Do NOT use for writing test code (test-automation) or running suites (regression-testing).
Score, grade, or evaluate things using AI against a rubric. Use when grading essays, scoring code reviews, rating candidate responses, auditing support quality, evaluating compliance, building a quality rubric, running QA checks against criteria, assessing performance, rating content quality, or any task where you need numeric scores with justifications — not just categories.
Build the optimal 250-byte Amazon backend search term string. Collects every candidate keyword, removes anything already indexed in the title and bullets, strips duplicates and Amazon-prohibited terms, prioritizes by search value, and packs the field to the byte limit. Use when a user asks about backend keywords, search terms, the hidden keyword field, generic keywords, "Search Terms" in the listing back end, or wants to fix wasted backend space. Trigger phrases: "backend keywords", "search terms field", "hidden keywords", "250 bytes", "generic keywords". Works with zero tools. the user pastes the current title, bullets, and any keyword list.
MoltOffer candidate agent. Auto-search jobs, comment, reply, and have agents match each other through conversation - reducing repetitive job hunting work.
Use when reranking search candidates is needed with Alibaba Cloud Model Studio rerank models, including hybrid retrieval, top-k refinement, and multilingual relevance sorting.
Guides competitive idea generation and ranking using tree-structured search (up to N_I=21 candidates across technique/domain/formulation axes) and Elo tournaments (4 dimensions: novelty, feasibility, relevance, clarity). Produces a ranked direction summary and full research proposal. Use when: user has a research direction and needs concrete ranked ideas, wants to compare multiple approaches, or mentions 'rank ideas', 'compare approaches', 'which idea is best', 'research proposal'. Do NOT use for finding a research direction from scratch (use research-ideation) or planning the paper itself (use paper-planning).
A qualitative research assistant tool based on Braun & Clarke's Reflexive Thematic Analysis framework. Supports two input modes: (1) Provide raw interview text directly → The skill completes initial TA coding for each document, then proceeds to theme identification after summarization; (2) Provide existing initial coding pool → Directly enter the process of clustering, review, and naming suggestions. Outputs a structured candidate theme table, clearly marking codes with ambiguous boundaries and naming suggestions to be decided by researchers. This skill is triggered when users mention terms such as "thematic analysis", "theme coding", "help me cluster codes", "extract themes from codes", "Braun Clarke", "candidate themes", "how to categorize these codes into themes", "help me check the theme structure", "conduct thematic analysis on interviews". Note the difference from grounded-coding: grounded-coding focuses on category construction and theoretical relationships for procedural grounded theory; thematic-analysis focuses on semantic theme identification following the Braun & Clarke approach, outputting theme structures rather than theoretical propositions.