306 件 ・ 関連度順
概要と使いどころ
Screen job applications against requirements and score candidates objectively. Use when a user asks to review applications, evaluate candidates, screen resumes, rank applicants, assess qualifications against a job description, shortlist candidates, or build a hiring scorecard.
日本語の概要は準備中です。原文の説明を表示しています。
Build a candidate sourcing strategy and outreach pipeline. Use when the user says "where do I find candidates for this role", "build a sourcing plan", "write a cold outreach message", "we're not getting enough applicants", "our pipeline is dry", "help me find passive candidates", "write a LinkedIn message", or wants to increase the volume or quality of candidates in the funnel - even if they don't explicitly say "sourcing strategy". Also use when a recruiter needs to go beyond job postings to find talent.
日本語の概要は準備中です。原文の説明を表示しています。
TenorやGiphyのGIFを検索・プレビュー・保存するスキル。端末上で候補を選び、指定時点の静止画や複数フレームを並べたPNG画像も作れます。
自社の強みや顧客像を基に、比較すべき競合と調査先を選びます。直接競合・周辺競合・目標となる企業に分類し、根拠付きの候補一覧を作って詳しい比較の準備を整えます。
Periodic architecture survey — walks the module graph and reports ranked deepening candidates (shallow modules, hypothetical seams, logic behind the wrong seam). Survey, not rescue: it finds candidates and hands them to the captain; it never refactors on its own.
日本語の概要は準備中です。原文の説明を表示しています。
Token-hygiene audit of the always-loaded context stack — CLAUDE.md, AGENTS.md, auto-memory MEMORY.md, and the bootstrap-rendered identity files (SOUL.md, USER.md, ACCESS_POLICY.md, HEARTBEAT.md) or their harness equivalents. Finds redundancy, contradictions, stale content, compression candidates, and skill-extraction candidates; produces a ranked action list sorted by token savings with a risk class per finding. REPORT-ONLY: this skill never edits any audited file. Recommendations for bootstrap-rendered files target the interview answer bank / templates, never the rendered output. Judging routes through `gbrain eval cross-modal` (single cheap model by default; full multi-model panel is explicit opt-in).
日本語の概要は準備中です。原文の説明を表示しています。
Analyze Stockbee-style Day 1 Episodic Pivot candidates from earnings, guidance raises, M&A, FDA/regulatory approvals, analyst actions, major contracts, product launches, short-squeeze catalysts, or theme/story events. Scores catalyst quality together with gap/range expansion, volume shock, neglect/revaluation context, liquidity, and risk to the EP-day low. Use when the user asks for EP candidates, episodic pivots, Day 1 catalyst trades, game-changing news reactions, delayed EP watchlists, or handoffs into PEAD monitoring.
日本語の概要は準備中です。原文の説明を表示しています。
Build a Stockbee-style setup model book from momentum-burst screener candidates, then update 3-day and 5-day forward outcomes with MFE/MAE, stop-hit status, outcome tags, and cohort statistics. Use when the user wants to study Stockbee Momentum Burst examples, track failed candidates, build setup fluency, review A/B setup quality, or convert screener outputs into a learning loop rather than immediate trade signals.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation. **Trigger when user asks to:** - Migrate or convert PostgreSQL tables to hypertables - Execute hypertable migration with minimal downtime - Plan blue-green migration for large tables - Validate hypertable migration success - Configure compression after migration **Prerequisites:** Tables already identified as candidates (use find-hypertable-candidates first if needed) **Keywords:** migrate to hypertable, convert table, Timescale, TimescaleDB, blue-green migration, in-place conversion, create_hypertable, migration validation, compression setup Step-by-step migration planning including: partition column selection, chunk interval calculation, PK/constraint handling, migration execution (in-place vs blue-green), and performance validation queries.
日本語の概要は準備中です。原文の説明を表示しています。
Build boolean and X-ray search strings to source candidates. Use when asked to build a boolean search, source candidates on LinkedIn/Google, write an X-ray search, or find people with specific skills. Produces ready-to-paste boolean strings (with synonyms, must-haves, and exclusions), X-ray variants for LinkedIn/GitHub, and a refinement plan to widen or narrow the result set.
日本語の概要は準備中です。原文の説明を表示しています。
Search Ashby public job boards (no key needed) and read/write the Ashby ATS: candidates, jobs, applications. Trigger phrases: ashby, job board, candidates, hiring, job search.
日本語の概要は準備中です。原文の説明を表示しています。
Detect Insecure Direct Object Reference (IDOR) vulnerabilities in a codebase using a three-phase approach: recon (find candidates), batched verify (check authorization in parallel subagents, 3 candidates each), and merge (consolidate batch results). Checks endpoints for missing ownership or authorization checks on user-supplied identifiers. Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/idor-results.md. Use when asked to find IDOR or authorization bypass bugs.
日本語の概要は準備中です。原文の説明を表示しています。
Detect hardcoded sensitive data (API keys, access tokens, private keys, passwords, etc.) in publicly accessible code — frontend JavaScript, mobile apps, client-side bundles, and HTML templates. Uses a three-phase approach: recon (find secret candidates), batched verify (confirm real secrets in public code paths, 3 candidates each), and merge (consolidate batch results). Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/hardcodedsecrets-results.md. Use when asked to find hardcoded secrets, leaked API keys, or exposed credentials.
日本語の概要は準備中です。原文の説明を表示しています。
Given a Japanese NLP GitHub repo/model/dataset (URL / owner/repo / tool name) OR a topic, find what's already in awesome-japanese-nlp-resources and discover related resources NOT yet listed (contribution candidates). Mines the bundled dataset, then expands via web research across GitHub and Hugging Face. Use when the user names a SPECIFIC repository, model, or tool and wants alternatives/equivalents, OR wants to discover Japanese NLP resources for a topic that are NOT yet in the list, OR wants to prepare a contribution. Trigger phrases include 'mecabに似たツール', 'fugashiの代替', 'alternatives to fugashi', 'repos like manga-ocr', 'what else is like sudachi', 'リストに無い新しい日本語NLP', 'awesome-japanese-nlpに追加できそうな', '最近公開された日本語NLPツール', 'find unlisted Japanese NLP repos', 'new Japanese models on Hugging Face', 'contribute a new resource'. For a simple lookup of what already exists, use the search skill instead.
Compare several Japanese NLP libraries, models, or datasets for a keyword (a specific tool name, or a function/task like '形態素解析') across a handful of criteria chosen for that comparison, rendered as a ○/△/✕ table. Use when the user wants a side-by-side comparison of multiple Japanese NLP tools/libraries/datasets, not just the single best one. Trigger phrases include 'X と Y と Z を比較して', '形態素解析ライブラリを比較', 'MeCab と Sudachi どっちがいい', 'どのツールを使うべき', 'compare japanese tokenizers', 'mecab vs sudachi vs janome', 'which embedding model should I use', '日本語NERライブラリの比較表', 'pros and cons of japanese OCR tools'. For a single ranked list use search; for alternatives to one specific tool (or contribution candidates) without a multi-axis table, use discover.
Generate, evaluate, and narrow brand concepts during early ideation including positioning territories, naming candidates, mood directions, and narrative angles. Use this skill whenever the user is in the early phase of brand creation, exploring brand directions, brainstorming names, building moodboards, generating positioning options, or trying to choose between multiple brand directions. Triggers on brand ideation, brand concept, naming, brand name, name candidates, positioning, brand positioning, mood board, brand directions, exploring brands, early brand work, brand exploration, brand brainstorm, brand options. Also triggers when the user has multiple half-formed brand ideas and needs help converging on one, even if they do not say 'ideation' explicitly.
日本語の概要は準備中です。原文の説明を表示しています。
This package aims to integrate GWAS-derived SNPs and coexpression networks to mine candidate genes associated with a particular phenotype. For that, users must define a set of guide genes, which are known genes involved in the studied phenotype. Additionally, the mined candidates can be given a score that favor candidates that are hubs and/or transcription factors. The scores can then be used to rank and select the top n most promising genes for downstream experiments.
日本語の概要は準備中です。原文の説明を表示しています。
Intent-driven knowledge-store and Run knowledge lifecycle management — read-only audit/prune reports, stage candidates (with signal recording), review/resolve/promote candidates, harvest artifacts, or manage wiki/domain knowledge.
日本語の概要は準備中です。原文の説明を表示しています。
HTTP/HTTPS application-layer reconnaissance after a web service is identified: fingerprinting, content/API discovery, JS/source maps, proxy/cache topology, WAF, CMS, and known CVE/PoC candidates. Recon only — do not exploit, write files, or obtain a shell. Hand CVE candidates and attack surface to the operator, who may select /web-attack. Non-HTTP ports belong to /recon.
日本語の概要は準備中です。原文の説明を表示しています。
Batch Resume Screening — score and rank candidates 0-100 against job requirements, flag red flags (job hopping, gaps, skill mismatches), output Pass/Phone Screen/Skip recommendation per candidate
日本語の概要は準備中です。原文の説明を表示しています。
Head-to-Head Candidate Comparison — two candidates side-by-side across skills, experience, culture fit, salary expectations, references, hire-or-no-hire recommendation
日本語の概要は準備中です。原文の説明を表示しています。
Phase 2B of the Gaia evidence verification pipeline. Verifies benchmark-result rows and benchmark-source candidates against registry/benchmark-sources.json before adversarial audit, link validation, catalog promotion, or ingestion. Use when running /ev-pipeline, checking benchmark-result evidence, reviewing benchmark-source candidates, or preparing benchmark rows for /gaia-ingest-batch.
日本語の概要は準備中です。原文の説明を表示しています。
Build a structured interview scorecard for a role. Use when the user says "create an interview scorecard", "build an interview guide", "what questions should I ask for this role", "help me evaluate candidates consistently", "design the interview process", or wants to assess candidates against defined criteria - even if they don't explicitly say "scorecard". Also use when a recruiter or hiring manager wants to prevent gut-feel hiring.
日本語の概要は準備中です。原文の説明を表示しています。
Search Ashby public job boards (no key needed) and read/write the Ashby ATS: candidates, jobs, applications. Trigger phrases: ashby, job board, candidates, hiring, job search.
日本語の概要は準備中です。原文の説明を表示しています。