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概要と使いどころ
Simulates lithium-ion battery charge, discharge and rest experiments with PyBaMM, records parameter-set provenance, checks mesh and solver sensitivity, and compares predicted voltage curves with measured cycling data. Use for SPM or DFN electrochemical battery modeling, C-rate protocols, voltage cutoffs, parameter studies and numerical validation of battery simulations.
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。
Conducts systematic, scoping, and narrative literature reviews using PubMed, arXiv, bioRxiv, Semantic Scholar, and other appropriate sources. Use for research synthesis, reproducible literature searches, screening, citation checking, or preparing Markdown and PDF reviews. Tracks search coverage, records versus studies, and evidence limitations; supports meta-analysis planning but does not supply a meta-analysis engine.
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。
Searches and reads biomedical papers, FDA/PMDA/EMA documents, clinical trials, and protein records with the GXL Paperclip CLI and Python SDK. Supports source-scoped search, full-text grep, metadata SQL, map/reduce extraction, figure analysis, optional repositories and claim verification, and line-pinned citations. Use when a task names GXL paperclip, asks to install or authenticate it, or requests literature retrieval and evidence extraction through Paperclip.
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。
Converts genomic intervals between coordinate conventions, normalises and compares variant representations, and detects assembly or contig-naming mismatches before they corrupt an analysis. Used whenever coordinates cross a format, tool, or assembly boundary - converting between BED, GFF/GTF, VCF, SAM/BAM, WIG, PSL, genePred, Picard interval_list, or region strings; reconciling 0-based half-open with 1-based inclusive; left-aligning or trimming indels; checking whether two variant records describe the same change; mapping genomic to transcript, CDS, or protein positions; auditing a BED/GTF/VCF for convention violations; or diagnosing GRCh37 vs hg19 vs GRCh38 vs T2T, chr-prefix, and liftover problems. Triggers include "off by one", "0-based", "1-based", "half-open", "coordinate system", "left-align", "normalize variant", "bcftools norm", "chr prefix", "wrong genome build", "liftover", "REF mismatch", and "HGVS".
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。
Review changed code against project standards. Checks for missing tests, dead code, type safety, lint issues, and coding conventions. Run after completing any implementation work.
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。
Create, revise, audit, and export manuscript scientific figures in Python or R. Use for 论文配图、科研绘图、多面板图 and submission-ready plots, or explicitly requested AI-generated graphical abstracts and mechanism schematics. Not for interactive dashboards, data cleaning, or statistics-only analysis.
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 when querying or extending GitNexus's PDG control/data-dependence surface (the `pdg_query` MCP tool, CDG/REACHING_DEF edges), or reasoning about "what controls X" / "where does Y flow" / guard clauses. Examples: "what guards this statement?", "trace this variable within the function", "why is the pdg_query result empty?", "add a CDG query".
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。
Writes and edits Markdown without inserting manual hard line breaks at a fixed column (e.g. 72 or 80). GitHub, docs sites, and editors soft-wrap prose; artificial breaks make diffs noisy and reflow awkward. Use when creating or editing .md files, PR bodies, ADRs, READMEs, rulecards, or any Markdown where the user cares about readable source and clean diffs — or when the user asks to avoid "hard wrapping" or "80-column" line breaks in prose.
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。
Review one or more ADRs with fresh eyes (as a team member without prior context), identify narrative and structural issues, then rewrite them. Use when the user asks to review, improve, rewrite, or take a fresh-eyes pass on an ADR or a set of ADRs (Architecture Decision Records).
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 when an agent, harness, gateway, MCP workflow, or multi-step automation claims completion and the available traces, checkpoints, approvals, tool calls, or deployment records must be judged without trusting self-reported success.
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。
Capture and retrieve durable YYLO Ledger artifact Records with intentional profiles, payload modes, provenance, retention, and secret-safe immutable evidence.
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。
Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto.
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。
Automate Airtable tasks via Rube MCP (Composio): records, bases, tables, fields, views. Always search tools first for current schemas.
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。
Dashboard metric register: metric, source module, formula, period, value, target, trend, owner and last-updated, as CSV, SQL, JSON Schema or Notion on request. Use for KPI dashboards.
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。
Rules for when an agent should recall from long-term memory before acting and when it should save decisions, corrections and failures afterwards. Works with any memory backend.
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。
Assists with job applications: evaluating job postings, tailoring CVs, writing cover letters, and preparing for interviews. Triggers on keywords like: job posting, job application, CV, cover letter, resume, interview prep, job fit, career, application, apply, ansøgning, stilling
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。
Finds new job postings matching your profile via installed portal-search CLIs (LinkedIn, local job boards, and any skills added with /add-portal). Deduplicates across runs. Triggers on: job scrape, find jobs, search jobs, new jobs, job search, scrape jobs, /scrape
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。
Crypto market leaderboards — ranked aggregate feeds across the whole market: social-hype/sentiment rank, trending / top-search / Binance Alpha / tokenized-stocks rank, smart-money net-inflow rank (which tokens received the most inflow), top meme tokens by breakout score on Pulse launchpad, top trader PnL leaderboard (ALL / KOL). Use whenever the user wants a ranked list of tokens or addresses by some metric — phrases like "trending tokens", "top N by hype", "leaderboard", "rank by X", "biggest inflows", "top traders this week".
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 when the user mentions connect/disconnect wallet, sign in, sign out, web3 wallet, wallet address, check balance, how much crypto do I have, send BNB/USDT/crypto, transfer tokens, swap tokens, buy/sell token, DEX trade, limit order, market order, cancel order, get a quote, transaction history, wallet settings, daily limit, slippage, MEV protection, supported chains, available networks, prediction market, predict.fun, YES/NO market, place a prediction, redeem winnings, claim payout, prediction portfolio, prediction PnL, x402 payment, HTTP 402 Payment Required, pay a known x402 API, check approvals, view token approvals, revoke approval, manage approvals, wallet approvals, authorization management, token authorization, DeFi protocols, DeFi position, DeFi portfolio, staking, liquidity pool, LP, yield farming, health factor, APY, TVL, DeFi investment, DeFi deposit, DeFi redeem, DeFi stake, DeFi unstake, add liquidity, remove liquidity, claim rewards, claim fees, or any on-chain wallet operation.
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。
Runs the job-search pipeline. Adds jobs from a link or pasted posting, keeps stages and dates accurate, sets follow-ups, runs a weekly review (stale applications, next actions, funnel stats), records the exact resume version sent, and drafts networking, LinkedIn, referral, recruiter-reply, follow-up, thank-you, status-check, expedite and withdrawal messages without sending them. Use when the user says "add this job to my tracker", "I applied", "log my interview", "I got rejected", "I was ghosted", "log my offer", "track my applications", "do my weekly review", "what should I follow up on", "write a thank-you note", "message this recruiter", "ask for a referral", "withdraw my application", or wants a job-search spreadsheet. Works with a Markdown/CSV tracker or Reactive Resume MCP tools. Not for resume work (resume-tailor, resume-bullet-writer, resume-content-guide), interview prep or practice (interview-prep, mock-interview), or judging, negotiating, accepting or declining an offer (offer-negotiation).
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。
Tailors one resume to one specific job posting. Builds a must-have/nice-to-have requirement table, an evidence map, gap questions instead of invention, a tailored copy (headline, summary, bullet order, skills), honest ATS guidance, and an optional cover letter for that posting. Use when the user says "tailor my resume to this job", "customize my CV for this posting", "match my resume to this job description", "what keywords am I missing", "make my resume ATS-friendly for this role", "will this get past the ATS", "the job asks for X and I don't have it", or pastes a job ad with a resume. Works from pasted text; uses Reactive Resume MCP tools when connected. Not for bullets with no target job (resume-bullet-writer), what belongs on a resume or a general audit (resume-content-guide), tracking or follow-ups (job-application-manager), or interview prep and practice (interview-prep, mock-interview).
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。
Creates task-oriented technical documentation with progressive disclosure. Use when writing READMEs, API docs, architecture docs, or markdown documentation. Also use this skill at the END of any completed reverse engineering, penetration testing, CTF, or security analysis task to generate a formal report in the user's project directory. Trigger keywords: 写报告, 写文档, 出报告, writeup, 技术文档, report, documentation.
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。
Track, calculate, and optimize key performance metrics for SaaS, marketplace, consumer, and B2B startups from seed through Series A, including unit economics, growth efficiency, and cash management. Use this skill when defining a metrics framework, calculating CAC/LTV/burn multiple, benchmarking business health, or preparing metrics dashboards for investors or board reporting.
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。
Train reasoning and verifiable-task behavior with GRPO and reinforcement learning from verifiable rewards (RLVR). Use when task success is algorithmically checkable (math, code, tool calls, structured output), when designing GRPO reward functions, or when a GRPO run diverges or reward-hacks.
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。