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概要と使いどころ
Post-call handling for a voice session — turn the transcript into a brain page, post the summary to the operator's messaging surface, archive the audio. The pipeline is the contract; the firing paths are operator-wired (see "Two firing paths" below for what ships today).
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Before fixing a slow/stale/timeout alert, measure the step yourself. Kill the theory with a stopwatch, not a code change. Measure-first ops triage for temporal alerts (stale, timeout, freshness, wedged, N hours behind) from gbrain doctor, autopilot, sync, and cron monitors — runs BEFORE any timeout raise, threshold change, or pipeline rewrite.
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Structured data research: search sources, extract structured data, archive raw sources, maintain canonical tracker pages, deduplicate. Parameterized via YAML recipes for investor updates, donations, company updates, or any email-to-structured-data pipeline.
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Ingest meeting transcripts from ANY meeting recorder into brain pages with attendee enrichment, entity propagation, and timeline merge. One unified pipeline: normalize the source into a standard transcript record, split multi-meeting recordings, resolve speakers by evidence, create the page, pass every surprising claim through the consistency check (transcript + brain + plausibility), enrich every entity, then run the verification checklist — substance AND sequence. A meeting is NOT fully ingested until the enrich skill has processed every entity AND the verification checklist passes, including the sequence verify (PASS or explicit user waive).
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When the user corrects a factual error, root-cause it immediately. Don't just note the correction — trace the error to its source, fix the source, and prevent recurrence. Every factual error is either a data error (bad brain page, bad memory file, bad rendered SOUL/USER identity, bad facts row) or a hallucination (LLM confabulated from partial signals).
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Compress an agent's routing file (RESOLVER.md or AGENTS.md) by converting granular skill-per-row tables into functional-area dispatchers. Each area lists sub-skills in a "(dispatcher for: ...)" clause. The LLM reads one area entry and routes to the correct sub-skill. Proven via held-out A/B eval: dispatcher pattern outperforms naive pipe-table compression.
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。
Unified Minions skill for both deterministic shell jobs and LLM subagent orchestration. Replaces the older `gbrain-jobs` routing intent. Use when: submitting gbrain jobs, shell/background tasks, spawning subagents, checking progress, steering running work, pausing/resuming, parallel fan-out. One durable, observable, steerable queue interface. Also carries the durable-execution doctrine for any operation expected to exceed ~2 minutes: capability ladder, deadman checks that verify the result was reported, and content-addressed stage checkpoints for expensive pipelines.
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。
Reference skill for Zoom AI Services Scribe. Use after routing to a transcription workflow when handling uploaded or stored media, Build-platform JWT auth, fast mode transcription, batch jobs, or transcript pipeline design.
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Full ICP-to-leads pipeline. Describe your ideal customer in plain English and get a ranked table of enriched decision-maker leads with emails and phone numbers.
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Runs the owner's Monday morning briefing as a two-link chain — business-pulse for the cross-connector snapshot of cash, sales, and pipeline, and report-builder for any saved KPI reports the owner has already defined — then reads the week ahead straight from Calendar and mail and merges all three parts into a single one-page Monday document, delivered per the owner's stored output preference. Requires no connectors and degrades gracefully, building from whatever of the ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books), Calendar, DocuSign, Expensify, Gmail or M365, HubSpot, PayPal, Ramp, RingEx Chat, Shopify, Slack, Stripe, and TikTok Ads is connected, or from pasted and uploaded data when none are. Trigger on "Monday brief," "start my week," "what do I need to know this week," "weekly briefing," "catch me up before Monday," or when the owner schedules a recurring start-of-week update.
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。
Scores inbound HubSpot leads by engagement signals, company fit, and urgency markers, ranks the ones worth a call today with talking points, drafts the follow-ups, and proposes calendar times for the owner to book. Use when the owner asks to prioritize, score, or rank leads, asks "who should I call first," or asks about their pipeline. An ask that wants the ranked leads turned into a call list with time blocked on the calendar routes to /call-list, which runs this skill first and then books the time.
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Answers one question for an SMB owner — is the growth engine actually working? Pulls sales trend by channel, funnel conversion, campaign return, best and worst performing products, and customer sentiment from whatever is connected, then ends with the three growth actions worth taking this week. The growth-side twin of business-pulse, which covers the finance side. Use this whenever the owner asks how marketing or sales is performing, whether a campaign paid off, where leads are coming from, why the pipeline is stalling, which products are moving, or says anything like "is my marketing working," "growth check," "how's the funnel," or "where should I put my money next month." Reach for it even when the owner names a single channel rather than growth overall.
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Read-only audit of your CRM opportunities for missing fields, stale dates, and stage mismatches - outputs a fix checklist to apply by hand or through update-opportunity. Use when the user asks "check my CRM hygiene", "check my Salesforce or HubSpot data", "clean up my CRM", "audit my opps", "what's missing in the CRM", or "clean up my pipeline".
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Produces a one-page cross-functional business snapshot for SMB owners — cash position (the ledger: MYOB, NetSuite, QuickBooks, Xero, or Zoho Books), sales trend (PayPal/Square/Stripe), pipeline movement (HubSpot), this week's commitments (Calendar), urgent watch-list items (Gmail or M365, Slack), and the single most important thing needing attention today. Proactively tries every available connector and gracefully scopes to whatever is connected — one connector gives a partial pulse; the full stack gives the full picture. Trigger when the user asks how the business is doing, wants a snapshot, a daily brief, a Friday recap, or says anything like "what am I missing" or "catch me up on the business." A start-of-week briefing routes to /monday-brief, which runs this skill as its first link.
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。
Leader's prep on a single rep before a 1:1 - their pipeline, recent activity, what they've been working on per chat and calendar, and where they might need help. Use when the user asks "prep for my 1:1 with [rep]" (a rep you manage), "how is [rep] doing", or "what's [rep] working on".
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Model what happens to your number if a deal slips, shrinks, or dies - quota impact, coverage ratio change, and the substitute pipeline needed to stay on plan. Use when the user asks "what if [deal] slips", "what happens if [account] pushes to next quarter", "can I still hit my number without [deal]", or "model losing [deal]".
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Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
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Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.
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Production pipeline for mathematical and technical animations using Manim Community Edition. Creates 3Blue1Brown-style explainer videos, algorithm visualizations, equation derivations, architecture diagrams, and data stories. Use when users request: animated explanations, math animations, concept visualizations, algorithm walkthroughs, technical explainers, 3Blue1Brown style videos, or any programmatic animation with geometric/mathematical content.
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Reference for writing a Workflow tool script (script API and gotchas, agent() options, pipeline() vs parallel(), verification and convergence patterns, resume, worked example). Load before authoring a script for a workflow the user already opted into; it does not itself authorize running one.
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This skill should be used when the user asks to "用 tmux 做真实测试", "保存 tmux 日志", "像真实用户一样测试 Qwen", "生成可复查的 TUI 测试报告", "测试 slash command 交互", or requests a tmux-based real user E2E run with complete readable logs. It guides real TUI usage with step-by-step capture-pane snapshots rather than ANSI raw pipe logs.
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Accessibility audit skill for scanning, fixing, and verifying WCAG 2.2 Level A and AA compliance across React, Next.js, Vue, Angular, Svelte, and plain HTML codebases. Use when auditing accessibility, fixing a11y violations, checking color contrast, generating compliance reports, or integrating accessibility checks into CI/CD pipelines.
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Creates demand generation campaigns, optimizes paid ad spend across LinkedIn, Google, and Meta, develops SEO strategies, and structures partnership programs. Use when planning demand gen strategy, growth marketing, advertising campaigns, PPC optimization, lead generation, pipeline generation, or marketing budgets. Covers multi-channel acquisition (Google Ads, LinkedIn Ads, Meta Ads), CAC analysis, MQL/SQL workflows, attribution modeling, technical SEO, and co-marketing partnerships. Default calibration profile is a Series A+ B2B SaaS scaling internationally (EU/US/Canada, hybrid PLG/Sales-Led) — adapt benchmarks for other stages and motions rather than skipping the skill.
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Use when the user asks to design a RAG pipeline, choose a chunking strategy or embedding model, pick a vector database, or evaluate retrieval quality (precision@k, recall@k, NDCG). Examples: 'design a RAG system for our docs', 'what chunk size should I use for this corpus', 'evaluate my retriever against ground truth'. NOT for general LLM cost tuning (use llm-cost-optimizer) or agent loops over retrieval (use agenthub).
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。