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ScientiaCapital
GitHub クリエイタープロフィール

ScientiaCapital

5 件の GitHub リポジトリにある 99 件の収集済み skills をリポジトリ単位で表示します。

収集済み skills
99
リポジトリ
5
更新
2026-07-12
リポジトリエクスプローラー

リポジトリと代表的な skills

gtm-brain-skill
市場調査アナリスト・マーケティングスペシャリスト

Relationship intelligence graph for GTM work. Reads and writes Contact/Account/Deal/Outcome nodes in Neo4j Aura. Syncs contacts/accounts from HubSpot MCP and imports call outcomes from Nooks MCP automatically. Surfaces what messaging, sequences, and personas worked across verticals. Use when: relationship graph, gtm brain, log outcome, what worked with, account map, sync from hubspot, import nooks calls, contact history, sequence performance.

2026-07-03
business-pulse
市場調査アナリスト・マーケティングスペシャリスト

Live firm-wide sales pulse from the Epiphan CRM — revenue vs pace, pipeline by stage, won/lost, BDR activity, with coaching takeaways. Use when: business pulse, how are we doing, pipeline health, revenue pace, weekly numbers, standup brief, are we on track, sales snapshot.

2026-07-03
dead-deal-recovery
プロジェクト管理専門家

Identify stalled and dying deals, run structured disqualification or recovery workflows, and clean pipeline of dead weight. Finds deals stuck 60+ days, with no champion, no activity, or overdue close dates and prescribes either a final re-engagement campaign or formal disqualification. Keeps pipeline honest and frees mental bandwidth. Use when: 'dead deals', 'stalled deals', 'clean pipeline', 'pipeline cleanup', 'disqualify deals', 'deal graveyard', 'zombie deals', 'which deals should I kill', 'pipeline hygiene', 'deal recovery', 'win back', 'lost deal review', 'close out stale deals'.

2026-07-03
orlob-discovery-framework
プロジェクト管理専門家

Chris Orlob's 5-step discovery framework applied to Epiphan — business problem → cause analysis → negative impact → future state → close. Per-vertical root causes, calibrated question bank, MEDDIC integration, and the 0-3 discovery scoring rubric used by sdr-call-coaching. Use when: discovery framework, orlob discovery, discovery questions, cause analysis, business problem discovery, how to run discovery.

2026-07-03
sales-revenue
卸売・製造業営業担当者(技術・科学製品除く)

Epiphan Video B2B sales - video capture/streaming lead qualification, pipeline metrics, MEDDIC discovery, and demo execution for Pearl devices, EC20 PTZ, and Epiphan Connect. Use for lead scoring, cold outreach to Higher Ed/Government/Corporate AV, and pipeline reviews.

2026-07-03
sdr-email-sms-playbook
サービス営業代表(広告・保険・金融・旅行除く)

5-touch email/SMS cadence for Epiphan BDR outreach. Full templates for Higher Ed, Courts, Gov, Healthcare by touch. Companion to epiphan-call-playbook (phone). Writing rules, subject line patterns, and SMS guidelines. Use when: email playbook, email template, outreach email, sdr email, cold email template, sms outreach, follow up email, 5 touch cadence.

2026-07-03
batch-send-drafts
サービス営業代表(広告・保険・金融・旅行除く)

Review and batch-send staged Gmail BDR outreach drafts. Lists all queued drafts, shows a prioritized review table (recipient / subject / vertical), and generates one-click Gmail send links so Tim can flush the outreach queue in under 2 minutes. Trigger: send staged drafts, flush draft queue, batch send drafts, review my drafts, what drafts do I have.

2026-07-03
weekly-kpi-report
市場調査アナリスト・マーケティングスペシャリスト

Friday 5:30 PM CST weekly KPI report for Tim's SDR pod (Tim Kipper, Edgar Marroquin, Vasil Ivanov). Pulls each rep's real Nooks dials/connects + HubSpot meetings-booked and pipeline-created for Mon 00:00 → Fri 5:30 PM CST, calculates connect rate + attainment vs weekly targets, builds color-coded per-rep scorecards (GREEN/YELLOW/RED) with week-over-week trend arrows, rolls up a team totals row, and delivers an HTML report + a compact Slack DM to Tim. Read-only everywhere except the Slack DM and the local HTML/prev-week JSON. This task MEASURES the reps; sdr-call-coaching (Fridays 11 AM) COACHES them — do not duplicate the coaching rubric here.

2026-07-03
このリポジトリの収集済み skills 85 件中、上位 8 件を表示しています。
adaptive-workflows
ソフトウェア開発者

Self-learning workflow system that tracks what works best for your use cases. Records experiment results, suggests optimizations, creates custom templates, and builds a personal knowledge base. Use to learn from experience and optimize your LLM workflows over time.

2025-11-07
dataset-engineering
データサイエンティスト

Create, clean, and optimize datasets for LLM fine-tuning. Covers formats (Alpaca, ShareGPT, ChatML), synthetic data generation, quality assessment, and augmentation. Use when preparing data for training.

2025-11-07
model-deployment
ソフトウェア開発者

Export and deploy fine-tuned models to production. Covers GGUF/Ollama, vLLM, HuggingFace Hub, Docker, quantization, and platform selection. Use after fine-tuning when you need to deploy models efficiently.

2025-11-07
training-optimization
データサイエンティスト

Advanced techniques for optimizing LLM fine-tuning. Covers learning rates, LoRA configuration, batch sizes, gradient strategies, hyperparameter tuning, and monitoring. Use when fine-tuning models for best performance.

2025-11-07
superbpe
データサイエンティスト

Train and use SuperBPE tokenizers for 20-33% token reduction across any project. Covers training, optimization, validation, and integration with any LLM framework. Use when you need efficient tokenization, want to reduce API costs, or maximize context windows.

2025-11-07
unsloth-tokenizer
ソフトウェア開発者

Analyze, compare, and work with tokenizers using Unsloth tools. Compare different tokenizers, analyze token efficiency, and integrate with Unsloth models. For SuperBPE training, see the 'superbpe' skill.

2025-11-07
unsloth-finetuning
データサイエンティスト

Fine-tune LLMs 2x faster with 80% less memory using Unsloth. Use when the user wants to fine-tune models like Llama, Mistral, Phi, or Gemma. Handles model loading, LoRA configuration, training, and model export.

2025-11-07
unsloth-mcp-server
ソフトウェア開発者

Work with the Unsloth MCP Server codebase. Use when maintaining, extending, or debugging this specific MCP server implementation. Provides architecture knowledge, code patterns, and development workflows.

2025-11-07
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