Loading...
Loading...
Found 35 Skills
Create institutional-grade financial documents from templates. Handles analysis reports, buy tickets, compliance memos, Excel model specs, presentations, and onboarding reports.
Create and update pitch decks, one-pagers, investor memos, accelerator applications, financial models, and fundraising materials. Use when the user needs investor-facing documents, projections, use-of-funds tables, milestone plans, or materials that must stay internally consistent across multiple fundraising assets.
Use this skill any time the user wants financial analysis, earnings research, or investment-related reports. This includes: earnings call summaries, quarterly financial analysis, stock research, equity research reports, financial due diligence, company valuations, DCF models, balance sheet analysis, income statement breakdowns, cash flow analysis, SEC filing summaries, investor memos, portfolio analysis, IPO analysis, M&A research, and credit analysis. Also trigger when: user says 分析财报, 做个估值, 股票研究, 财务尽调, 现金流分析, 收入分析, 季度财务分析. If financial research or analysis is needed, use this skill.
Produces executive-quality strategic documents in The Economist/HBR style. Use when writing strategy memos, market analysis, business cases, customer research reports, or any document for Product, Design, and Business leaders. Customer-led, evidence-based, narrative-driven.
Guides research engineering and science on LLM tokens—hypotheses about context use, tokenization, compression, and inference efficiency; rigorous benchmarks (tokens per task, quality–cost Pareto); ablation design; instrumentation and reproducible logs; and research memos that inform product decisions. Use when designing token-efficiency experiments, measuring context utilization, comparing compression or routing methods, analyzing tokenizer effects, or writing technical reports on token/cost trade-offs—not for phased cost roadmaps and owners (ai-token-improvement-plan-engineer), production context pipeline implementation (ai-context-engineer), single-prompt edits (prompt-engineer), general non-token AI research (ai-researcher), or shipping features (ai-engineer).
Guides privacy research engineering for safeguards—PII and sensitive-data detection research, redaction and de-identification evals, memorization and extraction risk studies, privacy benchmarks and labeled corpora, logging/retention minimization for safety pipelines, and research memos on privacy–utility trade-offs for guardrail systems. Use when measuring PII detector quality, designing privacy eval suites for moderation stacks, studying training-data leakage or prompt logging risk, or recommending privacy mitigations for safeguard models—not for SOC 2/GDPR evidence automation (compliance-engineer), legal DPIA or AI policy (ai-risk-governance), harm/toxicity classifier R&D (ml-research-engineer-safeguards), production inference gateways (ml-infrastructure-engineer-safeguards), or general non-privacy research (ai-researcher).
Drafts executive memos and stakeholder communications using Amazon's 6-pager structure, Stripe's memo format, and SCQA framework. Use when writing board updates, executive summaries, or strategic documents.
Stakeholder Communication (The Diplomat): Prepare context-aware communications (Emails, Updates, Memos) using project data.
SCPR (Situation-Complication-Problem-Recommendation) framework for structured problem solving and executive communication. Use when users need to structure strategic arguments, analyze business situations, create executive summaries, or develop clear problem statements using McKinsey-style communication. Apply when structuring recommendations, writing memos, or organizing strategic thinking.
Convert flomo memos from local desktop auth/API into grouped Markdown files for AI/NotebookLM reading, plus human-readable Markdown tag statistics with tree totals. Use when a user asks to export flomo notes to Markdown, split memos by month/quarter/year, generate NotebookLM-friendly archives, or produce flomo tag counts/aggregation.
Cleft Notes platform help — Apple-native AI voice-to-notes app with on-device transcription that turns spoken thoughts into organized markdown notes with auto-headings. Use when setting up Cleft Notes for capturing voice memos and converting rambling thoughts into structured notes, configuring Obsidian or Notion sync to route Cleft notes into an existing knowledge base, troubleshooting recordings that fail after a couple minutes or produce garbled transcription output, setting up Zapier automations to send Cleft notes to project management or CRM tools, choosing between Cleft free and Plus plans, deciding whether Cleft or Voicenotes or AudioPen fits your voice capture workflow, or evaluating Cleft for ADHD-friendly voice-first note-taking on Apple devices. Do NOT use for comparing AI meeting note-takers across platforms (use /sales-note-taker) or reviewing a sales call for coaching (use /sales-call-review).
Guides ML/research engineering for safeguards—safety classifier development, harm benchmarks and eval suites, labeled dataset design, fine-tuning and ablations, calibration and slice analysis, attack-surface research memos, and promotion criteria for new moderation models. Use when building or evaluating guardrail models, designing safety benchmarks, measuring precision/recall on policy categories, comparing mitigation techniques, or writing research reports on classifier improvements—not for production inference gateways (ml-infrastructure-engineer-safeguards), PII/leakage privacy research (privacy-research-engineer-safeguards), red-team attack campaigns (ai-redteam), AI governance policy (ai-risk-governance), general non-safety research (ai-researcher), or token-efficiency studies (research-engineer-scientist-tokens).