Data & Analytics

Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.

Use when discovering niche signals, auditing ICP or won/lost evidence, rescoring accounts, or building account and lead scoring Plays. Triggers on fit scoring, engagement scoring, external proxies, and scoring leakage. Skip pure outreach copy or contributor skill installation tasks.

Weekly F&O trade planning skill for Indian markets. Analyzes news/macro events, identifies trending sectors and instruments, determines probable direction, suggests option strategy with entry/exit levels, and manages stop-loss and profit booking after position entry. Use when user wants a weekly trade idea, F&O direction call, or ongoing position management for Nifty, Bank Nifty, or stock options.

This skill should be used when analyzing weekly price charts for Indian stocks (NSE/BSE), indices (Nifty 50, Bank Nifty, Sensex), or any other instrument. Use this skill when the user provides chart images and requests technical analysis, trend identification, support/resistance levels, scenario planning, or probability assessments based purely on chart data without consideration of news or fundamental factors.

Options strategy analysis for Indian F&O markets (NSE). Use when user requests options strategy recommendations, P/L analysis, Greeks calculation, risk management, or F&O strategy planning for Nifty, Bank Nifty, or stock options.

Screen Nifty 500 stocks for Mark Minervini's Volatility Contraction Pattern (VCP) — identifying Stage 2 uptrends with tightening price ranges and declining volume before potential breakouts. Use this skill when the user requests VCP screening, Minervini-style setups, Stage 2 breakout candidates, or volatility contraction patterns on NSE/BSE stocks.

Use when user requests analysis of Indian stocks, fundamental assessment, technical review, comparisons, or investment reports for NSE/BSE listed companies.

Track and analyze Indian stock market news, corporate announcements, SEBI circulars, bulk/block deals, and earnings calendars. Auto-fetches headlines from MoneyControl, Economic Times, LiveMint, BSE/NSE filings. Use when the user asks about recent news, corporate actions, upcoming events, or wants a daily market news briefing for NSE/BSE.

Analyze Indian market breadth health using advance/decline data, stocks above moving averages, new highs/lows, and sector participation. Use when assessing rally quality, market participation width, or equity exposure levels for NSE/BSE.

Track and analyze FII/DII daily buy/sell flows in Indian markets. Use when user asks about institutional flows, FII selling/buying trends, DII activity, or institutional impact on Nifty/market direction.

Monthly social media performance review for SMBs. Analyses post-level and account-level data from Instagram, LinkedIn, Facebook, or TikTok. Identifies what worked, what didn't, and why. Produces a client-ready report and specific recommendations that feed back into the content calendar. Accepts CSV exports, screenshots, or manual data input.

Load the latest model checkpoint, run evaluation on the test set, and generate a metrics report with confusion matrix. Use this after training to assess model performance or to re-evaluate a specific checkpoint.

Groups and prioritizes enriched findings by Line of Business (LOB) using the mappings defined in `references/lob-mappings.json`. Produces a structured output organized by LOB with severity-ranked findings.

Takes raw dashboard findings and enriches them with external context via web search. Transforms data-only insights into contextualized intelligence by adding competitor dynamics, market trends, and macro factors.

Browses the Strategy Cloud library, inventories all dashboards, opens each one, and detects anomalies, threshold breaches, and trends using the detection patterns defined in `references/finding-patterns.json`.

Model-specific pricing tables and cost calculation utilities for Claude API usage tracking and budget management.

Reusable analysis patterns and heuristics for plugin execution data. Provides standardized methods for pattern detection, anomaly scoring, and improvement signal extraction.

Reinforcement Learning best practices for Python using modern libraries (Stable-Baselines3, RLlib, Gymnasium). Use when: - Implementing RL algorithms (PPO, SAC, DQN, TD3, A2C) - Creating custom Gymnasium environments - Training, debugging, or evaluating RL agents - Setting up hyperparameter tuning for RL - Deploying RL models to production

Comprehensive guide for Deep Learning with Keras 3 (Multi-Backend: JAX, TensorFlow, PyTorch). Use when building neural networks, CNNs for computer vision, RNNs/Transformers for NLP, time series forecasting, or generative models (VAEs, GANs). Covers model building (Sequential/Functional/Subclassing APIs), custom training loops, data augmentation, transfer learning, and production best practices.

Use when the user wants to check dataset quality, diagnose eval issues, or before running evolve. Checks size, difficulty distribution, dead examples, coverage, and splits. Auto-corrects issues found.

Weekly / monthly closed-loop reporter for Peec AI visibility growth. Measures what moved (visibility per prompt, cluster, zone) against what was invested (content published, pitches sent, forum answers), detects winning patterns, and outputs a ranked next-actions list — not a dashboard. Closes the feedback loop for the growth agent. Use weekly for active projects or monthly for maintenance-mode.

Cross-project pattern layer for the Peec AI growth loop. After any Peec skill completes (or after peec-report closes a cycle), extract 1–3 concrete patterns from the output and persist them to SkillMind via mcp__skillmind__add_pattern / remember. On the next orchestrator run, recall matching patterns and pass them in as priors — so lessons learned on project A inform decisions on project B. Use when a Peec skill has produced an artifact (brief, zone map, outreach log, decision, learnings.json...

Content-intelligence workflow that turns a Peec AI visibility gap into a publish-ready content brief. Combines Peec (prompt visibility, source URLs, scraped chat responses), Visibly AI (backlinks, onpage, keywords, GSC), and Reddit / forum mining. Uses Query Fan-Out to expand one prompt into 5–8 sub-queries and scores competitor URLs for attackability. Use when the user wants to find content opportunities, evaluate competitor content, build a content brief from Peec data, or discover what con...

Turns a Peec AI prompt set into strategic content zones — not keyword groups. Groups prompts by buyer intent + funnel stage + visibility gap + shared demand signals, producing 4–8 "content zones" with the competitive weakness to attack, supporting evidence (forum quotes, SERP patterns), and a rank-ordered next-move per zone. Use when a Peec project has 20+ prompts and needs a topic architecture — not a flat content calendar. Output is a strategic map, not a list.