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Found 887 Skills
CI/CD pipelines, deployment strategy, and infrastructure. Use when setting up GitHub Actions workflows, choosing deployment platforms, configuring production environments, securing pipelines with OIDC, optimizing build performance, building container images, measuring DORA metrics, or setting up Docker multi-stage builds.
Generate a personalized follow-up sequence for any creator chasing scenario — missing info, unsigned contract, late content, missing metrics, or incomplete whitelisting setup. This skill should be used when chasing a creator for a response, writing a follow-up message to an influencer, nudging a creator about a late deliverable, following up on an unsigned contract, requesting missing campaign metrics, chasing whitelisting or ad access setup, escalating a non-responsive creator, writing a reminder to a creator who ghosted, building a follow-up cadence for overdue items, drafting a polite but firm nudge to an influencer, or managing creator communication when deadlines slip. For writing initial outreach messages, see creator-outreach-sequence-generator. For classifying and triaging creator replies, see reply-triage-classifier. For negotiating rates after a creator responds, see creator-negotiation-assistant.
Detect Single Responsibility Principle (SRP) violations using multi-dimensional analysis. Use when reviewing code for "SRP", "single responsibility", "god class", "doing too much", "too many dependencies", before commits, during refactoring, or as quality gate. Analyzes Python, JavaScript, TypeScript files with AST-based detection, metrics (TCC, ATFD, WMC), and project-specific patterns. Provides actionable fix guidance with refactoring estimates.
Analyze workforce data — attrition, engagement, diversity, and productivity. Trigger with "attrition rate", "turnover analysis", "diversity metrics", "engagement data", "retention risk", or when the user wants to understand workforce trends from HR data.
Create professional equity research earnings update reports (8-12 pages, 3,000-5,000 words) analyzing quarterly results for companies already under coverage. Fast-turnaround format focusing on beat/miss analysis, key metrics, updated estimates, and revised thesis. Includes 1-3 summary tables and 8-12 charts. Use when user requests "earnings update", "quarterly update", "earnings analysis", "Q1/Q2/Q3/Q4 results", or post-earnings report.
Audit an LLM eval pipeline and surface problems: missing error analysis, unvalidated judges, vanity metrics, etc. Use when inheriting an eval system, when unsure whether evals are trustworthy, or as a starting point when no eval infrastructure exists. Do NOT use when the goal is to build a new evaluator from scratch (use error-analysis, write-judge-prompt, or validate-evaluator instead).
Query and analyze Datadog logs, metrics, APM traces, and monitors using the Datadog API. Use when debugging production issues, monitoring application performance, or investigating alerts.
Use the public REST APIs that power dashboard.internetcomputer.org. Get data for canisters, ledgers, SNS, and metrics.
Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.
Expert product analytics strategist for SaaS and digital products. Use when designing product metrics frameworks, funnel analysis, cohort retention, feature adoption tracking, A/B testing, experimentation design, data instrumentation, or product dashboards. Covers AARRR, HEART, behavioral analytics, and impact measurement.
Profile and explore a dataset to understand its shape, quality, and patterns. Use when encountering a new table or file, checking null rates and column distributions, spotting data quality issues like duplicates or suspicious values, or deciding which dimensions and metrics to analyze.
Show the current health status of a GRACE project. Use to get an overview of project artifacts, codebase metrics, knowledge graph health, and suggested next actions — helps identify drift, missing contracts, or unpaired semantic blocks.