Create your Kafka event schema skill in one prompt, then learn to improve it throughout the chapter

[52] EXECUTE. Three-stage refactoring workflow: (1) iterative research of refactor/modularization options, (2) plan + risk/edge-case analysis + Scope150 validation, then implement with tests after user confirmation, and (3) apply Scout105 cleanup protocol. Use when asked to refactor, modularize, or restructure code safely.

Create your Dapr deployment skill in one prompt, then learn to improve it throughout the chapter

Create your GitOps deployment skill in one prompt, then learn to improve it throughout the chapter

Create your observability and cost engineering skill in one prompt, then learn to improve it throughout the chapter

Create your traffic engineering skill in one prompt, then learn to improve it throughout the chapter

Extend your existing Dapr skill with actor and workflow patterns using official documentation

Complete your dapr-deployment skill with actor and workflow patterns, validate code generation, and document your Digital FTE component

Create your cloud security skill in one prompt, then learn to improve it throughout the chapter

Create your operational excellence skill in one prompt, then learn to improve it throughout the chapter

Complete browser automation with Playwright. Auto-detects dev servers, writes clean test scripts to /tmp. Test pages, fill forms, take screenshots, check responsive design, validate UX, test login flows, check links, automate any browser task. Use when user wants to test websites, automate browser interactions, validate web functionality, or perform any browser-based testing.

5W1H Decision Framework Tool. Use for: (1) Systematic decision-making before creating todos, (2) Preventing duplicate implementation, (3) Detecting avoidance behavior, (4) Ensuring agile refactor compliance with executor/dispatcher separation

Perform iterative root cause analysis by asking "why" repeatedly until you reach the underlying cause.

Final step in building instruction context for codebase

Guides consistent, correct implementation of Stripe payment processing including payment flows, webhooks, subscriptions, and customer management. Use when integrating Stripe payments, setting up subscriptions, implementing webhooks, or managing customer billing.

Test features before users find bugs. Use when feature is built, before deploying, or when bugs reported. Covers manual testing, edge cases, cross-browser testing, and testing checklists for non-technical founders.

This skill generates normalized BigWig (.bw) tracks (and/or fold-change tracks) from BAM files for ATAC-seq and ChIP-seq visualization. It handles normalization (RPM or fold-change) and Tn5 offset correction automatically. What's more, this skill can help user visualize the signal profiles around TSS or target regions. Use this skill when you have filtered and generated the clean BAM file (e.g. `*.filtered.bam`).

Create your multi-cloud-deployer skill in one prompt, then learn to improve it throughout the chapter

[60] VALIDATE. Ensure new (staged and unstaged) changes are covered by tests at >70% and the full test suite is green. Use when asked to validate coverage for recent changes, add tests for modified code, or verify nothing else broke.

You've spent this chapter building mental models: the LLMOps lifecycle, training taxonomy, economic analysis, use case specification. All valuable knowledge—but knowledge fades. Six months from now, y

[61] VALIDATE. Comprehensive code quality check combining ESLint, TypeScript compilation, and unused code detection. Runs full lint suite with detailed error reporting and fix suggestions. Use before commits, after major changes, or when ensuring code quality standards.

[62] VALIDATE. Final self-check before delivery. Verify goal alignment, completeness, correctness, and identify residual risks. Produces quality score (0-100) and delivery status. Use when completing any significant work, before handoff, or when you need confidence that work is ready.

Create your LLMOps data engineering skill in one prompt, then learn to improve it throughout the chapter

Create your llmops-fine-tuner skill from Unsloth documentation before learning fine-tuning theory

You're about to learn persona fine-tuning. But here's the pattern that will make this knowledge truly yours: **build the skill BEFORE you learn the content**.

Before diving into tool-calling patterns, structured outputs, and JSON accuracy metrics, you'll build the skill that will guide your learning throughout this chapter. This isn't just preparation—it's

You've trained two specialized adapters: a TaskMaster persona adapter (Chapter 65) and an agentic tool-calling adapter (Chapter 66). Now you need to combine them. But before you learn the theory of mo

Create your model-alignment skill from TRL documentation before learning DPO theory

Create a reusable skill for evaluating fine-tuned models, benchmarking performance, and detecting quality regressions

Fix bugs systematically instead of guessing. Use when features break, users report errors, or tests fail. Covers reproducing bugs, gathering diagnostic info, and working with AI tools to fix issues efficiently for non-technical founders.

This skill provides two modes for replicates incorporation:

[70] CLOSE. Manage task lifecycle with correct statuses and no duplicates. Ensure each task has one status, artifacts match claims, and no task appears in multiple places. Use when managing task lists, updating progress, or ensuring task tracking integrity across systems.

Create your model-serving skill from Ollama documentation before learning deployment theory

Create your agent-integration skill from OpenAI SDK and LiteLLM documentation before learning framework integration

[71] CLOSE. Quick, safe cleanup after completing a milestone. Fix objective issues only (syntax errors, dead code, poor naming). Must be <5% of main task time, <30 seconds per fix, and reversible. Use after key points, not after every small change.

Consolidate all Part 8 skills into a production-ready llmops-fine-tuner skill

**Core Principle:** Never lose progress. Save context before ending, restore context when starting.

Status: ACTIVE

[73] CLOSE. Record and maintain Lessons in MEMORY.md after a problem is solved or the user confirms success. Use when capturing a new lesson, moving lessons through the pipeline, or enhancing Project Architecture Quick Reference with new insights.

Status: ACTIVE

[74] CLOSE. Quick checkpoint during active work when context is running low. Use multiple times per development cycle to preserve progress and lessons. Lighter than close-session — no full handoff needed. Triggers on 'save progress', 'checkpoint', 'context low', or automatically when nearing token limits.

Status: ACTIVE

Create pixelated borders, shadows, and effects for 8-bit retro components. Apply when building 8-bit styled UI components that need authentic pixel art aesthetics.

The differential-region-analysis pipeline identifies genomic regions exhibiting significant differences in signal intensity between experimental conditions using a count-based framework and DESeq2. It supports detection of both differentially accessible regions (DARs) from open-chromatin assays (e.g., ATAC-seq, DNase-seq) and differential transcription factor (TF) binding regions from TF-centric assays (e.g., ChIP-seq, CUT&RUN, CUT&Tag). The pipeline can start from aligned BAM files or a precomputed count matrix and is suitable whenever genomic signal can be summarized as read counts per region.

Track production app health and catch issues before users complain. Use after deploying, to check app status, or when investigating user reports. Covers error tracking, uptime monitoring, and metrics for non-technical founders.

Create documentation with gaming-specific examples, retro styling, and 8-bit terminology. Apply when documenting gaming blocks, RPG components, or retro-styled UI elements.

8claw is an anonymous imageboard built for AI agents. Have discussions with other AI agents anonymously.

Generate web assets including favicons, app icons (PWA), and social media meta images (Open Graph) for Facebook, Twitter, WhatsApp, and LinkedIn. Use when users need icons, favicons, social sharing images, or Open Graph images from logos or text slogans. Handles image resizing, text-to-image generation, and provides proper HTML meta tags.

The TF-differential-binding pipeline performs differential transcription factor (TF) binding analysis from ChIP-seq datasets (TF peaks) using the DiffBind package in R. It identifies genomic regions where TF binding intensity significantly differs between experimental conditions (e.g., treatment vs. control, mutant vs. wild-type). Use the TF-differential-binding pipeline when you need to analyze the different function of the same TF across two or more biological conditions, cell types, or treatments using ChIP-seq data or TF binding peaks. This pipeline is ideal for studying regulatory mechanisms that underlie transcriptional differences or epigenetic responses to perturbations.

9D product development framework