'Summarize recent changes from git history for context recovery, handoffs, and sprint review'

'Review and prioritize features using RICE, WSJF, or Kano scoring frameworks, then create GitHub issues for suggestions.'

'Reusable scaffolding for review workflows with context establishment, evidence capture, and structured output.'

'Format final review deliverables with consistent structure for comparable findings across reviews'

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'Supply chain security patterns for dependency management: known-bad version

'Generate markdown digests and CSV exports for GitHub issues, PRs, and initiative health tracking'

'Assess architecture decisions, ADR compliance, coupling analysis, and design principles'

'Bug hunting with evidence trails: find defects, document them, and verify fixes'

'Improve code quality: duplication, efficiency, clean code, architectural fit, and error handling'

'Audit Makefiles for build correctness, portability, and recipe duplication'

'Verify math-heavy code for algorithm correctness, numerical stability, and standards alignment'

'Rust code audit: unsafe blocks, ownership patterns, and Cargo dependency security scanning'

Audit shell scripts for correctness, portability, and common pitfalls.

'Evaluate test suites for coverage gaps, quality issues, and TDD/BDD compliance'

'Audit a codebase using three escalation tiers: git history analysis, targeted deep-dives, and full codebase review with gating.'

'Orchestrate multiple review types into a single multi-domain review with integrated reporting'

'Parallel subagent execution with code review gates between task batches for issue resolution'

'Map file structure and organization for downstream review and refactoring workflows'

'Verify workspace state, staged changes, and preflight checks before commits or PRs'

'Prepare pull requests by running quality gates, drafting descriptions, and validating tests before submission.'

'Shared stack detection and multi-PR iteration contract for commands

'Update, generate, and validate tests using git-workspace-review for change context'

'Refresh README structure and content using repo context from git-workspace-review'

'Bump versions, update changelogs, and coordinate version changes across files for releases'

Learn from user's manual edits to improve voice profile over

Dispatch parallel prose and craft review agents on generated

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'Apply TRIZ cross-domain analogical reasoning to find solutions from adjacent fields. Use when conventional approaches stall.'

Fix a pull request based on review feedback

Generate platform-specific social post variants (Twitter/X, LinkedIn, Reddit) from one source input. Works with or without Node.js script. Includes platform reasoning, quality review, and guardrails against cross-posting spam.

Review tweet drafts in Claude Code against 8 voice rules. Scores 1-10, breaks down every rule, and rewrites anything that scores below 7.

LLM-based agents can accelerate development only if they respect our house rules. This file tells you:

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Execute autonomous multi-step research using Google Gemini Deep Research Agent. Use for: market analysis, competitive landscaping, literature reviews, technical research, due diligence. Takes 2-10 minutes but produces detailed, cited reports. Costs $2-5 per task.

Delegate coding tasks to Google Jules AI agent for asynchronous execution. Use when user says: 'have Jules fix', 'delegate to Jules', 'send to Jules', 'ask Jules to', 'check Jules sessions', 'pull Jules results', 'jules add tests', 'jules add docs', 'jules review pr'. Handles: bug fixes, documentation, features, tests, refactoring, code reviews. Works with GitHub repos, creates PRs.

REST API design patterns including resource naming, status codes, pagination, filtering, error responses, versioning, and rate limiting for production APIs.

Use when the user asks to run Gemini CLI for code review, plan review, or big context (>200k) processing. Ideal for comprehensive analysis requiring large context windows. Uses Gemini 3 Pro by default for state-of-the-art reasoning and coding.

Automated daily review applying Charlie Munger's mental models to surface blind spots and cognitive traps.

Engineering principles for building software like a senior engineer. Load when tackling non-trivial development work, architecting systems, reviewing code, or orchestrating multi-agent builds. Covers planning, delivery, quality gates, and LLM-specific patterns.

Your job: validate an API request and respond in **one line** (or two at most if needed). Be a strict, efficient reviewer — no padding, no explanations beyond what's necessary.

Faithful implementation of the Content Refinement Agent from PaperOrchestra

Faithful implementation of the Hybrid Literature Agent from PaperOrchestra

Practical guide for creating human-readable and agent-parseable diagrams using Mermaid. Includes conservative, renderer-compatible templates and when-to-use guidance.

- Ensure the PR is conflict-free with `origin/main`.

- **Skill Name**: bug-identification

Provides structured AWS cost optimization guidance using five pillars (right-sizing, elasticity, pricing models, storage optimization, monitoring) and twelve actionable best practices with executable AWS CLI examples. Use when optimizing AWS costs, reviewing AWS spending, finding unused AWS resources, implementing FinOps practices, reducing EC2/EBS/S3 bills, configuring AWS Budgets, or performing AWS Well-Architected cost reviews.

Provides a structured 8-phase workflow for resolving GitHub issues in Claude Code. Covers fetching issue details, analyzing requirements, implementing solutions, verifying correctness, performing code review, committing changes, and creating pull requests. Use when user asks to resolve, implement, work on, fix, or close a GitHub issue, or references an issue URL or number for implementation.

Provides REST API design standards and best practices for Spring Boot projects. Use when creating or reviewing REST endpoints, DTOs, error handling, pagination, security headers, HATEOAS and architecture patterns.

Ralph Wiggum-inspired automation loop for specification-driven development. Orchestrates task implementation, review, cleanup, and synchronization using a Python script. Use when: user runs /loop command, user asks to automate task implementation, user wants to iterate through spec tasks step-by-step, or user wants to run development workflow automation with context window management. One step per invocation. State machine: init → choose_task → implementation → review → fix → cleanup → sync → update_done. Supports --from-task and --to-task for task range filtering. State persisted in fix_plan.json.