jamie-bitflight-skills

This skill should be used when the model's ROLE_TYPE is orchestrator and needs to delegate tasks to specialist sub-agents. Provides scientific delegation framework ensuring world-building context (WHERE, WHAT, WHY) while preserving agent autonomy in implementation decisions (HOW). Use when planning task delegation, structuring sub-agent prompts, or coordinating multi-agent workflows.

Comprehensive Bash 5.1+ and POSIX shell development plugin with modular skills for scripting, portability, testing, linting, logging, and version-specific features (5.1, 5.2, 5.3 changelogs with examples). Includes specialized agents for script development and code auditing.

This skill should be used when users need to generate ideas, explore creative solutions, or systematically brainstorm approaches to problems. Use when users request help with ideation, content planning, product features, marketing campaigns, strategic planning, creative writing, or any task requiring structured idea generation. The skill provides 30+ research-validated prompt patterns across 14 categories with exact templates, success metrics, and domain-specific applications.

When setting up commit message validation for a project. When project has commitlint.config.js or .commitlintrc files. When configuring CI/CD to enforce commit format. When extracting commit rules for LLM prompt generation. When debugging commit message rejection errors.

When writing a git commit message. When task completes and changes need committing. When project uses semantic-release, commitizen, git-cliff. When choosing between feat/fix/chore/docs types. When indicating breaking changes. When generating changelogs from commit history.

Query, transform, and convert structured data files (JSON, YAML, TOML, XML, CSV, HCL, INI) using dasel v3. Provides reference syntax, exploration workflows, transformation patterns, and cross-platform installation.

Language-agnostic development process harness implementing the Stateless Agent Methodology (SAM) 7-stage pipeline with ARL human touchpoint model and Voltron-style language plugin composition. Provides orchestration, workflows, planning, verification, and testing methodology that any language plugin can compose with.

Build FastMCP 3.x Python MCP servers — covers provider/transform architecture (including CodeMode, Tool Search, and server-level transforms), component versioning, session state, authorization (MultiAuth, PropelAuth, connection-pooled token verifiers), evaluation creation, Pydantic validation, async patterns, STDIO and HTTP transports, nginx reverse proxy deployment, background tasks, Prefab Apps UI, security patterns, client SDK usage, testing, deployment, and migration from FastMCP v2. TypeScript is a legacy reference only and is not updated for v3.

The model must apply when tasks involve .gitlab-ci.yml configuration, GitLab Flavored Markdown (GLFM) syntax, gitlab-ci-local testing, CI/CD pipeline optimization, GitLab CI Steps composition, Docker-in-Docker workflows, or GitLab documentation creation. Triggers include modifying pipelines, writing GitLab README/Wiki content, debugging CI jobs locally, implementing caching strategies, or configuring release workflows.

This skill should be used when the model needs to ensure code quality through comprehensive linting and formatting. It provides automatic linting workflows for orchestrators (format → lint → resolve via concurrent agents) and sub-agents (lint touched files before task completion). Prevents claiming "production ready" code without verification. Includes linting rules knowledge base for ruff, mypy, and bandit, plus the linting-root-cause-resolver agent for systematic issue resolution.

When calling LLM APIs from Python code. When connecting to llamafile or local LLM servers. When switching between OpenAI/Anthropic/local providers. When implementing retry/fallback logic for LLM calls. When code imports litellm or uses completion() patterns.

When setting up local LLM inference without cloud APIs. When running GGUF models locally. When needing OpenAI-compatible API from a local model. When building offline/air-gapped AI tools. When troubleshooting local LLM server connections.

Comprehensive Perl 5.30+ development plugin with modular skills for scripting, CPAN ecosystem, environment setup, testing, linting, and validation. Includes specialized agents for script development, code auditing, and CLI architecture.

Python 3.11+ development workflows including CLI apps with Typer/Rich, script writing, test development, code review, linting troubleshooting, and pyproject.toml configuration. Provides TDD patterns, modern type hints, PEP 723 metadata, and solutions for common Python errors.

Enforce mandatory pre-action verification checkpoints to prevent pattern-matching from overriding explicit reasoning. Use this skill when about to execute implementation actions (Bash, Write, Edit) to verify hypothesis-action alignment. Blocks execution when hypothesis unverified or action targets different system than hypothesis identified. Critical for preventing cognitive dissonance where correct diagnosis leads to wrong implementation.

When an application needs to store config, data, cache, or state files. When designing where user-specific files should live. When code writes to ~/.appname or hardcoded home paths. When implementing cross-platform file storage with platformdirs.

Complete plugin development toolkit for creating, refactoring, and validating Claude Code plugins and agents. Use when creating new plugins/skills/agents, refactoring existing plugins/skills, validating frontmatter, or restructuring plugin components. Includes specialized agents for assessment, planning, execution, and validation workflows.

Faithful information summarization with fidelity preservation, structured output, and anti-hallucination methodology. Provides skills for file, URL, and image summarization; agents for autonomous summarization tasks; and hooks for validating agent output structure.

Analyze Claude Code agent session transcripts to identify inefficiencies, anti-patterns, repeated mistakes, missing tooling opportunities, and user frustration signals for continuous improvement

Enforces orchestrator context window discipline via PreToolUse hooks and rules. Prevents the orchestrator from reading source files it will not edit, running diagnostic commands that should be delegated, and bypassing delegation with 'small change' rationalizations. Install to structurally prevent investigation escalation anti-patterns.

Converts bullet steps, ASCII art, markdown tables, and prose workflows into Mermaid diagrams for AI-facing documents. Includes process quality methodology for improving ambiguous or incomplete processes before conversion.

Documentation and authoring workflow router: audit docs vs code drift, sync docs after changes, optimize prompts and SKILL.md files, validate GLFM and Markdown formatting, summarize files/URLs/images with fidelity enforcement. Use when: docs are out of date, CLAUDE.md needs improving, SKILL.md needs optimizing, checking if documentation matches code, summarizing files or URLs.

The model must invoke this skill when any trigger occurs - (1) user mentions "clang-format" or ".clang-format", (2) user requests analyzing code style/formatting patterns/conventions, (3) user requests creating/modifying/generating formatting configuration, (4) user troubleshoots formatting behavior or unexpected results, (5) user asks about brace styles/indentation/spacing/alignment/line breaking/pointer alignment, (6) user wants to preserve existing style/minimize whitespace changes/reduce formatting diffs/codify dominant conventions.

Stop-hook that audits assistant output for speculation, ungrounded causality, pseudo-quantification, and completeness overclaims — blocks completion to force evidence-first rewrites

Use when debugging, investigating root causes, designing experiments, or performing scientific analysis — enforces hypothesis-driven reasoning, evidence-first observation, causality validation, and structured output templates. Use when facing unknowns, repeated failures, or complex investigations requiring rigorous methodology.

Reference guide for the Twelve-Factor App methodology — 15 principles (12 original + 3 modern extensions) for building portable, resilient, cloud-native applications. Use when evaluating architecture, reviewing codebases for methodology compliance, or advising on configuration, scaling, observability, security, and deployment patterns.