Claude Skills by Jamie-BitFlight

Use when creating a README for a Python package, preparing for PyPI publication, fixing README rendering errors found by twine check, choosing between README.md and README.rst, or configuring the readme field in pyproject.toml. Generates professional, PyPI-compliant README files.

Use when the python-cli-architect agent needs project structure reference and task completion quality gates for Python CLI projects. Loaded automatically by the python-cli-architect agent — covers package layout, Hatchling configuration, and implementation quality criteria.

Guided workflow for adding new features to Python projects. Use when planning a new feature implementation, when adding functionality with proper test coverage, or when following TDD to build features incrementally.

Debug functional issues in Python code using specs, logs, and observed behavior. Use when a feature isn't working as specified, when investigating runtime errors, or when scoping a problem before implementing a fix.

Python 3 development plugin documentation index. Load when needing to read about Python implementation patterns, task file conventions, or stdlib scripting.

Use when building Python 3.11+ CLI apps (Typer/Rich), writing pytest test suites, fixing ruff linting or ty/mypy type errors, configuring pyproject.toml, creating portable scripts, or reviewing Python code. Activates on all Python implementation tasks — routes to specialist agents for CLI architecture, test design, packaging, and code review. Authoritative reference for modern Python 3.11-3.14 patterns and TDD workflows.

Configure pyproject.toml and Python packaging for distribution. Use when setting up a new Python package, when configuring build tools and dependencies, or when preparing a project for PyPI publishing.

Set up CI/CD pipeline for Python package publishing to PyPI. Use when preparing to publish a package, when setting up automated releases, or when configuring GitHub Actions or GitLab CI for Python projects.

Comprehensive Python code review checking patterns, types, security, and performance. Use when reviewing Python code for quality issues, when auditing code before merge, or when assessing technical debt in a Python codebase.

Use when designing pytest test suite architecture, planning test coverage strategy, or reviewing test structure for Python 3.11+ projects. Activates on "design a test strategy", "plan test coverage", "create test architecture", or when TDD/BDD/property-based testing patterns are mentioned. Guides fixture design, parametrization, async testing, and mutation testing coverage decisions.

Use when writing Python code with the Rich library — console output with markup, tables, progress bars, syntax highlighting, pretty printing, logging, or tracebacks. Covers Console, markup syntax, renderables (Panel/Table/Tree), Progress, Live, RichHandler, and the __rich_console__ protocol.

Use when searching a codebase by behavior, intent, or natural language description rather than exact identifiers. Activates the CocoIndex Code MCP server for semantic code search — finding implementations without knowing exact names, exploring unfamiliar codebases, or locating code by concept.

Validate Python shebangs and PEP 723 inline script metadata. Use when checking if Python files have correct shebangs based on their dependency requirements, when fixing incorrect shebang patterns, or when adding PEP 723 script blocks to standalone scripts with external dependencies.

Implementation phase for stinkysnake workflow. Use when tests are written and plan is ready. Implements functions following the modernization plan, runs tests until passing.

Use as the routing layer for Python development tasks — matches task descriptions against trigger lists and activates specialist skills before starting work. Covers Typer, Rich, Textual, FastMCP/MCP, ty type checker, uv, Hatchling, TOML editing, pre-commit/prek, async Python, PyPI packaging, complex linting, and technical debt modernization.

LAST RESORT for stdlib-only Python 3.11+ scripts in CONFIRMED restricted environments (airgapped systems, no uv, no internet access). Creates portable dependency-free scripts using argparse, logging, config management (JSON/TOML/INI), and cross-platform patterns. Use ONLY when environment restrictions prevent Typer+Rich with PEP 723. Triggers on "stdlib-only script", "no dependencies", "airgapped", "restricted environment", "portable script no network". For standard CLI development, use pytho...

Progressive Python quality improvement with static analysis, type refinement, modernization planning, plan review, and test-driven implementation. Use when addressing technical debt, eliminating Any types, applying modern Python patterns, or refactoring for better design.

Use when encountering failing tests, diagnosing test errors, or establishing a systematic approach to test failure investigation. Activates on "test failure analysis", "debugging tests", or "why tests fail" requests. Establishes the mindset that treats test failures as valuable diagnostic signals requiring root-cause investigation — not automatic code fixes or test dismissal.

Use when building Textual TUI apps — creating widgets, laying out screens, handling events, managing reactivity, testing with Pilot, or running background workers. Covers App lifecycle, CSS styling, screen stack, reactive attributes, custom messages, actions, bindings, and the Worker API.

Use when reading or writing pyproject.toml or .toml config files in Python, editing TOML while preserving comments and formatting, designing configuration file formats for Python tools, working with tomlkit or tomllib, or implementing atomic config file updates.

Use when building Typer/Rich CLI applications or reviewing existing CLI code for correctness. Activates on requests involving Rich table rendering, console output handling, testing Rich-formatted output, or Typer command wiring. Prevents common AI mistakes — Rich table wrapping in non-TTY contexts, incorrect stderr/stdout separation, and integration pitfalls. Load alongside the typer and rich API reference skills.

Use when building CLI applications with Typer — creating commands, defining arguments and options, composing subcommands, testing with CliRunner, or using advanced features like callbacks and autocompletion. Covers app creation, parameter types, subcommand composition, testing patterns, and output utilities.

Use when creating new Claude Code skills following established patterns and templates. Activates on "create a new skill", "add a skill to plugin", or "write skill from template" requests. Note — commands are deprecated; this skill creates SKILL.md files instead. Produces correctly structured skill files with valid frontmatter and body.

Scan Claude Code session transcripts to find the strongest user reactions to assistant instruction-following failures, reconstruct the triggering assistant output, and render a shareable terminal-style PNG artifact. Use when you want to surface and share a moment where the assistant completely missed what was asked — captures what they were doing, what Claude said, and how the user reacted. Triggers on: "rtfp", "read the fucking prompt", "find my worst AI moment", "make a rage screenshot from...

Use when debugging software, investigating incidents, diagnosing flaky tests, or analyzing performance regressions — enforces structured observation recording with evidence IDs, causality validation, and verification gates to prevent correlation-causation pollution. Use when an agent might otherwise summarize or speculate instead of reporting observed evidence.

Design and run controlled experiments using the experiment-registry MCP server — domain-agnostic, pluggable, mechanically enforced. Use when you need evidence that a change actually improves behaviour.

Use when facing unknowns, debugging without a clear cause, or making architecture decisions — enforces hypothesis-driven scientific reasoning through observation, hypothesis formulation, prediction, experiment design, and evidence-based conclusion. Use when previous attempts have failed or the problem space involves uncertainty.

Rules for orchestrators handling sub-agent results without lossy re-summarization. Activates when receiving agent output, relaying research results, passing data between agents, or combining agent findings. Prevents the failure mode where 'not found' becomes 'doesn't exist' and counts get dropped.

Summarize files by reading content, extracting key passages, and applying type-specific strategies. Activates on summarize this file, what's in this file, describe this codebase, file summary, analyze this file, tl;dr this file, what does this code do, explain this config, break down this script. Routes to strategies for code, config, data, documentation, markup, and binary files based on extension and word count.

Describe images and screenshots by viewing content with multimodal Read tool and documenting only visible elements. Activates on what's in this image, describe this screenshot, summarize this diagram, image summary, what does this screenshot show, explain this diagram, break down this chart. Handles UI screenshots, architecture diagrams, charts, photos, code screenshots, and terminal output. Never infers from filenames.

Combines multiple summaries into coherent synthesis. Activates when user requests combine these summaries, synthesize results, merge findings, or multi-source synthesis. This is the reduce step after individual sources have been summarized. Enforces deduplication, conflict surfacing, confidence scoring across sources, and attribution to all sources.

Route summarization requests to the correct methodology and enforce fidelity rules. Activates on summarize, tl;dr, give me the highlights, what's important in this, break down this, what does this code do, explain this file, describe this image, read and summarize. Routes files, URLs, images, and multi-source content to type-specific strategies. Enforces anti-hallucination rules — read before summarizing, extract before abstracting, preserve counts, distinguish absence from nonexistence, prev...

Summarize web content by fetching URLs, extracting key passages with quote-grounding, and producing structured output. Activates on summarize this URL, what does this page say, summarize this article, read and summarize, summarize the documentation at, tl;dr this link, give me the highlights of this page, what's important on this site. Routes to fetching strategy based on content type — documentation, articles, API references, READMEs. Reports partial accessibility explicitly.

Use when the primary outcome is comparing documentation claims with implementation evidence, synchronizing docs from verified changes, or freshness review, including drift, missing coverage, stale claims, and post-change updates.

Use when the primary outcome is authoring, rewriting, summarizing, or validating user-facing documentation without implementation comparison or required source attribution, including READMEs, tutorials, API docs, GitLab Markdown, source-faithful summaries, and audience-specific prose.

Use when the primary outcome is creating publishable source-attributed content from URLs or supplied material, including research summaries, articles, briefs, and posts whose factual claims and quotations need reader-visible, verifiable citations.

Use when the primary outcome is creating a new portable Agent Skill directory from one local documentation file, documentation directory, or Git repository, including mixed text and capability-gated binary sources that require exhaustive source-to-output accounting.

Use when the primary outcome is refining an existing AI-facing artifact without dropping behavior, including SKILL.md, AGENTS.md, CLAUDE.md, rules, prompts, and agent definitions that need sharper invocation, structure, or completion criteria.

Use when the user explicitly asks Rewrite Room to route documentation work, or when audit/sync/freshness, user-facing authoring, citation-driven writing, docs-to-skill conversion, and AI-instruction optimization overlap and exactly one workflow must be chosen.

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 application architecture, designing cloud-native services, reviewing codebases for methodology compliance, advising on configuration, scaling, observability, security, and deployment patterns. Incorporates the 2025 open-source community evolution and cloud-native reinterpretations of each factor.

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.

/install-skill nextlevelbuilder/ui-ux-pro-max-skill ``` ```bash npx uipro-cli install ``` The CLI detects the active AI assistant environment or accepts an explicit `--assistant` flag and generates the correct integration files (e.g., `.claude/CLAUDE.md` entry, Kiro workflow YAML, Copilot instructions file). Once installed, the skill auto-activates on natural language requests: ```text User: Build a SaaS dashboard landing page in Next.js with a modern glassmorphism style Claude: [queries desi...

**Research Date**: 2026-05-10 **Source URL**: <https://github.com/Jamie-BitFlight/claude_skills/blob/main/.claude/skills/gh/SKILL.md> **GitHub Repository**: <https://github.com/cli/cli> **Version at Research**: 2.87.2 (released 2026-02-20) **License**: MIT (GitHub CLI project) ---

Scrapling Skill is a Claude Code community skill that wraps the Scrapling Python library to provide intelligent web scraping and HTML data extraction capabilities. It implements an automated decision tree to select optimal Fetcher strategies (curl_cffi, Playwright, Camoufox, or pure parsing) based on target website characteristics, then generates and executes parameterized Python scripts for scraping. The skill supports static pages, Cloudflare-protected sites, form-based login sessions, JavaScr

Codebase Recon is a multi-agent compatible skill that analyzes git history to understand a codebase before reading any code. It reveals project health, risk areas, team structure, and development momentum through structured analysis of seven codebase dimensions. The skill is designed for developers onboarding to new projects, technical leads auditing codebases, and consultants evaluating code repositories.

**Resource**: Internal Communications Skill Module **Repository**: [ComposioHQ/awesome-codex-skills/internal-comms](https://github.com/ComposioHQ/awesome-codex-skills/tree/master/internal-comms) **License**: Apache License 2.0 **Latest Update**: 2026-05-07