Analyze Claude Code session transcripts to detect the strongest user frustration from AI instruction failures, reconstruct the triggering assistant output and context, then generate shareable terminal-style PNG rage receipts.
Detects emotional user reactions in a user-only batch JSONL file. Spawned once per batch file during RTFP Stage 2. Returns flagged message indexes grouped by source file.
Picks the winner from flagged emotional reactions, reads full transcript context, and produces the 3-field RTFP artifact. Stage 3 of the RTFP pipeline.
RTFP orchestrator — lists sessions, runs the 3-stage RTFP pipeline (extract → detect → reconstruct), and renders the final rage receipt PNG. Use when asked to run RTFP, find a rage moment, or generate a rage receipt from a session.
Admin access level
Server config contains admin-level keywords
Uses power tools
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Uses Bash, Write, or Edit tools
Uses Bash, Write, or Edit tools
npx claudepluginhub jamie-bitflight/claude_skills --plugin frustration-analyzerThis 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 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.
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.
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.
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.
Analyze Claude Code agent session transcripts to identify inefficiencies, anti-patterns, repeated mistakes, missing tooling opportunities, and user frustration signals for continuous improvement
Meta-Cognition tool for Claude Code: session history analysis, workflow optimization, and 21 MCP tools for deep session insights.
Non-technical progress summaries for Claude Code work (hides diffs/log noise).
Session feedback analysis - capture skill bugs, enhancements, and positive patterns as GitHub issues
Developer experience essentials: GitHub Actions debugging, conversation cloning/half-cloning, context handoffs, and Reddit research
Debug issues systematically with root cause analysis and execution tracing