Claude Skills by Jamie-BitFlight

Grooms a backlog item by running RT-ICA assessment, enriching acceptance criteria, and preparing it for implementation. Use when the user asks to groom, refine, prioritize, or prepare a backlog item — activate with a backlog item number (#N) or title.

Grooms a GitHub milestone for parallel execution — batch-grooms ungroomed items, assesses scope gaps, analyzes cross-item dependencies via Impact Radius overlap, builds conflict groups, assigns items to execution waves, and persists the dispatch plan via dispatch_create_plan MCP tool. Calls dispatch_wave_start per wave to register state. Use when preparing a milestone for /work-milestone execution. Pass the milestone number as the first argument. Requires milestone items assigned via /group-i...

Use when assigning backlog items to a GitHub milestone. Args: {milestone-number} [P0|P1|P2|title-filter]. Uses backlog list to load items, shows items with GitHub Issue status, lets user select which to assign. Creates missing GitHub Issues for selected P0/P1 items, assigns all to the milestone, updates Project V2 Status to Backlog. Use after create-milestone to populate a sprint or release.

Quantitative cost measurement for technical research — token injection costs, payload sizes, context window consumption, and file-level counts from actual repo files. Use when a technical-researcher orchestrator needs the cost dimension of adding or changing something: how many tokens will it inject, how big are the artifacts, what is the context window impact? This is NOT blast-radius analysis (which files break) — it covers size, tokens, and performance cost only.

Use when the plan_ref returned by add-new-feature is provided. Executes the SAM implementation loop — dispatches ready tasks to specialist agents in parallel, manages bookend tasks (T0 baseline capture and TN verification), tracks concerns and contract violations per task, and relies on hooks to update task status. Manages task batches via sam_plan and sam_task MCP tools.

Manages feature implementation task state via SAM MCP tools. Use when querying task status, listing ready tasks, claiming tasks for execution, updating task timestamps, or coordinating multi-task feature rollout. Activated by the /dh:execution orchestrator to track progress — also activates directly when managing tasks or configuring hook profiles.

Routes a Superpowers plan file through the /work-backlog-item pipeline and writes SAM task back-references and chunk annotations into the original plan. Use when given a path to a Superpowers plan file via $ARGUMENTS and needing to create a linked backlog item plus SAM task plan.

Spawn and manage persistent interactive Claude Code CLI sessions with bidirectional communication via tmux. Provides spawn, send, read, status, list, and kill subcommands for orchestrating parallel peer sessions. Uses built-in --worktree and --tmux flags. Sessions stay alive for multi-turn steering. Triggers on "spawn claude session", "launch separate claude", "peer session", "inter-session communication", "shadow clone", "kage bunshin".

Entry point for refreshing the DH workflow graph after changes to skills, agents, Mermaid flowcharts, MCP tools, or artifact flows. Use after any structural workflow change to keep docs/dh-workflow-graph.json current.

Use when a diff needs four parallel perspective reviewers (Security, Performance, Quality, Accessibility). Creates an ephemeral SAM plan, collects structured verdicts, synthesizes them into one deduplicated cross-referenced punch list, prints one summary line per perspective, and exits non-zero if any perspective returns REJECT. SKIP is a passing outcome.

Same reverse-thinking method as dh:rt-ica (see dh:rt-ica or dh-glossary for the definition), but localizes any MISSING input to the affected task only, as a non-blocking information-completeness pre-pass before task decomposition and plan generation. Use when grooming backlog items, generating plans, decomposing tasks under uncertainty, or working in brownfield and refactor scenarios. Produces completeness summary (APPROVED-FOR-PLANNING, APPROVED-WITH-GAPS, or BLOCKED-FOR-PLANNING), missing i...

Use when Stage 1 Discovery is complete and design must begin — transforms the ARTIFACT:DISCOVERY into an actionable ARTIFACT:PLAN via RT-ICA prerequisite verification. Produces approach, components, success criteria, acceptance tests, and risks. Blocks on missing prerequisites before design proceeds.

Synthesis step in the multi-angle technical research pipeline. Receives structured outputs from all four research angles (api-state, ecosystem-research, impact-measurement, codebase-auditor), applies cross-angle signal weighting and conflict resolution, and produces a single synthesized Research section. Invoked by the technical-researcher orchestrator after all angle skills complete. Returns content to the orchestrator — does not write to the backlog.

Load the shared verdict, punch-list, SKIP, and gate schema used by the dh multi-perspective review orchestrator, reviewers, and synthesizer. Use only when one of those components must produce, parse, validate, or gate review results.

Works backward from the stated goal through its prerequisite chain — what must exist to reach the goal, then what each of those requires, back to the current state — classifying each prerequisite as AVAILABLE, DERIVABLE, or MISSING, and blocking planning until nothing is MISSING. Use before creating plans, delegating to agents, or defining acceptance criteria. Activates on specs, PRDs, tickets, RFCs, architecture designs, and multi-step engineering tasks. Always pass `#N` or an inline goal — ...

Use when creating or updating the project skill discovery config — generates or regenerates .dh/skill_discovery.yaml by scanning the repo to infer tech stack, inventorying installed skills via npx skills list, loading candidate skill content before suggesting, and writing a config-driven skill injection file. Triggers on /dh:setup-skill-discovery invocations and programmatic --auto calls from add-new-feature Phase 3.

Use when executing a SAM task — claims the task via MCP to set it IN PROGRESS, writes active-task context for hooks, loads task-level skills, implements against acceptance criteria, and marks complete via --complete flag. Triggers on task execution within the implement-feature loop or when an agent picks up a specific task from a plan file.

Where a dispatched step puts its output, and how it signals state upstream.

Decomposes a contextualized plan into atomic, independently executable tasks with complete embedded context, registered through the plan API. Use after SAM Stage 3 Context Integration produces the contextualized plan artifact — when the plan is ready for task generation with CLEAR ordering, CoVe checks, and dependency graphs for parallel execution.

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.

Scientific validation protocol for verifying fixes work through observation, not assumption. Use when claiming a bug fix, code change, refactoring, or implementation is complete — enforces reproduce-broken-state then define-success-criteria then apply-fix then verify-outcome. Success means observing intended behavior, not absence of errors.

Rigorous self-assessment checklist before marking any task as complete. Use when about to claim task completion, before final commit, when user asks "is it done?", or when transitioning from implementation to reporting. Prevents premature completion claims by requiring evidence for every assertion.

Use when creating, grooming, planning, or closing a backlog item. Bridges backlog items to SAM planning with issue, project, and milestone tracking against the configured backend. Activates on interactive browsing with no arguments, loading an item by issue reference or title match to run grooming and SAM planning, autonomous unattended runs that substitute evidence-derived decisions for clarifying questions, a quick path for one-file fixes where full grooming is disproportionate, dismissing ...

Executes a groomed milestone with parallel kage-bunshin sessions in isolated worktrees. Use when a milestone has been groomed and /groom-milestone has produced a dispatch plan. Reads the dispatch plan, creates an integration branch, spawns one kage-bunshin (independent claude -p process) per wave item in its own worktree — each session is a full orchestrator with the Agent tool. Sequentially merges worktree branches, relays wave discoveries to subsequent waves, then lands the integration bran...

Use when starting, stopping, or checking the dot-dash live session dashboard — a real-time browser UI that monitors all active Claude Code sessions, streams transcripts, and supports prompt injection

Query and invoke tools on MCP servers using fastmcp list and fastmcp call. Use when you need to discover what tools a server offers, call tools, or integrate MCP servers into workflows.

Use when building, extending, or debugging FastMCP v4 (or v3) Python MCP servers. Activates on FastMCP tool/resource/prompt creation, provider and transform implementation (CodeMode, Tool Search), auth setup (MultiAuth, PropelAuth, KeycloakProvider), client SDK usage, FastMCPApp and Generative UI server building, fastmcp-slim client-only installs, nginx reverse proxy deployment, Prefab Apps, OTEL observability, testing, and v3→v4 migration. Grounded in official FastMCP v4 docs plus locally-ve...

Write and evaluate effective Python tests using pytest. Use when writing tests, reviewing test code, debugging test failures, or improving test coverage. Covers test design, fixtures, parameterization, mocking, and async testing.

Use when asked to find a rage moment, analyze a direct transcript path, or analyze a session time range. RTFP finds the strongest instruction-following failure in Claude or Codex sessions and renders a rage receipt.

GitLab CI/CD pipeline configuration and GLFM documentation expertise. Use when modifying .gitlab-ci.yml, optimizing pipelines, testing with gitlab-ci-local, writing GitLab README/Wiki content, configuring Docker-in-Docker workflows, or implementing CI Steps composition.

Orchestrator delegation workflows for linting. Guides orchestrators on when and how to delegate to linting-root-cause-resolver and post-linting-architecture-reviewer agents. Use when orchestrating linting tasks, delegating quality checks, or reading linting resolution reports.

Linter-specific resolution workflows for ruff, mypy, pyright, and basedpyright. Provides systematic root-cause analysis procedures, suppression gates, and verification steps. Use when resolving linting errors as a sub-agent, implementing fixes systematically, or conducting type flow analysis.

Comprehensive linting and formatting verification workflows. Provides automatic format-lint-resolve pipelines for orchestrators and sub-agents. Use when running linters, fixing ruff/mypy/bandit errors, ensuring code quality before completion, or resolving linting issues systematically.

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.

Orchestrator discipline plugin documentation index. Load when needing to read about orchestration patterns, delegation rules, or context window management.

Orchestrator context window discipline enforcement. Prevents the orchestrator from reading source files it will not edit, running diagnostic commands that waste context, and rationalizing delegation bypasses. Use when setting up orchestrator guardrails, reviewing delegation discipline, or diagnosing context window waste in multi-agent workflows. Activates PreToolUse hooks that surface decision points before source file reads and diagnostic command execution.

This skill should be used when the user asks to install Perl modules, use cpanm, create a cpanfile, manage Perl dependencies, set up Carton, configure local lib, or mentions CPAN, cpanminus, module installation, or Perl package management.

This skill should be used when the user asks to "write a Perl script", "create Perl code", "modern Perl best practices", "Perl 5.30+", "use strict warnings autodie", or mentions Perl pragmas, subroutines, error handling, or scripting patterns. Provides comprehensive Perl 5.30+ development guidance.

This skill should be used when the user asks to "install perlbrew", "set up Perl environment", "install Perl version", "manage Perl versions", "switch Perl version", "install plenv", or mentions Perl version management, development environment setup, or multiple Perl installations.

This skill should be used when the user asks to lint Perl code, run perlcritic, check Perl style, format Perl code, run perltidy, or mentions Perl Critic policies, code formatting, or style checking.

This skill should be used when the user asks to write Perl tests, test Perl code, use Test More, run prove, create test suite, mock Perl, or mentions Perl testing, TAP, Test Class, Test Deep, or test-driven development in Perl.

This skill should be used when the user asks to "validate Perl script", "check Perl syntax", "verify Perl code", "/perl-validate", or mentions script validation, compile check, security review, or best practice compliance for Perl code.

Demonstrates all available skill frontmatter fields. Use when you need a reference for skill configuration, when learning about skill capabilities, or when creating new skills from scratch.

Add automated documentation updater to any Claude skill. Creates a Python sync script that downloads upstream docs, processes markdown for AI consumption, and maintains local cache with configurable refresh. Collects template variables, then delegates implementation through 5-phase workflow. Use when adding auto-updating reference documentation to plugins or skills.

Runs the description-drift experiment — spawns all Claude Code agents simultaneously to collect self-reported capabilities, then compares them against static frontmatter descriptions to reveal how reliable orchestrator routing based on descriptions actually is. Use when measuring description drift across the agent fleet, re-running the capability collection experiment, analyzing a specific agent's self-reported capabilities, or auditing whether frontmatter descriptions accurately reflect agen...

Create high-quality Claude Code agents from scratch or by adapting existing agents as templates. Use when the user wants to create a new agent, modify agent configurations, build specialized subagents, or design agent architectures. Guides through requirements gathering, template selection, and agent file generation following Anthropic best practices (v2.1.63+).

Agent Skills Open Standard reference (agentskills.io). Use when creating portable skills for Claude Code, Cursor, Gemini CLI, OpenAI Codex, VS Code, Roo Code, and 20+ compatible agents. Covers frontmatter schema, naming rules, directory structure, progressive disclosure, validation, and authoring. Load before creating cross-agent skills.

Knowledge reference for Autonomous Refinement Loop research — pattern research into prerequisites for autonomous execution without synchronous human blocking gates. Defines failure categories, prerequisites, and conditions for replacing human judgment with machine-verifiable checks. Use when designing or evaluating autonomous agent loops, gate conditions, or HOOTL execution patterns.

Format and score completed plugin assessment findings without starting another assessment workflow. Use when a plugin assessment needs the standard report structure, scoring criteria, severity breakdown, or marketplace-readiness result.