richfrem

create-hook

Design and scaffold an event-driven Claude Code hook

Discovers and persists the user's available AI environments (Claude, Copilot CLI, Agy CLI, Cursor, etc.) to context/memory/environment.md. Run once after OS setup or whenever the environment changes. os-architect and os-evolution-planner read this file to select the right delegation backend and cheapest brainstorm model automatically. Invoked by os-architect on first run if environment.md is absent.

os-eval-lab-setup

Bootstraps a skill evaluation lab repo for an autoresearch improvement run. Trigger with "set up an eval lab", "bootstrap the eval repo", "prepare the test repo for skill evaluation", "create an eval environment for this skill", "set up the lab space for this skill", or when starting a new skill optimization run that needs a standalone test environment.

os-evolution-planner

Codifies the plan-and-delegate workflow for evolving plugins, skills, and agents. Given a target (plugin/skill/agent name) and an evolution goal, this skill first brainstorms 2-3 approach options using the cheapest available model, presents them for selection, then writes a structured task plan and Copilot CLI delegation prompt for the chosen approach. Called by os-architect for Path B (update) and Path C (create) executions. Can also be invoked standalone.

os-evolution-verifier

Verifies that os-architect actually causes evolution — not just words. Dispatches os-architect in single-shot simulation mode for a given test scenario, then checks for real artifact presence (new files, HANDOFF_BLOCK, plan files). Reports PASS / FAIL with grep evidence. Accumulates results into a test report. Use after any changes to os-architect, os-evolution-planner, or improvement-intake-agent.

os-architect

SME-facing front-door skill for Agentic OS ecosystem evolution. Invokes the os-architect interview flow: classifies intent, audits existing capabilities, proposes evolution path (orchestrate / update / create), and dispatches work. Use when evolving plugins, skills, or agents — whether applying a new pattern, setting up an improvement lab, filling a capability gap, or coordinating multiple loops.

os-eval-backport

Reviews a completed os-eval-runner lab run and backports approved changes to master plugin sources. Trigger with "backport the eval results", "review the lab run", "apply eval improvements to master", "check what the eval agent changed".

os-experiment-log

Maintains a persistent, folder-based log of all agentic-os experiment runs. Each run writes one dated file to context/experiment-log/ and updates index.md. Supports five source types: verifier (qualitative), tester (qualitative), orchestrator (numeric), planner (qualitative), survey (mixed). Handles both numeric results (eval scores, KEEP/DISCARD, delta) and qualitative results (PASS/FAIL/PARTIAL, gap analysis). Use after any experiment run to persist findings before temp/ is cleared.

os-improvement-loop

Pattern 5: Concurrent Event-Driven Multi-Agent Loop. Coordinates multiple Claude sessions as OS threads sharing a common event bus and memory address space. Every loop cycle is a full improvement cycle: execute, eval against benchmark (KEEP/DISCARD), emit friction events during work, close with post_run_metrics, agent self-assessment survey saved to retrospectives, memory persistence, and Triple-Loop Retrospective trigger if friction threshold crossed. Four coordination topologies: turn-signal, fan-out, request-reply, triple-loop (Pattern D).

self-evolution

Self-healing and self-evolving pattern for agents operating against external systems (CDP automation, DOM-dependent tooling, web APIs). Classifies failures into three tiers — Gap / Failure / Regression — applies repo-profile-gated edits with appropriate autonomy, verifies the fix, and updates domain reference files ("The Map, not the Diary"). Invoke whenever a tool call or subprocess returns a failure that a patched helper could fix.

create-plugin

Scaffold a complete Claude Code plugin from scratch

create-skill

Scaffolds the filesystem structure for a new agent skill: creates the directory layout, writes a starter SKILL.md, generates evals/evals.json, references/, scripts/, and assets/ as needed, and runs a discovery interview to capture name, purpose, and trigger phrases before writing any files. Use when the user says "create a new skill", "scaffold a skill", "generate a skill", "new skill setup", or "make a skill directory". Does not handle content improvement for existing skills — that is handled by os-skill-improvement.

eval-autoresearch-fit

Trigger with "evaluate autoresearch fit", "score this skill for karpathy loop", "is this a good autoresearch candidate", "assess autoresearch viability for", "which skills are best for autonomous loop optimization", "score skills for 3-file architecture", or when the user wants to determine if a skill is a good candidate for applying the Karpathy autoresearch autonomous optimization loop pattern.

l5-red-team-auditor

Performs an uncompromising L5 Enterprise Red Team Audit on a given plugin against the 39-point architectural maturity matrix. Trigger when the user requests a security audit, red team assessment, structural compliance review, or maturity gap analysis of any agent plugin or skill directory.

mine-plugins

Trigger with "mine plugins", "analyze plugin collection", "run the full analysis pipeline", "inventory and analyze all plugins", "mine patterns from this directory", or when you want to run the complete virtuous cycle: inventory, analyze, extract patterns, synthesize recommendations, and deliver a structured report. Use this even if the user just says "analyze everything in this folder".

create-sub-agent

Design and scaffold a Claude Code sub-agent

create-agentic-workflow

Scaffold a GitHub agentic workflow from an existing skill

DevOps & Infrastructure Listed

create-docker-skill

Scaffold an agent skill with Docker runtime support

create-github-action

Scaffold a deterministic GitHub Actions CI/CD workflow

create-stateful-skill

Scaffold an advanced stateful agent skill with L4 patterns

agent-swarm

(Industry standard: Parallel Agent) Primary Use Case: Work that can be partitioned into independent sub-tasks running concurrently across multiple agents. Parallel multi-agent execution pattern. Use when: work can be partitioned into independent tasks that N agents can execute simultaneously across worktrees. Includes routing (sequential vs parallel), merge verification, and correction loops.

analyze-plugin

Systematically analyze agent plugins and skills to extract design patterns, architectural decisions, and reusable techniques. Trigger with "analyze this plugin", "mine patterns from", "review plugin structure", "extract learnings from", "what patterns does this plugin use", "check if this plugin is well-structured", "validate plugin compliance", or when examining any plugin or skill collection to understand its design. Use this skill even when the user just says "look at this plugin" or "tell me how this is structured."

os-eval-runner

Stateless evaluation engine that scores and gates skill improvement iterations using headless Python evaluation scripts. Use when the user says "evaluate this skill", "run autoresearch loop on", "optimize this skill", "run the eval loop", or when another agent proposes a change to an existing skill and needs empirical validation before applying it. Supports autonomous loop mode for iterative improvement and single-shot QA mode for validating one specific proposed change. Requires Python 3.8+ and a git repository.

dual-loop

(Industry standard: Sequential Agent / Agent as a Tool) Primary Use Case: Delegating a well-defined task to a worker agent, verifying its execution, and repeating if necessary. Inner/outer agent delegation pattern. Use when: work needs to be delegated from a strategic controller (Outer Loop) to a tactical executor (Inner Loop) via strategy packets, with verification and correction loops.

learning-loop

(Industry standard: Loop Agent / Single Agent) Primary Use Case: Self-contained research, content generation, and exploration where no inner delegation is required. Self-directed research and knowledge capture loop. Use when: starting a session (Orientation), performing research (Synthesis), or closing a session (Seal, Persist, Retrospective). Ensures knowledge survives across isolated agent sessions.

orchestrator

(Industry standard: Routing Agent / Orchestrator Pattern) Primary Use Case: Analyzing an ambiguous trigger and routing it to one of the specific specialized implementations. Routes triggers to the appropriate agent-loop pattern. Use when: assessing a task, research need, or work assignment and deciding whether to run a simple learning loop, red team review, dual-loop delegation, or parallel swarm. Manages shared closure (seal, persist, retrospective, self-improvement).

Code & Development Listed

red-team-review

(Industry standard: Review and Critique Pattern) Primary Use Case: Iterative generation paired with adversarial review, continuing until an 'Approved' verdict is reached. Orchestrated adversarial review loop. Use when: research, designs, architectures, or decisions need to be reviewed by red team agents (human, browser, or CLI). Iterates in rounds of research → bundle → review → feedback until approved.

triple-loop-learning

(Industry standard: Meta-Learning System / Automated Autoresearch) Primary Use Case: Continuous, self-improving orchestration of an agentic system over multiple sessions. Use when: building a continuous improvement layer that autonomously identifies workflow friction, postulates hypotheses, and tests improved instructions/coding skills against an objective headless benchmark before merging and persisting.

optimize-agent-instructions

Audits and rewrites AI agent instruction files (CLAUDE.md, GEMINI.md, .github/copilot-instructions.md) in any repo. Strips stale or foreign content, applies Karpathy's four behavioral principles, ensures platform-specific sections, and makes each file authoritative rather than a copy of another. Trigger when the user says "optimize my CLAUDE.md", "audit agent instructions", "improve my CLAUDE.md", "apply Karpathy principles to my agent files", "clean up my copilot instructions", "review my GEMINI.md", or "update my AI instruction files".

os-clean-locks

Safely removes all agent lock files from the context/.locks/ directory to resolve deadlocks caused by crashed agents leaving stale locks behind. Use when the user says "/os-clean-locks", "clear all locks", "reset agent locks", or when an agent is deadlocked and cannot acquire a lock because a previous agent crashed and left a stale lock behind in context/.locks/. Verifies lock existence, discovers and removes stale lock directories, updates OS state via kernel.py, and emits event bus notifications. Requires Python 3.8+ standard library only.

Code & Development Listed

os-guide

Trigger with "explain agentic os", "how do I set up a persistent agent environment", "what is the CLAUDE.md hierarchy", "explain the context folder structure", "how does session memory work", "what is soul.md or user.md", "explain auto-memory or MEMORY.md", "what is a loop scheduler or heartbeat", or when the user asks for the canonical guide.

os-improvement-report

Trigger with "show me the improvement chart", "how are we improving", "progress report", "graph the eval scores", "show cycle of improvement", "what's the trend", "are we getting better". Produces a visual/text summary of how the agentic loop is improving across cycles. Do NOT use this to run the learning loop or evaluate a specific skill change.

os-init

Trigger: "set up agentic OS", "initialize agent harness", "init my project for AI agents", "where do I put CLAUDE.md", "create my agent environment", "set up persistent memory". Guides users through an interview to understand their use case, then scaffolds the right Agentic OS structure. Use even when the user just asks WHERE to put files.

os-memory-manager

Trigger with "remember this", "update memory", "what should we record from this session", "capture learnings", "write a session log", or when closing a session. Guides agents on managing memory hygiene across sessions, deciding what to write to dated memory logs, what to promote to long-term memory.md, and when to archive. <example> User: I'm done for the day, can you write up a session log? Agent: <Bash> python context/kernel.py emit_event --agent os-memory-manager --type intent --action promote_memory python context/kernel.py state_update active_agent os-memory-manager </Bash> </example> <example> User: That's all, logging off now. Agent: <Bash> python context/kernel.py acquire_lock memory </Bash> </example> <example> User: How does the memory system work? Agent: <Read> ./references/architecture/context-folder-patterns.md </Read> </example>

create-command

This skill should be used when the user asks to "create a slash command", "add a command", "write a custom command", "define a command with arguments", "create a command that runs bash", "add a /command to my plugin", "use $ARGUMENTS in a command", "set up argument-hint", "create a workflow command", "interactive command", or needs guidance on slash command structure, YAML frontmatter fields, file references, bash execution, command organization, or command best practices. Use this skill whenever Claude Code slash commands are mentioned even without the word "command" -- e.g. "I want a shortcut that reviews PRs" or "automate my deploy workflow" should trigger this. Do NOT use this for hooks (use create-hook), skills (use create-skill), or agents (use create-sub-agent).

API & Backend Listed

ecosystem-authoritative-sources

Provides information about how to create, structure, install, and audit Agent Skills, Plugins, Antigravity Workflows, and Sub-agents. Trigger this when specifications, rules, or best practices for the ecosystem are required.

ecosystem-standards

Provides active execution protocols to rigorously audit how code, directory structures, and agent actions comply with the authoritative ecosystem specs. Trigger when validating new skills, plugins, or workflows.

fix-plugin-paths

Fixes broken path references in plugin skill and agent files to ensure portability across installed environments. Use when you see "plugins/" paths in SKILL.md or agent files, need to standardize path references after installing a skill, want to audit and fix cross-plugin path dependencies, run a portability audit on a repository, neutralize hardcoded machine paths like /Users/, find Python scripts using PROJECT_ROOT or Path() to reach into plugins/<name>/ at runtime, or are preparing plugin files for distribution via uvx or bootstrap.py. Also handles evolving a skill in-session while tracking quality scores with the eval runner to continuously improve skill routing accuracy.

install-apm-package

Activate when the user wants to install, deploy, test, or materialize an APM package into runtime directories such as .agents/, .github/, .claude/, .cursor/, .gemini/, .codex/, .opencode/, or .windsurf/. Use after creating or converting an APM package.

manage-marketplace

This skill should be used when the user wants to "create a marketplace", "setup a marketplace catalog", "scaffold marketplace.json", "initialize a plugin registry", or "configure a Gemini CLI extension". Use this even if they just mention "setting up a marketplace".

rlm-curator

Knowledge Curator agent skill for the RLM Factory. Auto-invoked when tasks involve distilling code summaries, querying the semantic ledger, auditing cache coverage, or maintaining RLM hygiene. Supports both Ollama-based batch distillation and agent-powered direct summarization. V2 enforces Concurrency Safety constraints.

rlm-distill-agent

Distills uncached files into the Recursive Language Model(RLM) Summary cache Ledger. You (the agent) ARE the distillation engine. Read each file deeply, write a high-quality 1-sentence summary, inject it via inject_summary.py. The purpose is if you read the full file once and produce a great summary once it will avoid the need to read the file every time you need to know what the script does or what the details of the file are. most cases the RLM summary should be sufficient. Use when files are missing from the ledger and need to be summarized. <example> user: "Summarize these new plugin files into the RLM ledger" assistant: "I'll use rlm-distill-agent to read and summarize each file into the cache." </example> <example> user: "The RLM ledger is missing 40 files -- fill the gaps" assistant: "I'll use rlm-distill-agent to process the missing files." </example>

rlm-init

Interactive RLM cache initialization. Use when: setting up a new project's semantic cache for the first time, or adding a new cache profile. Walks the user through folder selection, extension config, manifest creation, and first distillation pass.

rlm-search

3-Phase Knowledge Search strategy for the RLM Factory ecosystem. Auto-invoked when tasks involve finding code, documentation, or architecture context in the repository. Enforces the optimal search order: RLM Summary Scan (O(1)) -> Vector DB Semantic Search -> Grep/Exact Match. Never skip phases.

vector-db-init

Interactively initializes the Vector DB plugin. Guided discovery asks which folders to index, confirms the manifest, then scaffolds vector_profiles.json for high-performance In-Process or Native Server connections. Mandatory first step before ingestion or search.

vector-db-launch

Start the Native Python ChromaDB background server. Use when semantic search returns connection refused on port 8110, or when the user wants to enable concurrent agent read/writes.

create-azure-agent

Deploy a skill as an Azure AI Foundry hosted agent

create-mcp-integration

Add an MCP server integration to a plugin

todo-check

Audit a file for TODO comments, pending work items, or technical debt markers. Useful for checking code readiness before a commit or reviewing task status. Trigger with "check for todos", "audit for debt", "list pending work", or "scan for TODOs".

Code & Development Listed

compile-apm-package

Activate when the user wants to compile an APM package into top-level context documents such as AGENTS.md, CLAUDE.md, or GEMINI.md, especially for Codex, Gemini, OpenCode, or agents-protocol style hosts. Do not use when the user only needs per-skill installation; use install-apm-package instead.

convert-plugin-to-apm

Activate when the user wants to add APM governance, lockfile/audit readiness, or multi-runtime package management to an existing Claude/Copilot/agent plugin, or explicitly convert a plugin into an APM-native package.

create-apm-package

Activate when the user wants to create a new APM-native package from scratch for reusable agent skills, agents, commands, hooks, MCP configuration, prompts, or governance-managed agent assets. Do not use this for existing plugin migration; use convert-plugin-to-apm instead.

rlm-audit

Audit RLM cache coverage - compare manifest against filesystem

rlm-cleanup-agent

Removes stale and orphaned entries from the RLM Summary Ledger. Use after files are deleted, renamed, or moved to keep the ledger in sync with the filesystem. <example> user: "Clean up the RLM cache after I renamed some files" assistant: "I'll use rlm-cleanup-agent to remove stale entries from the ledger." </example> <example> user: "The RLM ledger has entries for files that no longer exist" assistant: "I'll run rlm-cleanup-agent to prune orphaned entries." </example>

vector-db-audit

Audit Vector DB coverage -- compares the live filesystem manifest against the ChromaDB index to identify coverage gaps.

vector-db-cleanup

Removes stale and orphaned chunks from the ChromaDB vector store for files that have been deleted or renamed. Use after files are removed or moved to keep the vector index in sync with the filesystem. <example> user: "Clean up the vector store after I deleted some files" assistant: "I'll use vector-db-cleanup to remove orphaned chunks." </example> <example> user: "The vector database has chunks for files that no longer exist" assistant: "I'll run vector-db-cleanup to prune them." </example>

vector-db-ingest

Ingests repository files into the ChromaDB vector store. Builds or updates the vector index from a manifest or directory scan using ingest.py. Use when new files need to be indexed or the vector store is out of date. <example> user: "Index these new plugin files into the vector database" assistant: "I'll use vector-db-ingest to add them to the vector store." </example> <example> user: "The vector store is missing recent files -- update it" assistant: "I'll use vector-db-ingest to re-index the changes." </example>

vector-db-search

Semantic search skill for retrieving code and documentation from the ChromaDB vector store. Use when you need concept-based search across the repository (Phase 2 of the 3-phase search protocol). V2 includes L4/L5 retrieval constraints.

Testing & QA Listed

spec-kitty-dashboard

Open the Spec Kitty dashboard in your browser.

4 Updated 1 months ago