Tyler-R-Kendrick

Use when producing agent/LLM evals, synthetic simulation data, or self-improvement pipelines for prompts, code, skills, agents, harnesses, and workflows. Covers AgentEvals/AgentV, Agent Skills evals, ASSERT, GEPA, Trace, VISTA, Agent Lightning, SkillOpt,…

Use when working with AI agent protocols, standards, interoperability specifications, evaluation contracts, synthetic simulation data, improvement pipelines, and agent steering workflows. Covers MCP, A2A, ACP, Agent Skills, AGENTS.md, ADL, Improve, x402, AP2,…

Use when a user corrects, rejects, edits, or redirects an LLM/agent response and the correction should become a reusable reasoning strategy. Converts feedback into generalized learnings for ~/.agents/STEERING.md with linked RDF/Turtle evidence. USE FOR: user…

Use when implementing the Agent-to-Agent (A2A) protocol for inter-agent communication, task delegation, and multi-agent collaboration. USE FOR: agent-to-agent communication, task delegation between agents, Agent Card publishing, multi-agent collaboration DO…

Use when implementing the Agent Communication Protocol (ACP) for REST-based agent-to-agent communication, task delegation, and multimodal message exchange. USE FOR: ACP agent servers, ACP client integration, agent discovery via manifests, run lifecycle…

Use when defining AI agents declaratively with Agent Definition Language (ADL). Covers agent identity, LLM configuration, tools, permissions, RAG inputs, and governance metadata. USE FOR: declarative agent blueprints, agent identity and permissions, LLM…

Use when creating, packaging, or distributing Agent Skills. Covers the SKILL.md specification, frontmatter schema, naming conventions, marketplace publishing, and the skills-ref validator. USE FOR: creating SKILL.md files, packaging reusable agent…

Use when creating or updating AGENTS.md files to guide AI coding agents. Covers file structure, placement, content guidelines, and best practices for project-level agent instructions. USE FOR: project-specific agent instructions, build/test commands for…

Use when evaluating models, writing or interpreting ship gates, claiming readiness, changing parse/fidelity/reward metrics, or deciding fixture-demo vs production ship

Operate the OpenUI SLM training pipeline end to end, including a continuous hands-off model and harness improvement loop. Bare /autotrain is non-terminating and must not stop for user confirmation; an explicit phase or --once is finite. Code fixes during…

How to do multi-step engineering work in this repo: parent agents plan and stack PRs with official GitHub Stacked PRs (`gh stack` / `gs`); subagents implement layers with incremental check-ins; parent closes every PR bottom-up with rubber-duck adversarial…

Use when a scheduled or interactive session should report the current autotrain evidence state — open hypothesis families, closed approaches, preflight blocks, pending confirmations, promoted-model status — as a short read-only markdown brief in chat

Close the data-quality loop after any training-data synthesis or build. Use whenever build_train_data (or the build_train_data job) runs, when a quality_report.json shows warnings, when planning changes to producers/synthesizers, or when deciding what data…

Discover and run the reasoning/revmath profile — reverse-mathematics / computability analysis over existing campaign, formal-preflight, evidence, and repair owners. Use when editing revmath schemas, fixtures, labeling, self-healing, four-axis ledgers, or…

Use when the Linear In Review queue for team SLM is backed up and an agent session should claim the oldest issues, finish each in an isolated git worktree (reusing open PRs), fix CI in bounded rounds, and squash-merge once merge preflight passes

Use when refreshing the generated OpenWiki pages under docs/openwiki — a scheduled or manual session installs the pinned OpenWiki CLI, selects a provider key, runs scripts.update_openwiki, and opens a PR on branch openwiki/update (skips cleanly when no…

Improve a markdown prompt file using Agent Lightning APO (Automatic Prompt Optimization). Use when the user asks to optimize or improve a markdown prompt, or starts a message with /trainer-optimize.

Own the end-to-end trainer loop for agent contract targets (*.agent.md files, custom agent definitions, and agent instruction documents). Use this whenever the caller needs to research, synthesize datasets, optimize, validate, and write back a trained…

Own the end-to-end trainer loop for Python code targets optimized with Microsoft Trace (nodes, bundles, models, and trainable agent components). Use this whenever the caller needs to research, synthesize test-based datasets, optimize, validate, and write back…

Own the end-to-end trainer loop for Python code targets optimized with Microsoft Trace (nodes, bundles, models, and trainable agent components). Use this whenever the caller needs to research, synthesize test-based datasets, optimize, validate, and write back…

Own the end-to-end trainer loop for prompt-like files (*.prompt.md, *.prompty, *.instructions.md, system prompts, and other natural-language instruction artifacts). Use this whenever the caller needs to research, synthesize datasets, optimize, validate, and…

Own the end-to-end trainer loop for prompt-like files (*.prompt.md, *.prompty, *.instructions.md, system prompts, and other natural-language instruction artifacts). Use this whenever the caller needs to research, synthesize datasets, optimize, validate, and…

Own the end-to-end trainer loop contract for a prompt-like file, skill contract, or agent contract after the caller has already chosen the concrete stage capabilities. Use this whenever the current agent must set up the local trainer workspace, coordinate…

Own the end-to-end trainer loop for agent skill targets (SKILL.md files and their supporting references, scripts, and evals). Use this whenever the caller needs to research, synthesize datasets, optimize, validate, and write back a trained candidate for a…

This skill helps an LLM pick the right AxAgent context tool for a job - contextMap for recurring corpora, contextPolicy presets for within-run trajectory compaction, agent.optimize for offline GEPA instruction/demo tuning, agent.playbook for an evolving…

This skill helps an LLM generate correct AxAgent memory retrieval, context-map, and dynamic skill-loading code using @ax-llm/ax. Use when the user asks about contextMap, AxAgentContextMap, onMemoriesSearch, memoriesCatalog, recall(...), inputs.memories,…

This skill helps an LLM generate correct AxAgent observability code using @ax-llm/ax. Use when the user asks about actorTurnCallback, onContextEvent, agentStatusCallback, onFunctionCall, reportSuccess, reportFailure, getChatLog(), getUsage(), resetUsage(),…

This skill helps an LLM generate correct AxAgent tuning and evaluation code using @ax-llm/ax. Use when the user asks about agent.optimize(...), judgeOptions, eval datasets, optimization targets, saved optimizedProgram artifacts, or agent optimization guidance.

This skill helps an LLM generate correct AxAgent RLM/runtime code using @ax-llm/ax. Use when the user asks about RLM code execution, AxJSRuntime, contextFields, contextPolicy, liveRuntimeState, promptLevel, stage prompt controls, executorModelPolicy,…

This skill helps an LLM generate correct core AxAgent code using @ax-llm/ax. Use when the user asks about agent(), child agents, namespaced functions, discovery mode, clarification, bubbleErrors, host-side final/clarification protocol, or ordinary agent…

This skill helps an LLM generate correct AI provider setup and configuration code using @ax-llm/ax. Use when the user asks about ai(), providers, models, presets, embeddings, batch audio with ai.transcribe() or ai.speak(), extended thinking, context caching,…

This skill helps an LLM generate correct audio code with @ax-llm/ax. Use when the user asks about ai.transcribe(), ai.speak(), signature audio inputs or outputs, agent audio behavior, .chat() conversational audio, OpenAI audio or realtime models, Gemini Live…

Use when coordinating, inspecting, running, or verifying the Project Khepri modernization workflow, Microsoft Agent Framework workflow, GHCP SDK custom-agent registry, increment squad workflow, AgentEvals gates, or app/data/infra/security modernization…

Deterministic workflow for GitHub Copilot modernization agents.

{what this skill teaches agents}

TDD loop for generating SDK-first squads with AgentEvals, evaluators, test data, rubrics, and live-evals.

Use when architecture-affecting code, agent profiles, workflow contracts, hooks, skills, MCP configuration, evals, CI, repository structure, or documentation changes could make Project Khepri docs or Mermaid diagrams stale.

Use this skill when the user corrects an agent, says prior behavior was wrong, asks the agent to remember a preference, or gives steering that should prevent repeat mistakes. Capture a succinct generalized correction in STEERING.md for all Project Khepri…

Discovery checklist for scoping modernization before implementation.

Planner and evolution handshake for generating a modernization-specific squad.

Runs scoped research as a composite skill through the shared skill registry and router.

Create and maintain paper research packets in this repo with the required research/<paper>/ layout, architecture docs, and experiment implementations aligned to the agent-browser TypeScript stack. Use this whenever the user asks to add a paper, summarize…

ARIS-inspired deep research workflow with adversarial executor/reviewer loops, persistent memory, and claim-evidence assurance checks.

Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.

Operating guide for the agent-browser workspace shell. Use this whenever the user asks how to inspect, navigate, or modify the active agent-browser workspace, Files surface, browser pages, sessions, clipboard history, render panes, or WebMCP tool flows.…

Create an AgentEvals-style eval suite for a named agent under .agents/<agent-name>/.evals/. Use this whenever the user asks for an eval, regression suite, benchmark, or repeatable acceptance test for a workspace agent. Prefer it even when the user asks for a…

Create a scoped agent folder with an AGENTS.md file under .agents/<agent-name>/. Use this whenever the user asks for a new agent, reusable agent instructions, a workspace-scoped AGENTS.md, or a named automation persona inside the current workspace. Prefer it…

Create a reusable agent skill bundle under .agents/skills/<skill-name>/ that follows agentskills.io conventions. Use this whenever the user asks for a SKILL.md, reusable workflow skill, skill scaffold, or packaged agent capability inside the current…

Capture before/after screenshots for any user-visible change and post them on the pull request, so a reviewer never has to run the app to see what changed. Use whenever a change touches CSS, a component, a screen, layout, chrome, copy shown on screen, an…

Attach a live hot-reload debug session when the human asks to run the app locally. Watch real console, page, and network errors and patch the same session. Use when: "run locally", "run the app", "start the server", "let me test", "debug this", "hot reload",…

Apply a refined Scandinavian visual system with a neutral black-and-white foundation, restrained sans-serif typography, purposeful product imagery, generous spacing, clear page chapters, and minimal decoration. Use when the user invokes /scandinavian-design…

Install, configure, initialize, and use OpenSesame MCP servers

Run and interpret OpenSesame security reviews using the repository's deterministic gates and targeted Codex Security scans. Use for security audits, SAST, scanner findings, auth or crypto changes, and pre-merge security evidence.

Install and configure the generic and optional Effect anti-slop Oxlint plugins in a local TypeScript or JavaScript repository. Use whenever a user asks to add anti-slop lint rules, copy the anti-slop plugin, configure opinionated Oxlint rules, or migrate an…

Install, configure, initialize, and use OpenSesame host and client CLIs

Install, configure, initialize, and use OpenSesame Host and Identity APIs

Configure and use reputable web scraping for TK TechNews. Use when an agent needs Firecrawl MCP, Firecrawl-backed scraping, dynamic page extraction, web source troubleshooting, or guidance on when to use RSS, local scraping, Firecrawl, or YouTube transcripts…

Use the local TK TechNews YouTube Data API MCP server. Trigger when an agent needs YouTube channel info, playlist info, playlist videos, video metadata, YouTube search, caption track listing, caption downloads, or VS Code MCP setup for the repo's local…

Use the local TK TechNews MCP server wrapping jdepoix/youtube-transcript-api. Trigger when an agent needs YouTube transcripts, generated subtitles, transcript language discovery, translation, SRT/VTT/text/JSON transcript output, or VS Code MCP setup for…

Draft cited TK TechNews articles from the normalized source summary ledger. Use when an agent needs to turn data/summaries/latest.json into a Markdown article, preserve citations in frontmatter, or create an initial explainer draft for editorial refinement.

Run the durable TK TechNews source-to-article pipeline. Use when an agent needs to ingest one URI, create a cited source brief, enrich it into the temporal knowledge graph, aggregate enriched docs by day, or generate an Astro article from an aggregate brief.

Validate and publish TK TechNews static Astro content. Use when an agent needs to check citation integrity, run the Astro build, preview the site, or prepare a generated article for commit or deployment.

Collect technology-news source material for TK TechNews. Use when an agent needs to fetch RSS feed items, scrape configured web pages, pull YouTube transcripts, refresh data/summaries/latest.json, or prepare source summaries before drafting an article in this…

Use the speculate skill when you need critique-ready hypotheses from new observations: recall context, run the mutation pipeline on a fresh observation, retrieve ranked inference and facet results from an inference/* branch, and present candidate text,…

Use the discover skill to find connections between claims through facet relations and to rank inference and facet candidates using manifold geometry. Covers facet modeling, manifold scoring, relatedness, distance, uncertainty, and retrieval of discovery…

Use the infer skill to generate speculative inference candidates from claims, persist them on branch-local inference graphs, and run the full mutation pipeline that coordinates active memory, claims, Terminus persistence, and speculative ranking.

Use the memorize skill to store observations, entities, tasks, and claims into the filesystem-backed active memory. Covers session lifecycle, working-set writes, entity/task card creation, claim extraction, and candidate promotion.

Use the recall skill to retrieve composed context from active memory, trusted Terminus graphs, and optional speculative branches. Also covers journal-backed history of past working-memory mutations.

Use the reflect skill to persist memories, claims, and inference nodes into the TerminusDB temporal graph. Covers branch lifecycle, trusted session writes, temporal recall, and the separation between session and inference branches.

Create, load, merge, and distribute persistent artifact plugin bundles for activation steering. Use this skill when the user wants to manage steering artifacts, create distributable plugin packs, load model bundles, merge multiple plugins, write new artifact…

Define, discover, and manage cognitive feature specifications and steering vectors for LLM activation steering. Use this skill when the user wants to create feature specs, define extraction examples with positive/negative labels, set evaluation criteria,…

Use when creating, compiling, validating, running, or debugging GitHub Agentic Workflows with the gh-aw CLI.

Build and query Neo4j-backed reasoning trajectory graphs for the hybrid meta-cognition agent. Use this skill when the user wants to persist task plans, subgoals, constraints, run states, and verifier outcomes in a Neo4j graph, retrieve evidence paths with…

Build and run a hybrid planner/retriever/steered-executor/verifier meta-cognition agent loop with persistent steering memory. Use this skill when the user wants to create an agent that plans tasks, retrieves context, applies activation steering, verifies…

Run activation-steering feature discovery for a Hugging Face model. Use this skill whenever the user says /steering, wants to steer a model, extract a cognitive feature, generate steering vectors, run feature discovery, or produce steering artifacts. Specify…

Use, extend, test, and integrate Epoch's TypeScript DVCS, CLI, WASM, and platform packages.

Optimize human-centered experiences (UX, DX, AX) via persona-driven review, Gherkin features, evidence capture, and a dual-regime infinite loop—reduce harms/friction/uncertainty to a floor, then maximize delight (excitement/ease/optimality) under harm…

Coordinate Epoch feature delivery end to end with persona-first BDD/TDD, stacked PRs, incremental commits, automatic reviews, anti-slop + DESIGN.md gates, Pact at integration boundaries, evidence packs posted on PRs, explicit subagent/branch/worktree…

Runs spec-grounded, counterexample-driven improvement campaigns over code, agents, research, and multimodal artifacts. Use when a task needs durable iteration, materialized candidates, trace analysis, independent evaluators, replayable experiments,…

Official Pact consumer-driven contract testing for Epoch service boundaries. Use when writing or verifying Pact contracts, HTTP client/provider integration tests, gossip/Community API contracts, or when the user says "pact", "contract test", "consumer driven…

Use when configuring, running, or verifying the UTK model proxy — the OpenAI-compatible local context gateway for GitHub Copilot — including history compaction into recoverable [utk-block] summaries, deferred tool discovery (utk_find_tool), repeated/stale…

Use when initializing Universal Token Killer schema artifacts for a project, discovering registered Copilot tools, seeding .utk output schemas, or narrowing schema generation to specific tool ids with descriptions or sample outputs

Use when installing, operating, recovering, or extending Universal Token Killer for GitHub Copilot tool-hook mediation, .utk artifacts, serializer configuration, schema routing, compact tool-output recovery, or RTK-parity token optimization

Use when compressing prompts, agent skills, AGENTS.md files, GitHub Copilot custom agents, subagent definitions, or bulky artifacts before sending text to LLM context; includes detoks-prompt CLI/MCP use, skill consolidation, AGENTS.md memory/skill extraction,…

Use when optimizing Agent Skills for lower token usage, creating compact companion skills, splitting skill instructions into references, extracting deterministic workflow scripts, or adding eval-backed skill compression