Tyler-R-Kendrick/copilot-auto-training

trainer-optimize

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.

trainer-train-agent

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…

trainer-train-code

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…

trainer-train-code

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…

trainer-train-prompt

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…

trainer-train-prompt

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…

trainer-train

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…

trainer-train-skill

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…

trainer-train

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…

trainer-train

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…

trainer-train

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…

agentv

Create, run, and manage AgentV evaluations for AI agents and skills using the AgentV CLI and AgentEvals standard EVAL.yaml format. Use this skill whenever the user wants to write evaluation files for AI agents, run evals with agentv CLI, convert existing test…

agentv

Create, run, and manage AgentV evaluations for AI agents and skills using the AgentV CLI and AgentEvals standard EVAL.yaml format. Use this skill whenever the user wants to write evaluation files for AI agents, run evals with agentv CLI, convert existing test…

engineer-code

Improve Python implementations with Microsoft Trace by turning prompts, helper functions, or small agent components into trainable code. Use this whenever the user wants to apply Trace or trace-opt, optimize Python behavior from tests or feedback, make a…

engineer-copilot-agent

Improve GitHub Copilot custom agents by validating agent contracts, tightening tool and MCP skill routing, and minimizing prompt bloat while keeping handoffs bounded to real workspace agents. Use this whenever the user wants to create, debug, or refine a…

engineer-prompt

Improve broken prompts and context plans by choosing the smallest prompt-engineering technique that fits. Use this whenever the user asks how to rewrite or debug a prompt, compare prompt-design options, choose between grounding, structured output, examples,…

engineer-skill

Improve agent skills by validating structure, optimizing YAML frontmatter for triggering accuracy, and refining SKILL.md prompt content for reliable agent behavior. Use this whenever the user wants to create, improve, debug, or optimize an agent skill, fix…

judge-outcome

Evaluate final outputs, response pairs, scored artifacts, or benchmark-style answer quality without relying on full trajectories. Use this whenever the judging task is mainly about end-state quality, final answer comparison, reference-plus-criteria scoring,…

judge-rubric

Generate a formalized rubric for scoring, grading, or evaluation in the current domain. Use when a judging task needs locked dimensions, pass-partial-fail boundaries, evidence requirements, tie-breakers, or confidence guidance before candidate comparison.

judge-trajectory

Evaluate agent trajectories, tool-use traces, intermediate artifacts, runtime failures, and side effects when process quality is part of the verdict. Use this whenever the judging task involves agent runs, tool calls, planning quality, web or code traces,…

learn

Capture user corrections and reusable lessons from the active conversation, then update the right persistent artifact so the same mistake is less likely to happen again. Use this whenever the user wants a fix reflected in agent memory, `.agents/MEMORY.md`,…

researcher-research

Research public datasets, benchmarks, documentation, and source material for official skill eval cases. Use this skill whenever a prompt or skill needs grounded public examples, authoritative dataset references, or a primary-source brief before synthesis or…

trainer-election

Elect the strongest prompt or skill candidate from an existing evaluation workspace. Use this skill whenever a workflow already has multiple scored configurations and needs a separate leader-selection pass over grading, timing, or benchmark artifacts,…

trainer-synthesize

Build official `evals/evals.json` cases and explicit APO datasets from grounded or computed source data. Use whenever a prompt or skill needs eval rows, `train.jsonl`, or `val.jsonl`, especially when correct outputs must be derived from raw fields, business…

trainer-optimize

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.

create-workflow

Create or update a GitHub Agentic Workflow in .github/workflows using gh aw, including frontmatter, markdown instructions, optional MCP servers, compilation, and debugging. Use this whenever the user wants new repository automation, wants to turn a repeated…

judge-rubric

Generate a formalized rubric for scoring, grading, or evaluation in the current domain. Use when a judging task needs locked dimensions, pass-partial-fail boundaries, evidence requirements, tie-breakers, or confidence guidance before candidate comparison.

trainer-election

Elect the strongest prompt or skill candidate from an existing evaluation workspace. Use this skill whenever a workflow already has multiple scored configurations and needs a separate leader-selection pass over grading, timing, or benchmark artifacts,…