trainer-optimize

name	trainer-optimize
description	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.
license	MIT
compatibility	Requires Python 3.11+, agentlightning, and openai packages. Works in Claude Code and any agent that supports the Agent Skills standard.
metadata	{"author":"your-org","version":"0.1.0"}

Prompt Optimization with Agent Lightning

Use this skill to improve a single markdown prompt file by running Agent Lightning prompt optimization against explicit JSONL train and validation datasets, while keeping authored eval cases in the official evals/evals.json layout. The runtime is single-shot: it returns one optimized prompt result and does not coordinate leader election internally.

When to use this skill

• The user says "optimize my prompt", "improve this prompt", or starts with /trainer-optimize
• The target artifact is a single .md (markdown) prompt template file
• The user provides, or can provide, train and validation JSONL datasets with expected outputs

Do not use this skill for general code optimization or non-markdown prompts.

Required inputs

The skill always needs a prompt path. It also needs train and validation datasets. In this repository, authored eval cases live under evals/evals.json, while APO datasets are passed explicitly.

• prompt: path to the markdown prompt file to optimize
• train: path to the JSONL training dataset
• val: path to the JSONL validation dataset

If those files do not exist, stop and report the missing path instead of guessing. Do not synthesize missing datasets inside trainer-optimize. Any dataset creation or conversion must happen before this skill runs.

Invocation syntax

/trainer-optimize prompt=<path> train=<path> val=<path> [options]

Required arguments:

• prompt — path to the markdown prompt file to optimize
• train — path to the JSONL training dataset
• val — path to the JSONL validation dataset

Optional arguments:

• iterations (default 3) — number of APO beam rounds
• algorithm (default apo) — supported values are apo and verl
• output — optional path to write the optimized markdown without touching the source prompt
• report — where to write the JSON optimization report
• beam_width (default 4)
• branch_factor (default 4)
• n_runners (default 4)
• judge_mode (default deterministic) — supported values are deterministic, custom, and llm_judge
• judge_prompt_file — optional path for llm_judge; falls back to assets/judge-default.md
• debug_only — run a smoke-test pass only, do not write output files
• in_place — overwrite the original prompt file with the optimized result

Current implementation behavior

• Supported algorithms: apo and verl, selected through the same optimization pipeline.
• Supported judge modes: deterministic, custom, and llm_judge.
• The optimizer uses the repository root .env COPILOT_MODEL setting as its single model configuration.
• The runtime returns a dashboard_url in debug and normal JSON output. Unless AGL_SERVER_PORT is already set, it binds Agent Lightning to a free local port on 127.0.0.1 instead of assuming 4747.
• Prompt rendering uses placeholder-targeted substitution rather than whole-string Python str.format(). Literal JSON or other brace-heavy examples remain literal, escaped braces like {{literal}} stay supported, and nested placeholders such as {input.question} are allowed.
• When no inference model is configured, or a live optimize run loses model access because of rate limiting or service unavailability, the runtime returns a manual_followup JSON payload instead of failing. That payload includes deterministic preparation results plus an agent-side inference handoff: the current @trainer agent can answer the returned model_prompt, save the result as a candidate prompt, and continue the workflow without an inference API token.
• By default the optimized prompt is returned in the JSON result and CLI stdout without writing files.
• The source prompt is only overwritten when in_place is requested.
• output writes a separate optimized prompt file when requested.
• report writes a JSON report only when requested.
• In manual_followup mode, the source prompt is left unchanged. The JSON payload preserves the baseline prompt, explains the blocker, and includes the model_prompt, agent_handoff_instruction, and rerun_command needed for the current @trainer agent to finish the optimize stage without an inference API token.
• scripts/train.py can run a Microsoft Trace self-training loop over one or more explicit prompt/train/val cases to tune the single-shot optimize policy.
• Require at least one row in both train and val.
• The repository's authored skill eval cases live under next to the prompt or skill.

Process

1. Parse the /trainer-optimize arguments from the user's message.
2. Resolve explicit train and val dataset paths.
3. If datasets still do not exist, stop and report the missing explicit dataset arguments or paths.
4. Validate that all resolved file paths exist and are readable.
5. Read the prompt file and inspect a sample row from the training dataset.
6. Verify the train and validation files each contain at least one JSON object row.
7. Extract template placeholders and verify they are satisfiable from the sample task schema.
8. Keep evaluator-only fields such as expected, expected_json, reference, criteria, and scoring out of the prompt rendering path.
9. If the prompt contains literal JSON or brace-heavy examples, keep them literal or escaped and do not let them introduce accidental placeholders.
10. If the prompt or dataset shape is incompatible, stop and explain the mismatch clearly.
11. Run scripts/run_optimize.py with the resolved arguments.
12. Return the optimized prompt in JSON/stdout unless explicit persistence flags request file output.
13. If the runtime returns manual_followup, use the returned model_prompt through the current @trainer agent to draft the candidate prompt, then report the blocker, the handoff instruction, and the saved candidate artifact instead of claiming the code runtime produced the candidate directly.
14. Otherwise report the optimized prompt path, report path, dashboard URL, and optimization metadata from the JSON result.

Algorithm guidance

• Default to apo for most prompt-file optimization tasks.
• Use verl when the user explicitly wants the alternative algorithm or when repo policy prefers it.
• Prefer deterministic when exact or rule-based scoring is available.
• Use custom for normalization or schema-based scoring.
• Use llm_judge only when deterministic or custom scoring is not a good fit.

Output Modes

The runtime produces a single optimized prompt result. Persistence is explicit:

• Default: return the optimized prompt in JSON/stdout only.
• output provided: write a separate optimized prompt file.
• in_place enabled: overwrite the source prompt_file.
• report provided: write a JSON report file.

If a higher-level workflow wants to compare multiple optimize results, that comparison belongs outside this skill.

Dataset format

Each line in the JSONL file must be a JSON object. See references/dataset-format.md for details.

Recommended form:

{"input": "user request", "expected": "ideal answer", "scoring": "exact_match"}

Repository-authored eval convention:

skills/
  trainer-optimize/
    SKILL.md
    evals/
      evals.json
      files/

For APO runs, keep train.jsonl and val.jsonl as explicit dataset files and pass them on the command line.

Use evals/evals.json for authored evaluation cases, and keep runtime-only optimization output separate from the published eval manifest.

Trace self-training:

python scripts/train.py \
  --prompt-file ../../examples/first-run/prompts/classify_support.md \
  --train-file ../../examples/first-run/datasets/train.jsonl \
  --val-file ../../examples/first-run/datasets/val.jsonl \
  --epochs 2 \
  --report-file /tmp/trace-train-report.json

Outputs

After a successful run, the JSON result always contains the optimized prompt text and optimization metadata. Files are written only when requested via output, report, or in_place.

When the runtime returns manual_followup, it still reports the validated prompt/dataset metadata plus a model-ready optimization prompt and an agent-side handoff. This is the supported fallback when external models are unavailable.

scripts/train.py writes a JSON training report only when --report-file is passed; otherwise it prints the report to stdout.

Dashboard

During an active optimization run, Agent Lightning starts a local dashboard server and returns its URL in the JSON result as dashboard_url.

• Open the returned dashboard root URL in a browser while the run is active.
• Unless AGL_SERVER_PORT is preconfigured, the runtime chooses a free local port automatically.
• The dashboard health endpoint is <dashboard_url>/v1/agl/health.
• Available pages include Rollouts, Resources, Traces, Runners, and Settings.
• If the UI shows Offline, verify the backend base URL in Settings, confirm the optimization process is still running, and check whether you opened the current dashboard_url rather than an older fixed port.

Placeholder validation

Template placeholders such as {input} must match keys the optimizer can validate from the task rows. Double-brace escapes like {{literal}} are ignored. Literal JSON examples such as {"input": "user request"} are not placeholders and should stay literal. If any placeholder is missing from the dataset schema, stop and explain the mismatch to the user.

Error cases

• Missing or unreadable files → report the specific path and stop
• Empty train or val dataset → require at least one task row in each
• Placeholder mismatch → list the unmatched placeholders and the actual dataset fields
• Immediate rollout failure in the dashboard → treat as a runtime exception, inspect stderr and traces first, and do not describe it as a low score
• Dashboard port conflict or stale forwarded URL → use the returned dashboard_url, or set AGL_SERVER_PORT explicitly if you need a stable port
• Missing dataset paths → report the missing explicit arguments or unreadable paths and stop
• Unsupported judge mode → explain the supported modes: deterministic, custom, and llm_judge
• Unsupported algorithm → explain the supported algorithms: apo and verl
• Missing model configuration or transient model availability failures such as RateLimitError → return manual_followup with deterministic preparation output and an agent-side inference handoff so the current @trainer agent can finish the optimize stage without an inference token

Running the script

python scripts/run_optimize.py \
  --prompt-file <path> \
  [--train-file <path>] \
  [--val-file <path>] \
  [--iterations 3] \
  [--output-file <path>] \
  [--report-file <path>] \
  [--beam-width 4] \
  [--branch-factor 4] \
  [--n-runners 4] \
  [--algorithm apo|verl] \
  [--judge-mode deterministic|custom|llm_judge] \
  [--judge-prompt-file assets/judge-default.md] \
  [--debug-only] \
  [--in-place]

Use --help if you need to confirm available flags in the current script version.

Copilot .env example at the repository root:

COPILOT_MODEL=default

Use /.env.sample as the canonical template for the supported Copilot setting.

See references/dataset-format.md for the dataset contract and assets/examples.md for worked examples.