advisor

name	advisor
description	Analyze the current project for autoresearch optimization opportunities. Scans the project, identifies candidates matching the keep/revert loop pattern (single mutable artifact, scalar metric, fixed time budget, repeatable execution), and presents ready-to-run suggestions with workflow.yaml snippets. Use when the user asks what they can optimize, what to autoresearch, or how to apply the optimization loop to their project.
argument-hint	[path — defaults to current directory]
user-invocable	true
allowed-tools	Bash Read Glob Grep

Harness-Optimize Advisor

Analyze a project for autoresearch optimization opportunities and present findings conversationally with concrete, ready-to-use workflow config snippets. Speak to the user — do not return raw JSON.

Hardware context

cat "${CLAUDE_PLUGIN_DATA:-${HOME}/.harness}/config.json" 2>/dev/null || echo "NO_CONFIG"

If output is NO_CONFIG, tell the user to run /harness:setup first to register hardware targets, then continue the analysis using target: local as a placeholder in any suggested workflow snippets.

Step 1: Determine the target directory

If $ARGUMENTS is provided, treat it as the path to analyze. Otherwise use the current working directory.

ls -la ${ARGUMENTS:-.}

Read key files: README.md, package.json, pyproject.toml, Cargo.toml, Makefile, main entry points, and any existing benchmark or test scripts. For ML repos specifically, look for train.py, program.md, prepare.py.

Step 2: Apply the four-condition test

The autoresearch keep/revert loop works when ALL FOUR conditions hold:

#	Condition	What to look for
1	Single mutable artifact	One file the agent edits each iteration
2	Scalar automated metric	A number computable without human judgment (lower or higher is better)
3	Fixed time-boxed cycle	Every experiment runs for identical wall-clock time
4	Repeatable automated execution	End-to-end, no human in the loop

For each candidate you identify, check all four honestly. If the metric requires human judgment or execution isn't fully automated, say so — don't oversell weak fits.

Strong candidate domains:

Domain	Typical artifact	Typical metric	Typical budget
ML training	train.py	val_bpb, val_loss	5 min
API latency	route handler	p99 response time (ms)	2–5 min load test
Database queries	query/schema file	execution time (ms)	1–3 min benchmark
Build pipeline	build config	build duration (s)	single build
Bundle size	bundler config	output bytes	single build
Inference speed	serving/model code	tokens/sec	fixed eval run
Prompt quality	prompt template	accuracy on eval set	eval run time
ETL pipeline	transform script	wall-clock time	fixed dataset

Do not suggest:

• Subjective quality with no automated metric
• Changes spanning multiple files (violates single-artifact constraint)
• Tasks requiring human judgment to evaluate

Step 3: Match to hardware

Use the hardware config injected above to recommend the best target for each candidate:

Scenario	Recommend
PyTorch / CUDA workload	server or runpod
Overnight unattended run	server or runpod — frees the Mac
Quick daytime iteration	local
Apple Silicon / MLX workload	local
No targets configured	Suggest /harness:setup before running

Step 4: Present findings

Present 2–3 concrete suggestions as prose. For each:

1. Name it and explain why it fits the pattern
2. Artifact — the exact file the agent would edit
3. Metric — what to measure and how to extract it from output (show the grep/regex if needed)
4. Hardware target — which one and why
5. Setup needed — be explicit if a benchmark script must be written first, data must be prepared, etc.

Then show a ready-to-copy workflow.yaml snippet for each:

# .harness/workflow.yaml
name: optimize-<what>
phases:
  - id: optimize
    plugin: harness
    type: loop
    config:
      artifact: "path/to/artifact.py"
      metric: "metric_name"
      direction: lower          # or higher
      run_command: "command to run"
      time_budget: "5m"
      max_experiments: 30
      target: local             # or server / runpod
      # ssh_host: "user@host"   # required if target: server
      # cwd: "/remote/path"     # required if target: server

Fill in actual values from your analysis — never leave placeholder text in the snippet.

Step 5: Offer next steps

After presenting suggestions, close with:

"Want me to build the full workflow.yaml for one of these? If you need a benchmark script written first, I can help with that too. Or run /harness:setup to register your hardware targets before we start."

If the project has no strong candidates, say so plainly:

"I didn't find a strong fit for the autoresearch pattern here. The main blocker is [specific reason]. To make it work, you'd need [what's missing]."

Constraints

• Prose output only — no raw JSON to the user
• Name actual files and commands found in the project — no generic placeholders
• Maximum 3 suggestions — quality over quantity
• Be honest about weak fits; a missing benchmark script is a real blocker, say so
• If metric extraction requires a custom parser or grep, show it explicitly