xvision-autooptimizer-ops

name	xvision-autooptimizer-ops
description	Operate the xvision autooptimizer: distill Observations into candidate Patterns, run the gate against today and the untouched test period, record blind findings, activate or retire Patterns, and preserve the lineage evidence for audit.

xvision autooptimizer ops

Use this skill for offline Pattern distillation work. AutoOptimizer commands are offline-only; do not run them inside a live trading decision process.

When to use

Run this skill when you are offline, outside a live trading decision, and need to distill candidate Patterns from Observations, run the gate, inspect a run, activate a passing run, or retire a Pattern.

When NOT to use

• Inspecting Pattern or Observation inventory → use xvision-memory-ops
• Reading flywheel velocity or overall health → use xvision-flywheel-ops
• Any task involving a live trading cycle or real-time data → no skill applies

Trigger examples

• Distill a new Pattern from last week's Observations for agent abc123
• Run the autoresearch gate on run_id xyz with a Sharpe baseline of 0.8
• Activate run xyz after the gate passed
• Retire Pattern p_001 — soft delete only
• Inspect autoresearch run xyz and show me the gate output JSON
• Write a blind finding for this autoresearch run before I see the scores
• Check whether Pattern p_002's training cutoff is correct

Standard flow

Note: xvn optimizer is deprecated. All sub-commands print a deprecation notice and delegate to xvn optimize. Use xvn optimize directly in new scripts.

# Run the full optimizer cycle against a strategy
xvn optimize run --strategy <strategy_id> --cycles 3 --json

# Or run a single cycle with fine-grained control
xvn optimize run-cycle --strategy <strategy_id> \
  --budget 100 --provider openrouter --model kimi-k2 \
  --objective sharpe --experiments-per-cycle 5 \
  --json

# Inspect a persisted optimization run
xvn optimize inspect <run_id> --json

The gate, activate, and retire operations are now managed via the dashboard Optimizer panel. Use xvn optimize inspect <run_id> to fetch run state for audit.

Invariants

• Never activate from a single Observation cohort.
• The gate's numeric decision (Kept / Dropped) must pass independently before activation.
• The finding is qualitative context, not the verdict.
• A Pattern's training cutoff must come from the latest source Observation's bar timestamp, not wall clock time.
• Retiring is soft-delete first; hard delete requires explicit operator confirmation through the memory janitor path.

Evidence to capture

• xvn optimize inspect <run_id> --json
• Gate input/output JSON, including the blind finding fields.
• Activated Pattern row and contributing Observation ids.
• Look-ahead-protection regression output before declaring an activation path change done.

Gotchas

Single-cohort activation: Never activate from one Observation cohort — the invariants section states this. The gate must pass first.

Wall-clock training cutoff: The cutoff must come from the latest source Observation's bar timestamp, not the current date. Using wall clock time introduces look-ahead contamination.

Finding written after scores visible: The blind finding must be composed before the numeric gate scores are read. Running xvn optimize run-cycle and then writing the finding retrospectively breaks the audit trail.

Hard delete without janitor path: Retiring is soft-delete. Hard delete requires the explicit memory janitor confirmation flow — skipping it destroys lineage evidence.

Wrong skill for inventory inspection: xvn optimize inspect shows one run. Use xvision-memory-ops to list all Patterns or Observations across an agent.

Owner

autoresearch-ops track (team/contracts/autoresearch-ops.md)