| name | harness |
| description | Pi-native evaluate→improve→persist harness. Provides a systematic loop for generating candidates, judging them against rubrics, and accumulating knowledge as playbooks. Use for iterative output improvement, not for one-off edits (use continuity) or single PR review (use zereight-review).
|
| argument-hint | [solve|knowledge|status] |
harness (Pi-native evaluation loop)
Builds on autoctx (judge/improve core) + Pi tools to provide a coherent harness.
Stack
| Layer | Source | Role |
|---|
| Extension | ~/.pi/agent/extensions/harness/index.ts | Tools + TUI + lifecycle |
| Judge engine | autoctx (via pi-autocontext dep) | LLM-based evaluation |
| Skill | .agents/skills/harness/SKILL.md | Usage guide + routing |
Tools
| Tool | Description |
|---|
harness_solve | Full loop: generate → judge → improve → persist playbook |
harness_knowledge | Read/write/list playbooks (knowledge/<scenario>/playbook.md) |
harness_status | Recent runs, scores, and scenarios overview |
Usage
# Solve a task with rubric-driven improvement
goal: "Improve zereight-review checklist for RN PRs"
rubric: "Actionable findings, Effect anti-patterns, security when relevant"
→ Best output + playbook saved to knowledge/<scenario>/
# Check previous runs
→ Recent runs with scores and scenarios
# Browse accumulated knowledge
action: list → all scenarios with playbooks
action: read, scenario: zereight-review → full playbook
When to use
| Need | Use |
|---|
| Systematic output improvement | harness_solve |
| Review accumulated lessons | harness_knowledge read |
| Check past run history | harness_status |
| One-off judge/improve | autocontext_judge / autocontext_improve (pi-autocontext) |
Cost
harness_solve consumes Pi provider quota per generation round.- Start with
gens: 1 to calibrate rubric, then increase.
harness_knowledge and harness_status are zero-cost.
