Bare Eval — Isolated Evaluation Calls

Run claude -p --bare for fast, clean eval/grading without plugin overhead.

CC 2.1.81 required. The --bare flag skips hooks, LSP, plugin sync, and skill directory walks.

When to Use

• Grading skill outputs against assertions
• Trigger classification (which skill matches a prompt)
• Description optimization iterations
• Any scripted -p call that doesn't need plugins

When NOT to Use

• Testing skill routing (needs --plugin-dir)
• Testing agent orchestration (needs full plugin context)
• Interactive sessions

Prerequisites

# --bare requires ANTHROPIC_API_KEY (OAuth/keychain disabled)
export ANTHROPIC_API_KEY="sk-ant-..."

# Verify CC version
claude --version  # Must be >= 2.1.81

Quick Reference

Call Type	Command Pattern
Grading	claude -p "$prompt" --bare --max-turns 1 --output-format text
Trigger	claude -p "$prompt" --bare --json-schema "$schema" --output-format json
Streaming grade	claude -p "$prompt" --bare --max-turns 1 --output-format stream-json
Optimize	echo "$prompt" | claude -p --bare --max-turns 1 --output-format text
Force-skill	claude -p "$prompt" --bare --print --append-system-prompt "$content"
@-file in prompt	claude -p "grade @fixtures/case-1.md against rubric" --bare (CC 2.1.113 Remote Control autocomplete)

--output-format stream-json

Newline-delimited JSON events (one per token/tool-call) — lets a runner score partial output or abort early on a failing probe without waiting for the full response.

claude -p "$prompt" --bare --max-turns 1 --output-format stream-json \
  | while IFS= read -r line; do
      # line is a single JSON event; inspect $.type == "content_block_delta"
      jq -r 'select(.type == "content_block_delta") | .delta.text' <<< "$line"
    done

Use stream-json over json when:

• grading long outputs and you want incremental scoring,
• piping into another CLI step-by-step (e.g. ork:eval-runner),
• you need per-token timing data alongside the content.

Invocation Patterns

Load detailed patterns and examples:

Read("${CLAUDE_SKILL_DIR}/references/invocation-patterns.md")

Grading Schemas

JSON schemas for structured eval output:

Read("${CLAUDE_SKILL_DIR}/references/grading-schemas.md")

Pipeline Integration

OrchestKit's eval scripts (npm run eval:skill) auto-detect bare mode:

# eval-common.sh detects ANTHROPIC_API_KEY → sets BARE_MODE=true
# Scripts add --bare to all non-plugin calls automatically

Bare calls: Trigger classification, force-skill, baseline, all grading. Never bare: run_with_skill (needs plugin context for routing tests).

Performance

Scenario	Without --bare	With --bare	Savings
Single grading call	~3-5s startup	~0.5-1s	2-4x
Trigger (per prompt)	~3-5s	~0.5-1s	2-4x
Full eval (50 calls)	~150-250s overhead	~25-50s	3-5x

Rules

Read("${CLAUDE_SKILL_DIR}/rules/_sections.md")

Troubleshooting

Read("${CLAUDE_SKILL_DIR}/references/troubleshooting.md")

Related

• eval:skill npm script — unified skill evaluation runner
• eval:trigger — trigger accuracy testing
• eval:quality — A/B quality comparison
• optimize-description.sh — iterative description improvement
• Version compatibility: doctor/references/version-compatibility.md