dev:dry-run
关于
The dev:dry-run skill performs smoke tests on the evolve pipeline to verify tools and plugins work end-to-end. It operates in two modes (online/offline) depending on LANGSMITH_API_KEY availability, checking tool syntax and argparse consistency. Use this skill when developers need to test pipelines or hear phrases like "dry run" or "smoke test."
快速安装
Claude Code
推荐npx skills add raphaelchristi/harness-evolver -a claude-code/plugin add https://github.com/raphaelchristi/harness-evolvergit clone https://github.com/raphaelchristi/harness-evolver.git ~/.claude/skills/dev:dry-run在 Claude Code 中复制并粘贴此命令以安装该技能
技能文档
/dev:dry-run
Smoke-test the evolve pipeline. Two modes depending on whether LANGSMITH_API_KEY is available.
Resolve Paths
TOOLS="${EVOLVER_TOOLS:-$([ -d "tools" ] && echo "tools" || echo "$HOME/.evolver/tools")}"
EVOLVER_PY="${EVOLVER_PY:-$([ -f "$HOME/.evolver/venv/bin/python" ] && echo "$HOME/.evolver/venv/bin/python" || echo "python3")}"
Check: Online or Offline?
if [ -n "$LANGSMITH_API_KEY" ]; then
echo "MODE: Online (LANGSMITH_API_KEY found)"
MODE="online"
else
echo "MODE: Offline (no LANGSMITH_API_KEY)"
MODE="offline"
fi
Offline Mode (no API key)
Validate tool syntax and argparse consistency:
echo "=== Tool Syntax Check ==="
for f in $TOOLS/*.py; do
python3 -c "import ast; ast.parse(open('$f').read())" 2>&1
if [ $? -eq 0 ]; then echo "OK: $(basename $f)"; else echo "FAIL: $(basename $f)"; fi
done
echo ""
echo "=== Argparse Flags Check ==="
for f in $TOOLS/*.py; do
$EVOLVER_PY "$f" --help > /dev/null 2>&1
if [ $? -eq 0 ]; then echo "OK: $(basename $f) --help"; else echo "FAIL: $(basename $f) --help"; fi
done
echo ""
echo "=== Skill Cross-Reference Check ==="
# Check every tool referenced in evolve skill exists
for TOOL in $(grep -oh '\$TOOLS/[a-z_]*.py' skills/evolve/SKILL.md | sed 's/\$TOOLS\///' | sort -u); do
if [ -f "$TOOLS/$TOOL" ]; then
echo "OK: $TOOL referenced and exists"
else
echo "FAIL: $TOOL referenced in evolve skill but not found"
fi
done
Online Mode (with API key)
Run the full pipeline with a mock agent:
1. Create temp directory with mock agent
TMPDIR=$(mktemp -d)
cat > "$TMPDIR/agent.py" << 'PYEOF'
import json, sys
input_path = sys.argv[1] if len(sys.argv) > 1 else None
if input_path:
with open(input_path) as f:
data = json.load(f)
question = data.get("input", data.get("question", ""))
print(json.dumps({"output": f"Mock answer to: {question}"}))
else:
print(json.dumps({"output": "No input provided"}))
PYEOF
cat > "$TMPDIR/test_inputs.json" << 'JSONEOF'
[
{"input": "What is 2+2?"},
{"input": "Name a color"},
{"input": "What is Python?"}
]
JSONEOF
echo "Mock agent created at $TMPDIR"
2. Run setup
$EVOLVER_PY $TOOLS/setup.py \
--project-name "dry-run-test" \
--entry-point "python3 $TMPDIR/agent.py {input}" \
--framework "unknown" \
--goals "accuracy" \
--dataset-from-file "$TMPDIR/test_inputs.json" \
--output "$TMPDIR/.evolver.json"
3. Run eval
$EVOLVER_PY $TOOLS/run_eval.py \
--config "$TMPDIR/.evolver.json" \
--worktree-path "$TMPDIR" \
--experiment-prefix "dry-run-v001a"
4. Read results
$EVOLVER_PY $TOOLS/read_results.py \
--experiment "dry-run-v001a" \
--config "$TMPDIR/.evolver.json" \
--format markdown
5. Trace insights
$EVOLVER_PY $TOOLS/trace_insights.py \
--from-experiment "dry-run-v001a" \
--output "$TMPDIR/trace_insights.json"
6. Cleanup
rm -rf "$TMPDIR"
echo "Dry run complete. Temp files cleaned up."
Report
Dry Run Results ({MODE} mode):
Tool syntax: {N}/{N} passed
Argparse: {N}/{N} passed
Cross-refs: {N}/{N} passed
{If online: setup/eval/read/trace pipeline: PASS/FAIL}
GitHub 仓库
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