dev:dry-run — Claude Code skill: smoke-test the evolve pipeline
Runs the evolve pipeline as a smoke test: checks tool syntax and skill cross-references, and with a LangSmith key exercises the full loop on a mock agent.
npx skills add raphaelchristi/harness-evolver --skill dev-dry-run -a claude-codeWhat dev:dry-run does
Reach for dev:dry-run when you want proof that the harness-evolver plugin actually runs before spending real experiments on it. The skill picks its own mode: an online pass when LANGSMITH_API_KEY is set, an offline check when it is not.
Offline, it walks every *.py file in the tools directory, parses each one with ast, and calls it with --help to catch argparse mismatches. Then it verifies that each tool named in skills/evolve/SKILL.md exists on disk. Paths come from EVOLVER_TOOLS and EVOLVER_PY; otherwise it falls back to tools or $HOME/.evolver/tools and python3.
Online, the skill builds a temp directory holding a small Python mock agent and three test questions, then runs setup.py, run_eval.py, read_results.py and trace_insights.py in sequence before deleting the temp files. No LANGSMITH_API_KEY means no full loop — only the syntax pass.
What it helps with
- confirm evolve tools import and parse their arguments without errors
- spot tools the evolve skill references that are missing from the directory
- exercise the setup → eval → read → trace loop against a mock agent
- verify the plugin runs before launching real experiments
- remove temp files after a test run
Example prompts for the agent
- Do a dry run of the evolve pipeline
- Smoke-test the evolve tools
- Test the whole evolve pipeline with the LangSmith key set
How to install dev:dry-run
1Skills CLI (Vercel)
npx skills add raphaelchristi/harness-evolver --skill dev-dry-run -a claude-code2Manually: put the files in place
~/.claude/skills/dev-dry-run/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/dev-dry-run/
1Skills CLI (Vercel)
npx skills add raphaelchristi/harness-evolver --skill dev-dry-run -a codex2Manually: put the files in place
.agents/skills/dev-dry-run/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add raphaelchristi/harness-evolver --skill dev-dry-run -a cursor2Manually: put the files in place
.cursor/skills/dev-dry-run/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install raphaelchristi/harness-evolver dev-dry-run2Manually: put the files in place
.github/skills/dev-dry-run/SKILL.md1Skills CLI (Vercel)
npx skills add raphaelchristi/harness-evolver --skill dev-dry-run -a gemini-cli2Platform CLI
gemini skills install https://github.com/raphaelchristi/harness-evolver --path .claude/skills/dev-dry-runManually: put the files in place
.devin/skills/dev-dry-run/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add raphaelchristi/harness-evolver --skill dev-dry-run -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about dev:dry-run
How do I install dev:dry-run in Claude Code?
Run npx skills add raphaelchristi/harness-evolver --skill dev-dry-run -a claude-code. The skill lands at ~/.claude/skills/dev-dry-run/SKILL.md.
What does dev:dry-run do?
It smoke-tests the evolve pipeline in two modes: offline it checks tool syntax, argparse flags and cross-references in the evolve skill; online it runs the full loop against a mock agent using temp files.
What do I need for online mode?
A LANGSMITH_API_KEY environment variable. Without it the skill falls back to the offline check and skips the full loop.
Source text
SKILL.md · MIT
---
name: dev:dry-run
description: "Use when the user wants to smoke-test the evolve pipeline, test tools, or verify the plugin works end-to-end. Also use when the user says 'dry run', 'smoke test', or 'test pipeline'."
allowed-tools: [Read, Bash, Glob, Grep]
---
# /dev:dry-run
Smoke-test the evolve pipeline. Two modes depending on whether LANGSMITH_API_KEY is available.
## Resolve Paths
```bash
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?
```bash
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:
```bash
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: $TOOL referenced in evolve skill but not found"
fi
done
```
## Online Mode (with API key)
Run the full pipeline with a mock agent:
### 1. Create temp directory with mock agent
```bash
TMPDIR=$(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
```bash
$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
```bash
$EVOLVER_PY $TOOLS/run_eval.py \
--config "$TMPDIR/.evolver.json" \
--worktree-path "$TMPDIR" \
--experiment-prefix "dry-run-v001a"
```
### 4. Read results
```bash
$EVOLVER_PY $TOOLS/read_results.py \
--experiment "dry-run-v001a" \
--config "$TMPDIR/.evolver.json" \
--format markdown
```
### 5. Trace insights
```bash
$EVOLVER_PY $TOOLS/trace_insights.py \
--from-experiment "dry-run-v001a" \
--output "$TMPDIR/trace_insights.json"
```
### 6. Cleanup
```bash
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}
```
© raphaelchristi · MIT · raphaelchristi/harness-evolver