Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add raphaelchristi/harness-evolver --skill dev-dry-rungit clone --depth 1 https://github.com/raphaelchristi/harness-evolverWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/raphaelchristi/harness-evolver/dev-dry-run)<a href="https://agentmods.dev/skills/raphaelchristi/harness-evolver/dev-dry-run"><img src="https://agentmods.dev/badge/skills/raphaelchristi/harness-evolver/dev-dry-run/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/raphaelchristi/harness-evolver/dev-dry-run"><img src="https://agentmods.dev/badge/skills/raphaelchristi/harness-evolver/dev-dry-run.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00049 | $0.01098 |
| Opus 5.5 | $0.00020 | $0.00439 |
| Sonnet 5.5 | $0.00010 | $0.00220 |
| Haiku 4.5 | $0.00005 | $0.00110 |
Grade C, and why
dev:dry-run scanned grade C with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
rm -rf "$TMPDIR" How it starts
The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/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"
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 144 lines · 49 tokens per session scan C b0e519fd1078
dev:dry-run is a skill published in the GitHub repository raphaelchristi/harness-evolver (57 stars, last pushed 6mo ago), licensed MIT. It adds 49 tokens to every session and 1,098 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
feature-verify
Feature verification (READ-ONLY, P0-P5). Use when: verifying feature behavior after deployment, validating API responses, diagnosing production issues, post-deploy smoke test. Not for: modifying data (use feature-dev), code review (use codex-review-fast), writing tests (use codex-test-gen), security audit (use…
post-dev-test
Post-development test completion. Use when: checking test coverage after feature-dev, writing missing integration/e2e tests. Not for: unit test generation (use codex-test-gen), test review (use test-review). Output: test files + coverage report.
verify
Verification loop — lint -> typecheck -> unit -> integration -> e2e.
check-coverage
Comprehensive assessment of Unit / Integration / E2E three-layer test coverage, identify gaps and provide actionable recommendations.
e2e
Add durable end-to-end tests for user/API-visible behavior. Detect or scaffold the E2E framework, write tests, run the app, and store evidence. Use for E2E, Playwright/Cypress, regression tests, or quality gates. Not exploratory QA.
verify-qa
A conditional end-to-end quality-assurance workflow for changes to a user interface. End-to-end testing checks a feature through the whole user path, often in a real browser, and this workflow also includes performance and accessibility checks.