run-deep-swe
Run reproducible DeepSWE coding-agent benchmark evaluations through OpenRouter and mini-swe-agent.
Profile →npx skills add sickn33/agentic-awesome-skills --skill run-deep-swe --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Run DeepSWE via OpenRouter ## When to Use - Use when the user wants to benchmark a model on DeepSWE or mini-swe-agent tasks. - Use when you need a reproducible coding-agent evaluation plan and output artifacts. DeepSWE (deepswe.datacurve.ai) is a 113-task Harbor-compatible coding-agent benchmark. It runs via **Pier** (Harbor fork) driving **mini-swe-agent** (model-agnostic). Any model reachable through OpenRouter can be scored. ## Prerequisites — state-check first ```bash which uv git docker || echo "MISSING: install uv, git, docker" docker info >/dev/null 2>&1 || echo "MISSING: Docker daemon not running (Pier's default sandbox)" echo "OPENROUTER_API_KEY set? ${OPENROUTER_API_KEY:+YES}" ``` **Docker must be running** — Pier sandboxes each task in Docker by default (`--env modal` for cloud instead). `OPENROUTER_API_KEY` must already be present in the environment. If it is unset, ask the user to configure their preferred secret-management path; do not read shell startup files, print secrets, or invent a key. ## Setup ```bash git clone https://github.com/datacurve-ai/deep-swe && cd deep-swe uv tool install datacurve-pier # PyPI (preferred) # or: uv tool install git+https://github.co
- When to Use
- Prerequisites — state-check first
- Setup
- OpenRouter wiring (the part the docs don't spell out)
- Smoke test FIRST (1 task — do this before any full run)
- Subset run (deterministic sample)
- Full 113-task corpus (costs tokens + time — confirm with user first)
- Output & leaderboard
- Failure modes
- Limitations
which uv git docker || echo "MISSING: install uv, git, docker"
docker info >/dev/null 2>&1 || echo "MISSING: Docker daemon not running (Pier's default sandbox)"
echo "OPENROUTER_API_KEY set? ${OPENROUTER_API_KEY:+YES}"
git clone https://github.com/datacurve-ai/deep-swe && cd deep-swe
uv tool install datacurve-pier # PyPI (preferred)
pier bundles mini-swe-agent as the --agent driver
pier run -p deep-swe/tasks --agent mini-swe-agent \
pier run -p deep-swe/tasks/<task-id> --agent mini-swe-agent \
list available task ids:
ls deep-swe/tasksWhat does the run-deep-swe skill do?
Run reproducible DeepSWE coding-agent benchmark evaluations through OpenRouter and mini-swe-agent.
How do I install it?
Run `npx skills add sickn33/agentic-awesome-skills --skill run-deep-swe --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From sickn33/agentic-awesome-skills, a repository with 44,414 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.