run-deep-swe
Score any AI model on the DeepSWE coding-agent benchmark via the OpenRouter API.
Skill by attentiondotnet
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
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About
Use cases: backend, testing, productivity, agent-building
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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
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codeload.github.com· found in install command
Files it touches
~/.claude/skills/run-deep-swe
What's inside
- Skill name
run-deep-swe- Description
- Score any AI model on the DeepSWE coding-agent benchmark via the OpenRouter API. Use when the user wants an independent, reproducible coding-agent eval — "run DeepSWE", "benchmark this model on DeepSWE", "score model X on the coding benchmark", "test a model via OpenRouter on DeepSWE", or to verify vendor-reported coding scores. Covers setup, the OpenRouter wiring for mini-swe-agent, single-task / subset / full 113-task runs, and leaderboard submission.
- License
- MIT
- Instructions
- 99 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Run DeepSWE via OpenRouter
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).
The user has a dedicated OpenRouter key for this benchmark exported globally in `~/.zshrc`. A fresh shell already has `OPENROUTER_API_KEY` available. If it's somehow not set, re-source the shell:
```bash
source ~/.zshrc && echo "key loaded? ${OPENROUTER_API_KEY:+YES}"
```
If still unset, ask the user — never 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.com/datacurve-ai/pier
# pier bundles mini-swe-agent as the --agent driver
```
Run all `pier` commands from inside `deep-swe/`, using relative `-p tasks/...`.
## OpenRouter wiring (the part the docs don't spell out)
mini-swe-agent has a native OpenRouter model class. Both routes below use `OPENROUTER_API_KEY` and the OpenRouter slug (`vendor/model`, e.g. `minimax/minimax-m3`):
**Route A — native OpenRouter class (preferred, hits openrouter.ai/api/v1 directly):**
```bash
pier run -p deep-swe/tasks --agent mini-swe-agent \
--model minimax/minimax-m3 --model-class openrouter
```
**Route B — LiteLLM provider prefix (fallback; same key):**
```bash
pier run -p deep-swe/tasks --agent mini-swe-agent \
--model openrouter/minimax/minimax-m3
```
Notes:
- Slug = the exact OpenRouter slug. Verify it at openrouter.ai/models before running.
- Free/zero-cost models: OpenRouter cost tracking can error. Set `export MSWEA_COST_TRACKING=ignore_errors`.
- Flag spelling can vary by version — confirm with `pier run --help` and `mini --help`.
## Smoke test FIRST (1 task — do this before any full run)
Always validate end-to-end wiring on a single task before spending tokens on the corpus:
```bash
pier run -p deep-swe/tasks/<task-id> --agent mini-swe-agent \
--model minimax/minimax-m3 --model-class openrouter
# list available task ids:
ls deep-swe/tasks
```
Pass criteria: run completes, model returns actions (not auth/format errors), a score/trajectory is emitted. If it 401s → key wrong. If "provider not provided"/"model not mapped" → fix slug or switch route.
## Subset run (deterministic sample)
```bash
pier run -p deep-swe/tasks --agent mini-swe-agent \
--model minimax/minimax-m3 --model-class openrouter \
--n-tasks 10 --sample-seed 0
```
## Full 113-task corpus (costs tokens + time — confirm with user first)
```bash
pier run -p deep-swe/tasks --agent mini-swe-agent \
--model minimax/minimax-m3 --model-class openrouter
# add `--env modal` to run in parallel Modal sandboxes (needs Modal configured)
```
## Output & leaderboard
- Trials land in `jobs/<run>/<trial_id>/`. Inspect with `pier view jobs/<run>`, `pier analyze jobs/<run>`, or `pier critique run jobs/<run>`.
- Report: the exact command used, pass/fail, score, and any blockers.
- Submit results for the official leaderboard to: **<email-address>**
## Failure modes
| Symptom | Cause | Fix |
|---|---|---|
| HTTP 401 | bad/missing key | re-export `OPENROUTER_API_KEY` |
| "LLM Provider NOT provided" | missing slug prefix | use Route B `openrouter/...` or Route A with `--model-class openrouter` |
| "model isn't mapped"/cost error | unknown cost for model | `export MSWEA_COST_TRACKING=ignore_errors` |
| unknown flag | version drift | check `pier run --help` |From attentiondotnet/davidondrej-skills, MIT.
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Signals
- Stars
- 11
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Jul 7, 2026 (3 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
run-deep-swe skill. Agent Mart. https://agentmart.dev/skills/attentiondotnet-run-deep-swe (last updated 2026-10-24)