davidondrej
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run-deep-swe
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 Dee
Resumen
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.
Leer documentación completa
Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.
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
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).
A dedicated OpenRouter key for this benchmark should be exported globally in the shell profile (weekly hard spend limit set as a safeguard). A fresh shell already has OPENROUTER_API_KEY available. If it's somehow not set, re-source the shell:
source ~/.zshrc && echo "key loaded? ${OPENROUTER_API_KEY:+YES}"
If still unset, ask the user — never invent a key.
Setup
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):
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):
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 --helpandmini --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:
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)
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)
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 withpier view jobs/<run>,pier analyze jobs/<run>, orpier critique run jobs/<run>. - Report: the exact command used, pass/fail, score, and any blockers.
- Submit results for the official leaderboard to:
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 |
Metadatos del archivo
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. disable-model-invocation: true
Ver texto original
---
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.
disable-model-invocation: true
---
# 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).
A dedicated OpenRouter key for this benchmark should be exported globally in the shell profile (weekly hard spend limit set as a safeguard). 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` |
Revisar el código fuente
Precio y costes de ejecución
- Obtener el skill
- Precio sin confirmar
- Ejecutarlo
- Requisitos sin confirmar. Consulta los costes del agente, API y servicios en la fuente.
- Licencia
- MIT
- Precio sin confirmar
- No hemos confirmado el precio. Los enlaces existentes al código y a la instalación siguen disponibles.
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Fuente del skill registrada
La ruta de instrucciones está registrada. No implica pruebas de ejecución, seguridad ni compatibilidad.
Revisar antes de instalar: Evitar instalación automática
Licencia: MIT
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- The provided SKILL.md excerpt is truncated in the review text, but the full file appears complete based on the visible content.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
Las herramientas son indicios de metadatos, no compatibilidad probada. Los prompts son sugerencias.
Empieza con una tarea pequeña
- 1Lee la fuente y confirma entradas, resultados, dependencias y permisos.
- 2Pide un plan al agente. Aprueba la configuración y los costes antes de probar en un entorno aislado.
- 3Comprueba resultados y archivos modificados. Informa solo de lo ejecutado y conserva la revisión de la fuente.
Consulta dependencias, claves API y costes externos en la fuente. Un repositorio público no implica servicios gratuitos.
Fuente y notas de uso
Indexado
Los metadatos y revisiones son orientativos. Popularidad, descubrimiento y ejecución correcta son hechos distintos.
- Repositorio fuente
- davidondrej/skills
- Licencia
- MIT
- Versión
- 1.0.0
- Último push de GitHub
- 2 sept 2026
- Registro actualizado
- 2 sept 2026
- Ruta de instrucciones
- skills/agent-orchestration/run-deep-swe/SKILL.md @ 9dc174b058f7
Versión declarada en el registro; consulta las versiones de la fuente.
Calidad
80/100
Sólido
Confianza
64/100
Solo sandbox
Auditoría
78/100
Requiere revisión
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- The provided SKILL.md excerpt is truncated in the review text, but the full file appears complete based on the visible content.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Verified installs
- —
- Resultados
- —
Copiar no es instalar. Los recuentos requieren un informe de instalación correcta, no garantizan calidad general.
Acceso para agentes
La API Registry expone señales de decisión, confianza, auditoría, casos de uso e instalación sin raspar la interfaz.
Más detalles
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],
"payload_template": {
"event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
"skill_slug": "davidondrej-run-deep-swe",
"task": "Use run-deep-swe in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/davidondrej-run-deep-swe",
"api": "https://www.openagentskill.com/api/agent/skills/davidondrej-run-deep-swe",
"audit": "https://www.openagentskill.com/skills/davidondrej-run-deep-swe/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=davidondrej-run-deep-swe&task=Use%20run-deep-swe%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20run-deep-swe%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20run-deep-swe%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/davidondrej-run-deep-swe/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/davidondrej-run-deep-swe"
}
}Para el creador
Fuente de la ficha
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Esta ficha se indexó desde fuentes públicas y no está marcada como oficial hasta que se apruebe una reclamación de mantenedor.
- Creador
- davidondrej
- Fuente
- davidondrej/skills
- Indexado por
- Índice comunitario de OpenAgentSkill
La atribución enlaza al repositorio público o al perfil del creador. Los creadores pueden reclamar la ficha para actualizar las señales de propiedad.
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[](https://www.openagentskill.com/skills/davidondrej-run-deep-swe?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/davidondrej-run-deep-swe/audit)
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