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run-deep-swe

Prix non confirmé★ 3,850 Stars GitHubRegistre mis à jour · 2 sept. 2026agent-skill

Vue d’ensemble

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.

Lire la documentation complète

Documentation source, pas des instructions pour ce site. Vérifiez les permissions avant d’exécuter des commandes.

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 --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:

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 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:

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`

Voir la source ↗

Métadonnées du fichier

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

Voir le texte 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` |

Examiner la source

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Licence: MIT
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Réviser avant installation: Éviter l’installation automatique

Licence: 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

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Commencer par une petite tâche

1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
2Demandez un plan à l’agent. Approuvez la configuration et les coûts avant un test isolé.
3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.

Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.

Source et conseils d’utilisation

Répertorié

Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.

Dépôt source: davidondrej/skills
Licence: MIT
Version: 1.0.0
Dernier push GitHub: 2 sept. 2026
Registre mis à jour: 2 sept. 2026
Chemin des instructions: skills/agent-orchestration/run-deep-swe/SKILL.md @ 9dc174b058f7

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

80/100

Solide

Confiance

64/100

Sandbox uniquement

Audit

78/100

Revue nécessaire

Ouvrir l’audit complet Voir le rapport d’évaluation

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: —
Résultats: —

Copier ne signifie pas installer. Les compteurs nécessitent un rapport de réussite et ne garantissent pas la qualité globale.

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Plus de détails

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      "install_command": "npx skills add davidondrej/skills --skill run-deep-swe",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "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"
  }
}

Pour le créateur

Source de la fiche

Indexé par Registry

Revendiable

Cette fiche a été indexée à partir de sources publiques et n’est pas marquée officielle tant qu’une revendication de mainteneur n’est pas approuvée.

Créateur: davidondrej
Source: davidondrej/skills
Indexé par: Index communautaire OpenAgentSkill

L’attribution renvoie au dépôt public ou au profil du créateur. Les créateurs peuvent revendiquer la fiche pour mettre à jour les signaux de propriété.

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Cette fiche Indexé par Registry est attribuée à davidondrej, mais n’est pas encore marquée officielle. Revendiquez-la pour ajouter un signal de propriétaire vérifié et rendre les futures mises à jour de lancement, d’installation et d’audit plus fiables.

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