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 attentiondotnet/davidondrej-skills --skill run-deep-swegit clone --depth 1 https://github.com/attentiondotnet/davidondrej-skillsWrote 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/attentiondotnet/davidondrej-skills/run-deep-swe)<a href="https://agentmods.dev/skills/attentiondotnet/davidondrej-skills/run-deep-swe"><img src="https://agentmods.dev/badge/skills/attentiondotnet/davidondrej-skills/run-deep-swe/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/attentiondotnet/davidondrej-skills/run-deep-swe"><img src="https://agentmods.dev/badge/skills/attentiondotnet/davidondrej-skills/run-deep-swe.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 | $0.00111 | $0.01166 |
| Opus 5 | $0.00056 | $0.00583 |
| Sonnet 5 | $0.00022 | $0.00233 |
| Haiku 4.5 | $0.00011 | $0.00117 |
Grade A, and why
run-deep-swe scanned grade A with 0 findings 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 11d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
92% identical to run-deep-swe — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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).
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:
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.
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.
- 11d ago First seen · 104 lines · 111 tokens per session scan A eca74c85bbab
run-deep-swe is a skill published in the GitHub repository attentiondotnet/davidondrej-skills (11 stars, last pushed 2mo ago), licensed MIT. It adds 111 tokens to every session and 1,166 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to run-deep-swe, differing in 3 lines, and is treated as a copy.
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