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 agentmods add skills/cliffren/swf/expnpx skills add cliffren/swf --skill expgit clone --depth 1 https://github.com/cliffren/swfWhat 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 | $0.00020 | $0.01420 |
| Opus 5 | $0.00010 | $0.00710 |
| Sonnet 5 | $0.00004 | $0.00284 |
| Haiku 4.5 | $0.00002 | $0.00142 |
Grade A, and why
exp 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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Management
Standardized experiment workflow: design → run → record → compare → collect for paper.
Sub-commands
$ARGUMENTS[0] determines the action. If omitted, show help.
/swf:exp new <title>
Create a new experiment record from template.
-
Ask experiment type:
benchmark— Phase 2 标准化对比实验(和 baseline 公平对比)case— Phase 3 Case Study / Feature Demo(深入分析,讲故事)
-
Auto-number: scan the corresponding subdirectory for next number
- Benchmark →
docs/experiments/benchmark/bench-<NNN>-<slug>.md - Case →
docs/experiments/case/case-<NNN>-<slug>.md
- Benchmark →
-
Ask type-specific questions:
Benchmark:
- 和哪些 baseline 比较?(方法名、来源、版本)
- 用哪些数据集?
- 评估哪些指标?
- 公平性控制:相同数据划分?重复次数?超参调优方式?
Case Study:
- 为什么选这个 case?生物学背景?
- 这个 case 要展示什么能力?
- 数据来源(GEO ID 等)?
- 论文里这个 case 想讲什么故事?
-
Generate record from corresponding template in
${CLAUDE_SKILL_DIR}/../reference/experiment-template.md -
Auto-capture environment:
python --version pip list | grep -E "torch|scanpy|numpy|scipy|pandas" hostname nvidia-smi --query-gpu=name,memory.total --format=csv,noheader 2>/dev/null -
Generate config file if applicable →
configs/<prefix>-<NNN>.yaml -
Generate run command and write into record
-
Commit the record and config
-
Create results directory:
mkdir -p results/<prefix>-<NNN> -
Remind: "实验记录已创建。运行实验后,用
/swf:exp log <prefix>-<NNN>记录结果。"
/swf:exp log <NNN>
Record results for a completed experiment.
- Read the experiment record
docs/experiments/exp-<NNN>-*.md - Collect results:
- Check
results/exp-<NNN>/for output files - If metrics file exists (
.json,.csv), auto-extract key metrics - If figures exist, list them with paths
- If log file exists, extract final metrics (loss, accuracy, etc.)
- Check
- Ask the user:
- 结果是否符合预期?
- 关键发现是什么?
- 有什么后续需要做?
- Update the experiment record:
- Fill in results table with metrics
- Fill in figure references
- Fill in conclusion
- Update status to Completed (or Failed)
- Record the current git commit hash
- Commit the updated record
- Prompt next steps:
- "要和其他实验比较吗?(
/swf:exp compare)" - "要更新 design.md 吗?(
/swf:update-design)" - If results invalidate assumptions: "实验结果和 design.md 假设不一致,建议写 ADR 记录 (
/swf:adr)"
- "要和其他实验比较吗?(
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.
- 2d ago First seen · 150 lines · 20 tokens per session scan A 495c1ee5080f
exp is a skill published in the GitHub repository cliffren/swf (5 stars, last pushed 3mo ago), licensed MIT. It adds 20 tokens to every session and 1,420 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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