exp

An experiment-tracking workflow for recording research experiments, their results, comparisons, and data needed for a paper. It supports benchmark experiments, which compare methods fairly, and case studies, which examine one example in depth.

In plain words
What is it for?
Use it to create numbered experiment documents, record benchmark or case-study details, capture the computing environment, compare methods, and collect findings for paper writing.
Why use it?
It keeps experiment plans, settings, results, and comparisons in a consistent record instead of scattered notes.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/cliffren/swf/exp
Any agent
npx skills add cliffren/swf --skill exp
Clone the repo
git clone --depth 1 https://github.com/cliffren/swf

Made for: Claude Code, Codex.

Per session 20 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,420 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 2d ago against content hash 495c1ee5080f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

skills/exp/SKILL.md · 150 lines

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.

  1. 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)
  2. 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
  3. 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?
  4. Generate record from corresponding template in ${CLAUDE_SKILL_DIR}/../reference/experiment-template.md

  5. 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
    
  6. Generate config file if applicable → configs/<prefix>-<NNN>.yaml

  7. Generate run command and write into record

  8. Commit the record and config

  9. Create results directory: mkdir -p results/<prefix>-<NNN>

  10. 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.

  1. Read the experiment record docs/experiments/exp-<NNN>-*.md
  2. 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.)
  3. 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?
  4. 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
  5. Commit the updated record
  6. 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)"

Read the full file on GitHub · 150 lines

Changes

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.

  1. 2d ago First seen · 150 lines · 20 tokens per session scan A 495c1ee5080f

Subscribe to this mod's changes

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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