workflow-tracker

An automatic log for machine-learning and AI experiments. It detects training, parameter tuning, accuracy changes, data analysis, and experiment conclusions, then records them in project files.

In plain words
What is it for?
Recording model training, tuning, metric comparisons, parameter changes, script results, and experiment conclusions in engineering or paper projects.
Why use it?
It preserves what was tried and what happened without requiring the user to remember to update a separate log.

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/markd1zzz/workflow-tracker/workflow-tracker
Any agent
npx skills add MarkD1Zzz/workflow-tracker --skill workflow-tracker
Clone the repo
git clone --depth 1 https://github.com/MarkD1Zzz/workflow-tracker

Made for: Claude Code, Codex.

Per session 100 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,793 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 $0.00100 $0.01793
Opus 5 $0.00050 $0.00897
Sonnet 5 $0.00020 $0.00359
Haiku 4.5 $0.00010 $0.00179

Measured 2d ago against content hash 18753d7b7773, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

workflow-tracker 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.

SKILL.md · 178 lines

How it starts

The opening of the file, as written. The whole thing — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Workflow & Experiment Tracker

自动拆解为 Phase → Task → Experiment 三级结构。识别项目类型后输出对应格式。

触发

检测到以下任一信号即静默记录,不打断用户

  • 看到数字变化("精度从X变到Y"、"loss降到Z"、"F1从A变到B")
  • 用户口述实验结论("试了X"/"不行"/"有效"/"放弃")
  • 运行脚本后看到结果输出
  • 参数变更("把A改成B"、"新增了C")
  • 用户说"记录"/"保存"/"更新日志"

不必等到用户说"记录"才行动。在回复中自然地追加一条记录即可。

项目类型识别

首次进入项目时,扫描目录+用户对话,判定类型:

信号 类型 存储
tex/manuscriptfigures/投稿论文ablation 论文 CHANGELOG.md + experiment_log.md
data/train/models/train.pypipelinemain.py 工程 workflow.json + workflow.md
用户说"部署"/"产品"/"上线"/"API" 工程 workflow.json + workflow.md
无法判断 默认工程 workflow.json + workflow.md

论文模式

直接追加两个 Markdown 文件,不建 JSON

CHANGELOG.md

按时间线累积。每条格式:

## 2026-06-14 — 简短标题

### 背景
(1-2句说明为什么做这个改动)

### 改动
| 参数 | 旧值 | 新值 | 原因 |
|------|------|------|------|

### 效果
(关键数字变化)

experiment_log.md

每次实验一条,可独立阅读:

## 2026-06-14 — 实验标题

- **假设**: ...
- **方法**: ...
- **参数**: ...
- **结果**: 基线X → 新Y (delta)
- **结论**: SUCCESS / FAILED / PARTIAL
- **备注**: ...

工程模式

维护 .claude/workflow/workflow.json + 自动导出 .claude/workflow/workflow.md

JSON 最小结构

{
  "project": "项目名",
  "updated": "2026-06-14T14:30",
  "phases": [{
    "name": "Phase 1: XXX",
    "status": "in_progress",
    "tasks": [{
      "name": "Task 1.1: XXX",
      "status": "completed",
      "experiments": [{
        "date": "2026-06-14",
        "title": "实验标题",
        "method": "一句话方法",
        "params": {"old_lr": 0.001, "new_lr": 0.0002},
        "results": {"baseline": 93.75, "new": 94.79, "delta": 1.04},
        "conclusion": "SUCCESS",
        "tags": ["classifier", "svm"]
      }]
    }]
  }]
}

字段说明:

  • title/method/results/conclusion 必填,其他可选
  • params 用 old_xxx / new_xxx 格式记录变更
  • results.delta 统一用 pp(百分点)或绝对值
  • tags 至少一个分类标签
  • conclusion 取值: SUCCESS | FAILED | PARTIAL | INCONCLUSIVE

workflow.md 自动导出

每次更新 JSON 后同步重写。格式:

Read the full file on GitHub · 178 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 · 178 lines · 100 tokens per session scan A 18753d7b7773

Subscribe to this mod's changes

workflow-tracker is a skill published in the GitHub repository MarkD1Zzz/workflow-tracker (10 stars, last pushed 2mo ago), licensed MIT. It adds 100 tokens to every session and 1,793 once invoked, about $0.0005 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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