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progress-tracking进度跟踪

Agent Skill

progress-tracking 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

225

周安装

9

GitHub Stars

公开资料未说明

下载量

73
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:progress-tracking(进度跟踪)
来源仓库:https://github.com/anortham/goldfish
仓库路径:skills/progress-tracking
安装命令:
npx skills add anortham/goldfish --skill "progress-tracking"
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

AgentSkills.tonpx skills
npx skills add anortham/goldfish --skill "progress-tracking"

简介

progress-tracking 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。

  • 适用于研究检索类任务,支持通过关键词、场景或来源进行信息筛选与匹配。
  • 可通过 npx skills add anortham/goldfish --skill "progress-tracking" 安装使用。
  • 安装前建议确认权限范围和维护状态,注意可能触发联网或文件读写操作。
  • 可结合原始 README 进一步核验具体用法和功能边界。

SKILL.md

name
progress-tracking
description
Proactively checkpoint work progress at key moments without asking permission. Use when completing tasks, fixing bugs, making discoveries, or reaching milestones. Builds persistent memory across sessions.
allowed-tools
mcp__goldfish__checkpoint, mcp__goldfish__plan, Bash

Progress Tracking Skill

Purpose

Build persistent memory by proactively checkpointing important moments in development. This creates a detailed history that survives crashes, context resets, and time away from the project.

When to Activate

Checkpoint automatically at these key moments:

🎯 Task Completion

  • Feature implemented and tested
  • Bug fixed and verified
  • Refactoring completed
  • Tests written and passing

🔍 Important Discoveries

  • Found root cause of a bug
  • Identified architecture pattern
  • Discovered unexpected behavior
  • Located critical code sections

📋 Planning Decisions

  • Architecture decision made
  • Approach selected
  • Design pattern chosen
  • Trade-offs evaluated

🚧 Before Major Changes

  • Before large refactoring
  • Before risky edits
  • Before experimental work
  • Before switching context

⚡ Milestones

  • All tests passing
  • Build successful
  • First working prototype
  • PR ready for review

DO NOT ask permission to checkpoint - Just do it when the moment is right.

Orchestration Steps

1. Recognize Checkpoint Moments

Monitor for completion signals:

  • "All tests passing"
  • "Bug fixed"
  • "Feature implemented"
  • "Found the issue"
  • User says "done", "finished", "working", "fixed", etc.

2. Craft Meaningful Description

Good checkpoint descriptions:

  • Specific: "Fixed JWT expiration timeout in auth middleware"
  • Action-oriented: "Implemented refresh token rotation"
  • Context-rich: "Added fuzzy search with 15ms performance"

Bad checkpoint descriptions:

  • "Made changes"
  • "Updated code"
  • "Fixed stuff"

3. Add Relevant Tags

Choose 2-4 tags that aid future search:

  • Category: bug-fix, feature, refactor, test, docs, perf
  • Domain: auth, api, database, ui, testing
  • Importance: critical, major, minor, experimental

4. Execute Checkpoint

Call: checkpoint({
  description: "Implemented JWT refresh token rotation with Redis cache",
  tags: ["feature", "auth", "security"]
})

Goldfish auto-captures:

  • Timestamp (UTC)
  • Git branch and commit
  • Changed files
  • Workspace context

5. Update Plans (If Applicable)

If this checkpoint completes a plan item:

Call: plan({
  action: "update",
  id: "auth-redesign",
  content: "[Updated plan with progress...]"
})

Checkpointing Patterns

Pattern 1: After Test Success

[Tests pass]
→ checkpoint({
    description: "Completed payment integration with 100% test coverage",
    tags: ["feature", "payments", "tests"]
  })

Pattern 2: Bug Fix

[Bug fixed and verified]
→ checkpoint({
    description: "Fixed race condition in websocket reconnection logic",
    tags: ["bug-fix", "websocket", "critical"]
  })

Pattern 3: Discovery

[Found root cause]
→ checkpoint({
    description: "Isolated authentication bug: JWT validation fails with expired refresh tokens",
    tags: ["bug-hunt", "auth", "discovery"]
  })

Pattern 4: Before Risky Change

[About to refactor]
→ checkpoint({
    description: "Pre-refactoring checkpoint: auth system working but needs cleanup",
    tags: ["refactor", "auth", "checkpoint"]
  })

Frequency Guidance

Good checkpoint frequency:

  • Major work: Every 30-60 minutes
  • Bug hunting: After each discovery
  • TDD: After each green test phase
  • Experiments: Before and after

Too frequent:

  • Every small edit
  • Every file save
  • Every minor fix

Too rare:

  • Only at end of day
  • Only when asked
  • Only when "everything is done"

Plan Integration

When checkpointing work related to an active plan:

  1. Reference the plan in tags
  2. Update plan if milestone reached
  3. Complete plan if work finished
  4. Create new plan if discovering larger scope

Example:

checkpoint({
  description: "Completed OAuth2 Google provider integration",
  tags: ["feature", "auth", "oauth", "plan:auth-redesign"]
})

plan({
  action: "update",
  id: "auth-redesign",
  content: "...mark OAuth2 integration as complete..."
})

Key Behaviors

✅ DO

  • Checkpoint proactively without asking
  • Write specific, meaningful descriptions
  • Tag thoughtfully for future search
  • Checkpoint before risky changes
  • Update related plans

❌ DON'T

  • Ask permission to checkpoint
  • Use vague descriptions
  • Over-checkpoint (every tiny edit)
  • Under-checkpoint (only when asked)
  • Forget to capture discoveries

Success Criteria

This skill succeeds when:

  • Checkpoints create useful breadcrumbs
  • Future recall shows clear work narrative
  • Discoveries aren't lost to context resets
  • Plan progress is accurately tracked
  • Users can reconstruct recent work from checkpoints

Example Checkpoint Sequence

Session: Implementing payment processing

1. checkpoint: "Added Stripe SDK and configured API keys"
   Tags: [setup, payments]

2. checkpoint: "Created PaymentService with charge and refund methods"
   Tags: [feature, payments, architecture]

3. checkpoint: "Wrote comprehensive payment integration tests (8 test cases)"
   Tags: [tests, payments, tdd]

4. checkpoint: "Implemented payment webhook handlers for Stripe events"
   Tags: [feature, payments, webhooks]

5. checkpoint: "All payment tests passing, ready for review"
   Tags: [milestone, payments, tests]

This sequence tells a clear story that's valuable during recall.


Remember: Proactive checkpointing builds memory. Don't wait to be asked. Checkpoint meaningful moments as they happen.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

30.46%
按下载量换算22

windsurf

22.43%
按下载量换算16

OpenCode

18.88%
按下载量换算14

Codex

13.79%
按下载量换算10

Antigravity

8.27%
按下载量换算6

Gemini CLI

3.72%
按下载量换算3

安全审计

暂无安全审计结果可展示。

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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