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writing-plans写作计划

Agent Skill

用于辅助文档、README、Markdown、说明文和内容稿件的整理与改写。它适合让 Agent 提炼结构、补齐章节、统一术语、检查链接或把零散材料整理成可读文档。使用时应保留项目已有事实、命令和路径,不要把未确认的信息写成确定结论;涉及对外文案时,还需要控制语气,避免过度营销或夸大能力。

总安装

272

周安装

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GitHub Stars

5

下载量

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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

unknown

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:writing-plans(写作计划)
来源仓库:https://github.com/ahmedhamadto/software-forge
仓库路径:skills/writing-plans
安装命令:
npx skills add https://github.com/ahmedhamadto/software-forge --skill writing-plans
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。

skills.shnpx skills
npx skills add https://github.com/ahmedhamadto/software-forge --skill writing-plans

简介

用于编写详细实施计划,假设工程师对项目背景和技术栈几乎不了解。

  • 文档需包含文件清单、测试策略、依赖检查和验证方法,便于快速上手。
  • 采用 DRY 和 YAGNI 原则,避免过度设计,聚焦当前需求。
  • 计划应以小任务形式拆解,配合频繁提交和 TDD 实践降低风险。
  • writing-plans 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Writing Plans

Overview

Write comprehensive implementation plans assuming the engineer has zero context for our codebase and questionable taste. Document everything they need to know: which files to touch for each task, code, testing, docs they might need to check, how to test it. Give them the whole plan as bite-sized tasks. DRY. YAGNI. TDD. Frequent commits.

Assume they are a skilled developer, but know almost nothing about our toolset or problem domain. Assume they don't know good test design very well.

Announce at start: "I'm using the writing-plans skill to create the implementation plan."

Context: This should be run in a dedicated worktree (created by brainstorming skill).

Save plans to: docs/plans/YYYY-MM-DD-<feature-name>.md

Experience-Informed Planning (MANDATORY)

Before finalizing any plan, query past experience from neo4j-agent-memory to avoid repeating mistakes and leverage proven patterns.

Step 1: Extract concepts. Identify 3-5 key technical concepts from the plan (technologies, patterns, architectural choices).

Step 2: Query the knowledge graph. For each concept, run:

memory_search(query="[concept]", memory_types=["entities"])

Then run a scoped Cypher query for cross-project principles:

graph_query: MATCH (p:Entity)
  WHERE p.type = 'OBJECT' AND p.subtype IN ['PATTERN', 'GOTCHA']
    AND p.metadata IS NOT NULL
  WITH p, apoc.convert.fromJsonMap(p.metadata) AS meta
  WHERE any(tag IN meta.stack_tags WHERE tag IN $plan_stack_tags)
    AND meta.confidence IN ['validated', 'derived']
    AND (meta.scope = 'cross-project' OR meta.scope = $current_project_scope)
  RETURN p.name, p.description, meta.confidence, meta.source_lineage
  ORDER BY CASE meta.confidence WHEN 'validated' THEN 0 ELSE 1 END
  LIMIT 10

Replace $plan_stack_tags with the plan's tech stack (e.g., ['python', 'postgresql', 'fastapi']) and $current_project_scope with 'project:[name]'.

Step 3: Incorporate. If any results contradict or inform the plan, add constraints or warnings to the relevant tasks. Note which principles were applied in the plan header under **Experience Applied:**.

Skip if neo4j-agent-memory is unavailable. Do not block plan generation on graph queries.

Bite-Sized Task Granularity

Each step is one action (2-5 minutes):

  • "Write the failing test" - step
  • "Run it to make sure it fails" - step
  • "Implement the minimal code to make the test pass" - step
  • "Run the tests and make sure they pass" - step
  • "Commit" - step

Plan Document Header

Every plan MUST start with this header:

# [Feature Name] Implementation Plan

> **For Claude:** REQUIRED SUB-SKILL: Use software-forge:executing-plans to implement this plan task-by-task.

**Goal:** [One sentence describing what this builds]

**Architecture:** [2-3 sentences about approach]

**Tech Stack:** [Key technologies/libraries]

---

Task Structure

### Task N: [Component Name]

**Files:**
- Create: `exact/path/to/file.py`
- Modify: `exact/path/to/existing.py:123-145`
- Test: `tests/exact/path/to/test.py`

**Step 1: Write the failing test**

def test_specific_behavior(): result = function(input) assert result == expected


**Step 2: Run test to verify it fails**

Run: `pytest tests/path/test.py::test_name -v`
Expected: FAIL with "function not defined"

**Step 3: Write minimal implementation**

def function(input): return expected


**Step 4: Run test to verify it passes**

Run: `pytest tests/path/test.py::test_name -v`
Expected: PASS

**Step 5: Commit**

git add tests/path/test.py src/path/file.py git commit -m "feat: add specific feature"

Remember

  • Exact file paths always
  • Complete code in plan (not "add validation")
  • Exact commands with expected output
  • Reference relevant skills with @ syntax
  • DRY, YAGNI, TDD, frequent commits

Output Protocol (MANDATORY)

After generating the full plan:

  1. Write the complete plan to docs/plans/YYYY-MM-DD-<topic>-plan.md
  2. Display ONLY a summary table to the conversation:
#TaskFilesTestsEst. Complexity
1[name][count][count]S/M/L
  1. End with: "Full plan saved to docs/plans/[filename]. [N] tasks, [M] files, [T] tests."

Do NOT output the full plan to the conversation. The plan lives on disk.

Execution Handoff

After saving the plan, offer execution choice:

"Plan complete and saved to docs/plans/<filename>.md. Two execution options:

1. Subagent-Driven (this session) - I dispatch fresh subagent per task, review between tasks, fast iteration

2. Parallel Session (separate) - Open new session with executing-plans, batch execution with checkpoints

Which approach?"

If Subagent-Driven chosen:

  • REQUIRED SUB-SKILL: Use software-forge:subagent-driven-development
  • Stay in this session
  • Fresh subagent per task + code review

If Parallel Session chosen:

  • Guide them to open new session in worktree
  • REQUIRED SUB-SKILL: New session uses software-forge:executing-plans

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

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

展示第三方安全扫描或审计结果

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

Codex

35.14%
按下载量换算30

Claude

26.66%
按下载量换算23

Cursor

18.44%
按下载量换算16

Gemini CLI

9.78%
按下载量换算8

安全审计

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通过

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通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。

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