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Varnan-Tech/opendirectory

告诉技能你的产品发布了什么。它会为 `docs/changelog.md` 撰写一条带日期的更新日志条目,并同时生成一个可直接使用的内容包:推文、领英帖子、邮件摘要和一句话简介。

opendirectory 是什么?

该技能专为AI Agent(如Claude Code、Cursor、Codex)设计,用于自动化和产品发布记录与多渠道内容生成过程。它接受用户对产品更新、Git提交或GitHub PR的描述,将技术变更转化为用户友好的日志条目和营销内容包。技能的核心功能包括从多种来源收集信息、过滤噪音、生成结构化日志条目,并严格遵守内容规则,确保语言自然、有吸引力且无陈词滥调。它生成四个可分发的内容形式:一句话摘要、不超过5条推文的Twitter Thread、纯文本的LinkedIn帖子以及简洁的邮件片段。技能内置了自检QA机制,会验证生成内容中是否包含禁止词汇、推文长度、以及领英帖子的格式合规性。该工具省去了产品发布笔记和公告的人工起草工作,特别适用于API更新和日常功能迭代。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/Varnan-Tech/opendirectory/tree/main/skills/product-update-logger

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打开一个已预加载此 Agent Skill 的新对话。

文档

产品更新日志

告诉此技能你的产品发布了什么。它会为 docs/changelog.md(一个持续记录、最新条目在最前的日志文件)撰写一条精炼的变更日志条目,并同时生成一个内容包:推文、领英帖子、邮件摘要和一句话简介。

输入来源:来自你消息的任意文本、本地仓库的Git提交自动读取,或提供仓库时的GitHub PR。支持任意组合。

参考文件

每次运行前请阅读以下文件:

cat references/changelog-format.md
cat references/content-rules.md
cat references/noise-filter.md

步骤 1:环境检查

echo "GITHUB_TOKEN: ${GITHUB_TOKEN:-未设置 -- GitHub PR 获取已禁用}"
echo "Git:          $(git rev-parse --is-inside-work-tree 2>/dev/null && echo '检测到仓库' || echo '不是 Git 仓库')"
echo "变更日志:    $(ls docs/changelog.md 2>/dev/null && echo '已存在' || echo '将创建')"

注意 Git 是否可用以及变更日志是否已存在。这决定了版本标签的格式。


步骤 2:解析输入

从对话中收集:

  • items -- 发布内容的自由文本描述(多个项用管道符分隔)。如果 Git 可用,则为可选。
  • since -- 回溯的天数。默认值:7。用户可以说“过去两周”(14)或“自上次发布以来”。
  • repo -- 用于获取 PR 的 GitHub “所有者/仓库”。可选。
  • version_label -- 自定义标签,如“v2.1.0”或“速度更新”。可选;默认为基于日期。

如果用户未提及任何项目且没有 Git 仓库: 询问“你发布了什么?列出功能、修复或改进——每行一个。”

如果 Git 可用且用户未指定具体内容: 继续执行步骤 3 的 Git 自动读取。向用户展示发现的内容,并在转换前请求确认。

写入解析后的输入:

python3 << 'PYEOF'
import json, os, re

inp = {
    "items": "",          # 填写:管道符分隔的自由文本,若无则留空
    "since": 7,           # 填写:整数天数
    "repo": "",           # 填写:“所有者/仓库” 或 “”
    "version_label": ""   # 填写:“” 表示自动(基于日期),或自定义字符串
}

with open("/tmp/pul-input.json", "w") as f:
    json.dump(inp, f, indent=2)
print(f"回溯天数: {inp['since']} 天")
print(f"自由文本项目: {inp['items'] or '无(将使用 Git/GitHub)'}")
print(f"GitHub 仓库: {inp['repo'] or '无'}")
print(f"版本标签: {inp['version_label'] or '自动(基于日期)'}")
PYEOF

步骤 3:运行收集脚本

ls scripts/gather.py 2>/dev/null && echo "脚本已找到" || echo "错误:scripts/gather.py 未找到"
GITHUB_TOKEN="${GITHUB_TOKEN:-}" python3 scripts/gather.py \
    --since "$(python3 -c "import json; print(json.load(open('/tmp/pul-input.json'))['since'])")" \
    --repo "$(python3 -c "import json; print(json.load(open('/tmp/pul-input.json'))['repo'])")" \
    --items "$(python3 -c "import json; print(json.load(open('/tmp/pul-input.json'))['items'])")" \
    --output /tmp/pul-raw.json

验证输出:

python3 -c "
import json
with open('/tmp/pul-raw.json') as f:
    d = json.load(f)
print(f'找到的项目数量:      {d[\"total_items\"]}')
print(f'过滤的噪声数量:   {d[\"noise_filtered\"]}')
print(f'Git 可用:    {d[\"git_available\"]}')
print(f'GitHub 可用: {d[\"github_available\"]}')
print(f'来源: git={sum(1 for i in d[\"items\"] if i[\"source\"]==\"git_commit\")}, '
      f'prs={sum(1 for i in d[\"items\"] if i[\"source\"]==\"github_pr\")}, '
      f'text={sum(1 for i in d[\"items\"] if i[\"source\"]==\"free_text\")}')
print()
print('项目:')
for item in d['items']:
    print(f'  [{item[\"source\"]}] {item[\"subject\"]}')
"

如果 total_items == 0: 停止。告知用户:“未找到已发布的项目。请描述你发布了什么,指向一个包含最近提交的 Git 仓库,或添加一个 GitHub 仓库(repo: owner/repo)和 GITHUB_TOKEN。”

向用户展示项目列表并询问:“这些是我找到的项目。在我写入变更日志之前,需要添加或删除什么吗?”

等待用户确认或编辑。如果用户说“看起来不错”、“继续”,或未做任何更改,则继续执行。如果用户添加或删除了项目,则在步骤 4 之前相应更新 /tmp/pul-raw.json。


步骤 4:生成变更日志条目

打印项目以供参考:

python3 -c "
import json
with open('/tmp/pul-raw.json') as f:
    d = json.load(f)
print(json.dumps(d['items'], indent=2))
print()
print(f'现有变更日志格式: {d[\"existing_changelog\"][\"format\"]}')
print(f'最后一个标签: {d[\"existing_changelog\"][\"last_label\"]}')
print(f'今天日期: {d[\"date\"]}')
"

AI 指令: 将每个原始项目从技术语言转换为面向用户的收益语言。遵循 references/changelog-format.md 中的转换规则和示例。

规则:

  • 不要编造成果或指标。 “快 40%” 必须来自源数据。如果提交或 PR 中没有数字,就不要添加。
  • 使用过去时: “添加了”、“修复了”、“改进了”——而不是“添加”、“修复”
  • 为每个项目分配恰好一个类别: 新功能、改进、修复、或底层优化
  • 底层优化: 仅在与开发者相关时包含(API 更改、破坏性更改)。省略空段落。
  • 省略 任何映射到:测试更改、CI 更改、仅文档提交的内容

确定版本标签:

  • 如果用户指定了:直接使用
  • 如果 existing_changelog.format == "semver":基于更改递增(仅修复为 patch,任何新功能为 minor)
  • 默认:使用今天的日期,格式为 Week of [Month Day, Year]

将条目写入 /tmp/pul-entry.json:

{
  "label": "2026年4月23日周",
  "date": "2026-04-23",
  "new": [
    {"title": "深色模式", "description": "在设置 > 外观中切换。适用于所有视图。"}
  ],
  "improved": [
    {"title": "API 响应时间", "description": "平均快 40%。仪表板现在可在 1 秒内加载。"}
  ],
  "fixed": [
    {"title": "CSV 导出", "description": "导出不再丢失最后一行。"}
  ],
  "under_the_hood": []
}

验证条目:

python3 -c "
import json
with open('/tmp/pul-entry.json') as f:
    e = json.load(f)
print(f'标签: {e[\"label\"]}')
total = 0
for cat in ['new', 'improved', 'fixed', 'under_the_hood']:
    items = e.get(cat, [])
    if items:
        print(f'{cat.replace(\"_\", \" \").title()} ({len(items)}):')
        for item in items:
            print(f'  - {item[\"title\"]}: {item[\"description\"]}')
        total += len(items)
print(f'总计: {total} 个项目')
"

步骤 5:生成内容包

使用步骤 4 中的变更日志条目,生成全部四个内容片段。严格遵循 references/content-rules.md。

一句话摘要(最多 20 词):一句话涵盖最重大的 1-2 个项目。使用平实语言,无术语。

推文线程(3-5 条推文):

  • 推文 1:钩子——“本周我们发布了 [N] 项内容。”或从最大的功能开始
  • 推文 2-N:每条推文一个项目,最多 1-2 句话
  • 最后一条推文:“变更日志:[链接]”或“下周更多。”(可选)
  • 每条推文严格不超过 280 个字符
  • 无标签。无破折号。主动语态。

领英帖子:

  • 无 Markdown(LinkedIn 上星号会作为文字显示)
  • 无标签
  • 创始人语气:“我们发布了”,而不是“我们很高兴地宣布”
  • 短段落(每段 1-2 句话),段落之间用空行分隔
  • 以问题或观察结尾,而非行动号召
  • 总计 150-400 字

邮件摘要:

  • 主题:“本周发布内容:[最大项目] + [再一个]”
  • 正文:50-100 词。“以下是本周发布的内容:” 然后使用项目符号列表。

写入 /tmp/pul-content.json:

{
  "one_liner": "深色模式、更快的 API 和修复的导出错误。",
  "tweet_thread": [
    "本周我们发布了 3 项内容。",
    "深色模式已上线。在设置 > 外观中切换。适用于所有地方。",
    "API 响应时间现在快了 40%。仪表板在一秒内加载。",
    "修复了:CSV 导出会丢失最后一行。现在已修复。",
    "变更日志:[链接]"
  ],
  "linkedin_post": "本周我们发布了 3 项更新。\n\n深色模式已上线。在设置的“外观”下切换。它适用于每一个视图。\n\nAPI 响应时间平均快了 40%。现在大多数用户的仪表板在一秒内加载。\n\n我们还修复了一个错误,即 CSV 导出会悄悄丢弃最后一行。如果你遇到此问题并停止了导出,现在值得再试一次。\n\n你一直期待的功能是什么?",
  "email_snippet": {
    "subject": "本周发布内容:深色模式 + 更快的 API",
    "body": "以下是本周我们发布的内容:\n\n- 深色模式:在设置 > 外观中切换\n- API 响应时间:快 40%,仪表板在 1 秒内加载\n- 修复:CSV 导出不再丢失最后一行\n\n完整变更日志如下。"
  }
}

步骤 6:自我 QA

python3 -c "
import json, re

with open('/tmp/pul-raw.json') as f:
    raw = json.load(f)
with open('/tmp/pul-entry.json') as f:
    entry = json.load(f)
with open('/tmp/pul-content.json') as f:
    content = json.load(f)

full_text = json.dumps(entry) + json.dumps(content)
fails = 0

# 检查 1:无破折号
if chr(8212) in full_text:
    print('失败:发现破折号——请替换为连字符')
    fails += 1
else:
    print('通过:无破折号')

# 检查 2:禁用词汇
banned = ['powerful', 'robust', 'seamless', 'innovative', 'game-changing',
          'streamline', 'leverage', 'transform', 'revolutionize', 'excited to announce',
          'pleased to announce', 'we are thrilled', 'cutting-edge', 'best-in-class',
          'world-class', 'unlock', 'delightful']
found = [w for w in banned if w.lower() in full_text.lower()]
if found:
    print(f'失败:发现禁用词汇:{found}')
    fails += 1
else:
    print('通过:无禁用词汇')

# 检查 3:推文长度
thread = content.get('tweet_thread', [])
long_tweets = [(i+1, len(t)) for i, t in enumerate(thread) if len(t) > 280]
if long_tweets:
    print(f'失败:超过 280 字符的推文:{long_tweets}')
    fails += 1
else:
    print(f'通过:所有 {len(thread)} 条推文都小于 280 字符')

# 检查 4:领英帖子无标签
li = content.get('linkedin_post', '')
if re.search(r'#[A-Za-z]', li):
    print('失败:领英帖子中发现标签')
    fails += 1
else:
    print('通过:领英帖子无标签')

# 检查 5:领英帖子无 Markdown
if '**' in li or '__' in li:
    print('失败:领英帖子中有 Markdown 格式(星号会显示为文字)')
    fails += 1
else:
    print('通过:领英帖子无 Markdown')

# 检查 6:一句话摘要字数统计
one_liner = content.get('one_liner', '')
word_count = len(one_liner.split())
if word_count > 20:
    print(f'失败:一句话摘要为 {word_count} 个词(最多 20)')
    fails += 1
else:
    print(f'通过:一句话摘要为 {word_count} 个词')

# 检查 7:项目数量
entry_items = (len(entry.get('new', [])) + len(entry.get('improved', [])) +
               len(entry.get('fixed', [])) + len(entry.get('under_the_hood', [])))
raw_total = raw['total_items']
print(f'信息:{entry_items} 个变更日志项目来自 {raw_total} 个原始项目')

print()
print(f'结果:{\"通过\" if fails == 0 else f\"失败 ({fails} 个问题)\"}')
"

如果任何检查失败: 在继续步骤 7 之前修复相关临时文件中的问题。修复后重新运行检查。


步骤 7:追加到变更日志并保存内容

python3 << 'PYEOF'
import json, os, re

with open('/tmp/pul-entry.json') as f:
    entry = json.load(f)
with open('/tmp/pul-content.json') as f:
    content = json.load(f)

# 构建新的变更日志部分
lines = [f"## {entry['label']}", ""]

CAT_HEADERS = {
    "new": "### 新功能",
    "improved": "### 改进",
    "fixed": "### 修复",
    "under_the_hood": "### 底层优化",
}

for cat, header in CAT_HEADERS.items():
    items = entry.get(cat, [])
    if items:
        lines.append(header)
        for item in items:
            lines.append(f"- **{item['title']}** -- {item['description']}")
        lines.append("")

lines.append("---")
lines.append("")
new_section = "\n".join(lines)

# 前置添加到 docs/changelog.md
os.makedirs("docs", exist_ok=True)
changelog_path = "docs/changelog.md"

if os.path.exists(changelog_path):
    existing = open(changelog_path).read()
    # 插入到顶级标题之后(如果有的话)或最顶部
    if existing.startswith("# "):
        end_of_heading = existing.index("\n") + 1
        updated = existing[:end_of_heading] + "\n" + new_section + existing[end_of_heading:]
    else:
        updated = new_section + existing
else:
    updated = "# 变更日志\n\n" + new_section

with open(changelog_path, "w") as f:
    f.write(updated)

print(f"变更日志已更新:{changelog_path}")

# 保存内容包
date = entry['date']
content_dir = "docs/product-updates"
os.makedirs(content_dir, exist_ok=True)
content_path = f"{content_dir}/{date}-content.md"

content_lines = [
    f"# 内容包:{entry['label']}",
    "",
    "## 一句话摘要",
    content.get('one_liner', ''),
    "",
    "## 推文线程",
    "",
]
thread = content.get('tweet_thread', [])
for i, tweet in enumerate(thread, 1):
    content_lines.append(f"[{i}/{len(thread)}] {tweet}")
    content_lines.append("")

content_lines += [
    "## 领英帖子",
    "",
    content.get('linkedin_post', ''),
    "",
    "## 邮件摘要",
    "",
    f"主题:{content.get('email_snippet', {}).get('subject', '')}",
    "",
    content.get('email_snippet', {}).get('body', ''),
    "",
]

with open(content_path, "w") as f:
    f.write("\n".join(content_lines))

print(f"内容包:{content_path}")
PYEOF

步骤 8:清理并展示

rm -f /tmp/pul-input.json /tmp/pul-raw.json /tmp/pul-entry.json /tmp/pul-content.json
echo "完成。"

按以下顺序向用户展示:

1. 变更日志条目(格式化的 Markdown,而非原始 JSON):

## 2026年4月23日周

### 新功能
- **深色模式** -- 在设置 > 外观中切换。适用于所有视图。

### 改进
- **API 响应时间** -- 平均快 40%。仪表板现在可在 1 秒内加载…

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

Varnan-Tech/opendirectory

Use when the user asks to generate a blog cover image, thumbnail, or article header. Automatically uses modern typography, brand logos, and Google Search grounding to create beautiful 16:9 images with Gemini 3.1 Flash Image Preview.

Varnan-Tech/opendirectory

World-class brand strategist and naming expert. Uses an interrogation-led discovery phase to extract your brand's DNA, then applies scientific naming frameworks (Phonosemantics) and automated multi-TLD domain checking.

Varnan-Tech/opendirectory

Use when the user asks to generate or update a project's CLAUDE or AGENTS context file from a codebase scan. Writes a focused file under 100 lines containing only the non-obvious build commands, conventions, and gotchas Claude Code needs.

Varnan-Tech/opendirectory

Use when the user wants to verify cold emails, enrich a lead list, or autonomously guess email addresses from a CSV using ValidEmail.co or the open-source Reacher engine.

Varnan-Tech/opendirectory

Competitive intelligence orchestrator tracking companies across 8+ platforms (GitHub, Twitter, Reddit, HN, PH, YC Jobs) with heat scores and AI briefings.

Varnan-Tech/opendirectory

Give it your product URL or description. It finds your top 5 competitors, runs three-track PR research across all of them (editorial, podcasts, communities), identifies which channels appear most frequently, looks up the journalist or host for each, and returns a tiered outreach list with story angles and ready-to-send cold pitch drafts tailored to your product. Use when asked to find PR opportunities, discover where competitors got featured, build a media outreach list, find which journalists cover my space, or get pitch templates for press coverage.

Varnan-Tech/opendirectory

Generate high-converting, deep-dive growth case studies in MDX format. Use this skill when asked to write a case study or blog post about a company's growth, tech stack, or product-led strategy. It handles the full pipeline (researching the company via Tavily, generating a 16:9 cover image, quality checking the draft, uploading assets to cloud storage, and pushing directly to the target repository).

Varnan-Tech/opendirectory

Scans your project for outdated npm, pip, Cargo, Go, or Ruby packages. Runs a CVE security audit. Fetches changelogs, summarizes breaking changes with Gemini, and opens one PR per risk group (patch, minor, major). Includes Diagnosis Mode for install conflicts. Use when asked to update dependencies, check for outdated packages, open dependency PRs, scan for package updates, audit for CVEs, or flag breaking changes in upgrades. Trigger when a user says "check for outdated packages", "update my dependencies", "open PRs for dependency updates", "scan for CVEs", or "which packages need upgrading".

Varnan-Tech/opendirectory

Generates and updates README.md and API reference docs by reading your codebase's functions, routes, types, schemas, and architecture. Uses graphify to build a knowledge graph first, then writes accurate docs from it. Use when asked to write docs, generate a README, document an API, update stale docs, create an API reference from code, add an architecture section, or document a project in any language. Trigger when a user says their docs are missing, outdated, or wants to document their codebase without writing it manually.

Varnan-Tech/opendirectory

Evaluates expired domain candidates against a target niche, scores them by topical relevance, historical activity level, and history cleanliness, then outputs a ranked shortlist with explainable reasoning and risk flags.

Varnan-Tech/opendirectory

Brutally honest developer-experience audit for a GitHub repo. Scores 10 DX dimensions (time-to-first-success, README clarity, visual proof, install, quick-start, docs, examples, community, trust, marketing), writes a shareable roast in the requested tone (brutal/honest/kind), produces a prioritized action plan ranked by impact × effort, and sketches an ideal README. Trigger when user says "roast my repo", "audit my README", "dx audit", "developer experience review", "score my GitHub project", "before launch checklist", or "make my repo shareable".

Varnan-Tech/opendirectory

Drafts and designs a complete HTML email newsletter from a topic or content brief. Outputs paste-ready HTML for Loops, Mailchimp, Beehiiv, Resend, or any standard email platform. Includes subject line options and plain-text fallback. Trigger when a user says "write a newsletter", "draft an email newsletter", "create an HTML email", "design an email for my subscribers", or "write a newsletter about [topic]".

Varnan-Tech/opendirectory

Takes a GitHub PR URL or the current branch and writes a plain-English explanation of what it does and why, then posts it as a PR comment. Use when asked to explain a PR, summarize a pull request, write a plain-English description of a PR, add a summary comment to a PR, or understand what a PR changes. Trigger when a user says "explain this PR", "summarize this pull request", "what does this PR do", "add a comment explaining the PR", or shares a GitHub PR URL and asks what it does.

Varnan-Tech/opendirectory

Audit how often LLMs recommend your brand vs competitors and generate a GEO action plan.

Varnan-Tech/opendirectory

Takes a competitor's public GitHub repo URL, fetches their open issues via the GitHub REST API, filters noise locally, clusters issues into 6 demand categories, computes a demand score per issue and per cluster, and outputs a ranked demand gap report with a GTM messaging brief. Use when asked to scan a competitor's GitHub issues, find what their users are begging for, turn GitHub complaints into product positioning, identify competitor feature gaps, or generate messaging from real user demand. Trigger when a user says "scan competitor issues", "what are users asking for on X repo", "find demand gaps in Y", "turn GitHub issues into messaging", or "what should I build based on competitor complaints".

Varnan-Tech/opendirectory

Find recurring confusion in your repo's GitHub Discussions, rank it by urgency, and draft the actual docs fixes and content angles — with verbatim community quotes and source links as evidence.

Varnan-Tech/opendirectory

SEO keyword research workflow for blog generation using Google Trends data. Use when writing blog posts, planning content calendars, or optimizing articles for search engines. Finds breakout keywords, builds content structure, and generates SEO-optimized blog outlines targeting tech and developer audiences.

Varnan-Tech/opendirectory

Generates a professionally designed case study PDF for B2B SaaS sales and marketing. Supports 7 page layouts, 9 style presets, 1-4 page output. Trigger when user says "create a case study", "write a customer story", "make a case study PDF", "design a success story", "turn this customer win into a PDF".

Varnan-Tech/opendirectory

Generates data visualization charts (bar, line, area, pie, doughnut, scatter, radar, treemap) as PNG using Apache ECharts v6. 1080×1080px default, 5 style presets, highlight annotations. Trigger when user says "create a chart", "visualize data", "make a bar chart", "line graph", "pie chart", "data visualization", "chart this data", "plot", "graph", or "visualize these numbers".

Varnan-Tech/opendirectory

Fetches low-star App Store and Google Play reviews, clusters them into broken-promise patterns, and generates a ranked copy brief with positioning opportunities.

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