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Agent Skill · 开发

pagerduty-oncall(传呼值班)

pagerduty-oncall 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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。

pagerduty-oncall 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描有提醒项,按安装量在开发类的 21,746 个 Skill 里排第 12,288。

总安装 1,354周安装 57GitHub Stars 1下载 474核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:pagerduty-oncall(传呼值班)
来源仓库:https://github.com/delexw/claude-code-misc
仓库路径:skills/pagerduty-oncall
安装命令:
npx skills add https://github.com/delexw/claude-code-misc --skill pagerduty-oncall
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/delexw/claude-code-misc --skill pagerduty-oncall

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

pagerduty-oncall 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中围绕仓库状态、代码变更或协作事项进行整理时使用。它用于认证、列出升级策略、获取所有事件详情并分析值班团队的关联性。

通过 GitHub 安装并使用 npx skills add 命令添加技能,需结合原始 README 核验具体用法。安装前建议确认权限范围和维护状态,注意是否会触发联网、命令执行或文件读写操作。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/pagerduty-oncall

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/delexw/claude-code-misc --skill pagerduty-onca…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

pagerduty-oncall 怎么安装?

在终端执行 npx skills add https://github.com/delexw/claude-code-misc --skill pagerduty-oncall。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

pagerduty-oncall 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

pagerduty-oncall 需要什么权限?

权限级别是接触敏感数据。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。

pagerduty-oncall 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

pagerduty-oncall 用的人多吗?

公开数据是累计安装 1,354 次,近一周安装 57 次,下载 474 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

pagerduty-oncall 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
pagerduty-oncall

传呼值班 · 本页

1,3541接触敏感数据未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

PagerDuty On-Call Incident Investigator

Authenticate, list escalation policies, fetch all incidents and their details, then analyse relevance across on-call teams.

Inputs

Raw arguments: $ARGUMENTS

Infer from the arguments:

  • QUERY: what to investigate. Defaults to "incidents today". Interpret the time range to derive --since and --until dates (YYYY-MM-DD) in UTC (not local timezone). See the date derivation table in Step 5.
  • MODE: Inferred from QUERY.

- If QUERY starts with "incident " or contains a PagerDuty URL (matching pagerduty\.com/incidents/([A-Z0-9]+)) → SINGLE-INCIDENT mode - Otherwise → LIST mode (existing behaviour)

  • SINCE: (optional) UTC ISO8601 start of analysis window. Takes precedence over QUERY-derived dates in LIST mode.
  • UNTIL: (optional) UTC ISO8601 end of analysis window. Takes precedence over QUERY-derived dates in LIST mode.
  • TZ_DISPLAY: (optional) Timezone abbreviation for report display (e.g. the output of date +"%Z" on the calling machine). When provided, use this for all report timestamps instead of detecting via system clock.

Target Escalation Policies

The list of escalation policies to investigate is resolved in order:

  1. config.json — escalation_policies array in config.json
  2. PD_ESCALATION_POLICIES — comma-separated env var (e.g. "On Call, Platform Engineering On-Call")
  3. If both are empty, all escalation policies are included

System Requirements

  • pd CLI installed (https://github.com/martindstone/pagerduty-cli)
  • Environment variable PAGERDUTY_API_TOKEN set with a valid PagerDuty REST API token. Important: When checking this variable, verify at least 2 times before concluding it is not set. Environment variables can appear unset due to shell context differences. Never expose the value — use existence checks only (e.g. test -n "$PAGERDUTY_API_TOKEN").

Scripts

All parsing and filtering is handled by scripts in ${CLAUDE_SKILL_DIR}/scripts/:

ScriptPurpose
extract-json.jsExtracts JSON from pd CLI output that may contain non-JSON text
filter-eps.jsFilters EPs by target names, extracts relevant fields
filter-incidents.jsFilters incidents by service IDs from ep-list.json, extracts fields
parse-log.jsExtracts relevant fields from incident log entries
parse-notes.jsExtracts relevant fields from incident notes
parse-analytics.jsExtracts relevant fields from incident analytics

All scripts read from stdin and write to stdout. Pipe pd CLI output through extract-json.js first, then through the appropriate filter/parse script.

Output Directory

All intermediate JSON and the final report are saved to:

.pagerduty-oncall-tmp/
├── ep-list.json              # Filtered escalation policies
├── incidents.json            # Incidents filtered by target EPs
├── logs/<INCIDENT_ID>.json   # Log entries per incident
├── notes/<INCIDENT_ID>.json  # Notes per incident
├── analytics/<INCIDENT_ID>.json # Analytics per incident
└── report.md                 # Final analysis report

Execution

1. Verify Connection

Check that PAGERDUTY_API_TOKEN is set (verify at least 2 times before concluding it is missing):

test -n "$PAGERDUTY_API_TOKEN" && echo "TOKEN_SET" || echo "TOKEN_MISSING"

If token is set, authenticate:

pd auth add --token "$PAGERDUTY_API_TOKEN"

If authentication fails, use AskUserQuestion to inform the user and link to the PagerDuty CLI User Guide for setup instructions. Do NOT continue until authentication succeeds.

2. Detect Local Timezone

If TZ_DISPLAY was not provided, run the following to get the local timezone from the current machine:

date +"%Z"

Record the output as the display timezone for all report timestamps.

3. Prepare Output Directory

Create the output directory .pagerduty-oncall-tmp/ and subdirectories logs/, notes/, analytics/ before saving any files.

4. Load Escalation Policy Configuration

Read config.json using the Read tool. Extract the escalation_policies array.

If the array is empty, check for the PD_ESCALATION_POLICIES env var:

test -n "$PD_ESCALATION_POLICIES" && echo "$PD_ESCALATION_POLICIES" || echo "EMPTY"

Build the list of target EP names for the next step. If both sources are empty, no names will be passed (all EPs included).

5. List and Filter Escalation Policies

Fetch, extract JSON, filter by target names, and save in one pipeline:

pd ep list --json 2>&1 | node ${CLAUDE_SKILL_DIR}/scripts/extract-json.js | node ${CLAUDE_SKILL_DIR}/scripts/filter-eps.js "EP Name 1" "EP Name 2" > .pagerduty-oncall-tmp/ep-list.json

Pass each target EP name as a separate argument to filter-eps.js. If no target names, omit the arguments (all EPs pass through).

6-ALT. Single-Incident Mode (when MODE = SINGLE-INCIDENT)

Extract the incident ID from QUERY:

  • From URL: match pagerduty\.com/incidents/([A-Z0-9]+) → capture group is the ID
  • From shorthand: match incident ([A-Z0-9]+) → capture group is the ID

Fetch the specific incident:

pd rest get --endpoint "/incidents/<INCIDENT_ID>" 2>&1 \
  | node ${CLAUDE_SKILL_DIR}/scripts/extract-json.js \
  > .pagerduty-oncall-tmp/incidents.json

Extract from the incident JSON:

  • id, incident_number, title, status, urgency
  • created_at, resolved_at (null if still open)
  • service.summary → SERVICE
  • escalation_policy.summary → EP

Also fetch related incidents on the same service within ±2h of created_at:

pd incident list --json --statuses=open --statuses=closed --statuses=triggered --statuses=acknowledged --statuses=resolved \
  --since=<created_at minus 2h, YYYY-MM-DD UTC> --until=<created_at plus 2h, YYYY-MM-DD UTC> 2>&1 \
  | node ${CLAUDE_SKILL_DIR}/scripts/extract-json.js \
  | node ${CLAUDE_SKILL_DIR}/scripts/filter-incidents.js .pagerduty-oncall-tmp/ep-list.json \
  > .pagerduty-oncall-tmp/related-incidents.json

Then continue to Step 6 (gather details) and Step 7 (report) as normal.

The report MUST include a structured metadata section at the top (UTC ISO8601 for machine parsing):

## Incident Metadata
- created_at: <ISO8601 UTC>
- resolved_at: <ISO8601 UTC or "ongoing">
- service: <name>
- title: <title>

Followed by the human-readable timeline in local timezone (see Step 7).

6. Fetch and Filter Incidents (LIST mode)

Derive SINCE_DATE and UNTIL_DATE (YYYY-MM-DD, in UTC) from QUERY. Important: --until is exclusive in the pd CLI — it does NOT include that day.

If SINCE and UNTIL are provided as UTC ISO8601 timestamps, pass them directly to pd incident list — the pd CLI accepts ISO8601 for --since / --until:

pd incident list --json ... --since=<SINCE> --until=<UNTIL> ...

Otherwise, derive SINCE and UNTIL as UTC ISO8601 timestamps from QUERY using the agent's local timezone. "Today" means the local calendar day — an incident at 11am local is in "today" even if it falls on a different UTC date. Compute local day boundaries and convert to UTC ISO8601 for the pd API.

For relative durations, use pd CLI natural language directly:

pd incident list --json ... --since "24 hours ago" --until "now" ...

For named days and calendar ranges, compute local midnight as UTC ISO8601:

# Start of local today as UTC ISO8601 (Linux / macOS)
date -d "$(date +%Y-%m-%d) 00:00:00" -u +%Y-%m-%dT%H:%M:%SZ          # Linux
date -jf "%Y-%m-%d %H:%M:%S" "$(date +%Y-%m-%d) 00:00:00" -u +%Y-%m-%dT%H:%M:%SZ  # macOS

# Start of local yesterday
date -d "$(date -d yesterday +%Y-%m-%d) 00:00:00" -u +%Y-%m-%dT%H:%M:%SZ       # Linux
date -jf "%Y-%m-%d %H:%M:%S" "$(date -v-1d +%Y-%m-%d) 00:00:00" -u +%Y-%m-%dT%H:%M:%SZ  # macOS

Use these ISO8601 values directly as --since / --until on pd incident list. For "today": SINCE = start of local today as UTC ISO8601, UNTIL = omit (defaults to now). For "yesterday": SINCE = start of local yesterday, UNTIL = start of local today. For explicit date ranges: SINCE = start of first local day, UNTIL = start of day after last local day.

Then fetch, extract JSON, filter by service IDs from ep-list.json, and save. Run this as two separate commands — do NOT chain Step 4 and Step 5 into a single pipeline, as filter-incidents.js reads ep-list.json from disk and the file must be fully written before Step 5 begins:

# Step 4 must be complete before running this
pd incident list --json --statuses=open --statuses=closed --statuses=triggered --statuses=acknowledged --statuses=resolved --since=SINCE_DATE --until=UNTIL_DATE 2>&1 > /tmp/pd-incidents-raw.json
node ${CLAUDE_SKILL_DIR}/scripts/extract-json.js < /tmp/pd-incidents-raw.json | node ${CLAUDE_SKILL_DIR}/scripts/filter-incidents.js .pagerduty-oncall-tmp/ep-list.json > .pagerduty-oncall-tmp/incidents.json

Read .pagerduty-oncall-tmp/incidents.json to check the result. If the array is empty, write a report noting zero incidents and stop.

7. Gather Incident Details

For each incident in .pagerduty-oncall-tmp/incidents.json, fetch details sequentially (to avoid PagerDuty API rate limits). Use the id field (e.g. Q1V3O5Q3JX39LJ), NOT incident_number — the pd CLI only accepts internal IDs.

Log entries:

pd incident log -i <INCIDENT_ID> --json 2>&1 | node ${CLAUDE_SKILL_DIR}/scripts/extract-json.js | node ${CLAUDE_SKILL_DIR}/scripts/parse-log.js > .pagerduty-oncall-tmp/logs/<INCIDENT_ID>.json

Notes:

pd incident notes -i <INCIDENT_ID> --output=json 2>&1 | node ${CLAUDE_SKILL_DIR}/scripts/extract-json.js | node ${CLAUDE_SKILL_DIR}/scripts/parse-notes.js > .pagerduty-oncall-tmp/notes/<INCIDENT_ID>.json

Analytics:

pd incident analytics -i <INCIDENT_ID> --json 2>&1 | node ${CLAUDE_SKILL_DIR}/scripts/extract-json.js | node ${CLAUDE_SKILL_DIR}/scripts/parse-analytics.js > .pagerduty-oncall-tmp/analytics/<INCIDENT_ID>.json

8. Analyse and Report

Read all saved JSON files from .pagerduty-oncall-tmp/ using the Read tool. Produce a structured analysis and save it using Write to .pagerduty-oncall-tmp/report.md:

When in SINGLE-INCIDENT mode, begin the report with the structured metadata block (UTC ISO8601, for machine parsing by the PIR orchestrator):

## Incident Metadata
- created_at: 2026-03-20T02:30:00Z
- resolved_at: 2026-03-20T04:15:00Z
- service: storefront
- title: High error rate on checkout

Then include:

  1. Incident Summary Table — For each incident: ID, title, service, escalation policy, status, urgency, created/resolved timestamps in local timezone (use TZ_DISPLAY if provided, otherwise detect via system clock; format: 2026-03-20 15:30 <TZ>), duration
  2. Cross-Team Correlation — Identify incidents that overlap in time across different escalation policies. Flag potential cascading failures or shared root causes
  3. Timeline — Chronological view of all incidents across all teams in local timezone, highlighting clusters of activity
  4. Key Findings — Patterns, recurring services, repeated triggers, or escalation policy gaps
  5. Recommendations — Actionable suggestions based on the analysis

All timestamps displayed to users must use local timezone with TZ abbreviation. Do NOT use UTC in human-readable sections. Use TZ_DISPLAY if provided, otherwise detect via system clock (date +"%Z").

After writing the report, explicitly tell the user its location as the final line of your response:

Report saved to .pagerduty-oncall-tmp/report.md

平台分布

Codex

33.75%
按下载量换算160

Claude

31.87%
按下载量换算151

Cursor

20.55%
按下载量换算97

Gemini CLI

9.74%
按下载量换算46

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

可疑

权限和风险

接触敏感数据

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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