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monitor-ci监测 CI

Agent Skill

monitor-ci 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。

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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:monitor-ci(监测 CI)
来源仓库:https://github.com/nrwl/nx-ai-agents-config
仓库路径:skills/monitor-ci
安装命令:
npx skills add https://github.com/nrwl/nx-ai-agents-config --skill monitor-ci
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/nrwl/nx-ai-agents-config --skill monitor-ci

简介

修复自动应用跳过

  • 修复已验证但自动应用已跳过(例如,循环预防)。告知用户,提供手册申请。
  • 修复应用就绪
  • 修复已验证(所有任务或仅限 e2e)。通过 MCP 申请。
  • 修复需要本地验证
  • 修复了未经验证的非 e2e 任务。在本地运行,然后应用或增强。
  • 修复需求审查
  • 修复验证失败/未尝试的问题。分析并决定。
  • 修复失败
  • 自愈失败。获取大量数据,尝试本地修复(首先进行门检查)。
  • 没有修复
  • 没有可用的修复。获取大量数据,尝试本地修复(首先进行门检查)或退出。
  • 环境问题
  • 通过 MCP 请求环境重新运行(首先进行门检查)。
  • 自我修复节流
  • 拒绝旧的修复,尝试本地修复。
  • 无新菜谱
  • CI 尝试从未产生。自动修复工作流程或在指导下退出。
  • cipe_无_任务
  • CI 失败,没有任务。使用空提交重试一次。
  • 主要规则(始终适用):
  • Git 安全:按名称暂存特定文件 — git add -A
  • 或 git add 。
  • 存在泄露用户不相关的正在进行的工作或秘密的风险
  • 环境故障(OOM、未找到命令、权限被拒绝):立即保释。这些不是代码错误,因此在它们上花费本地修复预算是浪费
  • 门检查:运行 ci-state-update.mjs gateway
  • 在本地修复尝试之前 - 如果预算用尽,则打印消息并退出
  • 主循环
  • 第 1 步:初始化跟踪
  • Cycle_count = 0 # 仅针对代理启动的周期递增(根据 --max-cycles 进行计数)
  • 开始时间 = 现在()
  • 无进度计数 = 0
  • 本地验证计数 = 0
  • env_rerun_count = 0
  • 最后的食谱网址=空
  • Expected_commit_sha = null
  • agent_triggered = false # 在监视器采取触发新 CI 尝试的操作后设置 true
  • 轮询计数 = 0
  • 等待模式=假
  • 上一个状态=空
  • prev_cipe_status = null
  • prev_sh_status = null
  • prev_verification_status = null
  • prev_failure_classification = null
  • 第 2 步:轮询循环
  • 重复直到完成:
  • 2a.生成子代理 (FETCH_STATUS)
  • 根据模式确定选择字段:
  • 等待模式:使用 WAIT_FIELDS ( cipeUrl,commitSha,cipeStatus)
  • 正常模式(第一次轮询或在 newCipeDetected 之后):使用 LIGHT_FIELDS
  • 任务( 代理:“ci-monitor-subagent”, 型号: 俳句, 提示:“分支 '<branch>' 的 FETCH_STATUS。
  • 选择:'<字段>'")
  • 子代理调用 ci_information
  • 并返回一个包含请求字段的 JSON 对象。这是一个前台调用——等待结果。
  • 2b.运行决策脚本
  • 节点 < Skill_dir > /scripts/ci-poll-decide.mjs '<subagent_result_json>' < poll_count > < 详细程度 > \
  • [ --等待模式 ] \
  • [ --prev-cipe-url <last_cipe_url > ] \
  • [--expected-sha<expected_commit_sha>]\
  • [ --prev-status < prev_status > ] \
  • [ --timeout < 超时秒数 > ] \
  • [ --new-cipe-timeout < new_cipe_timeout_seconds > ] \
  • [ --env-rerun-count < env_rerun_count > ] \
  • [ --no-progress-count < no_progress_count > ] \
  • [ --prev-cipe-status < prev_cipe_status > ] \
  • [ --prev-sh-status < prev_sh_status > ] \
  • [ --prev-verification-status < prev_verification_status > ] \
  • [ --prev-failure-classification < prev_failure_classification > ]
  • 该脚本输出单个 JSON 行: { action, code, message, delay?, noProgressCount, envRerunCount, fields?, newCipeDetected?, verificableTaskIds? }
  • 2c.处理脚本输出
  • 解析 JSON 输出并更新跟踪状态:
  • no_progress_count = 输出.noProgressCount
  • env_rerun_count = 输出.envRerunCount
  • prev_cipe_status = subagent_result.cipeStatus
  • prev_sh_status = subagent_result.selfHealingStatus
  • prev_verification_status = subagent_result.verificationStatus
  • prev_failure_classification = subagent_result.failureClassification
  • prev_status = 输出.action + ":" + (output.code || subagent_result.cipeStatus)
  • 轮询计数++
  • 基于行动
  • 行动==“民意调查”
  • : 打印输出.message, 睡眠输出.delay
  • 秒,转到 2a
  • 如果输出.newCipeDetected
  • :清除等待模式,重置wait_mode = false
  • 动作==“等待”
  • : 打印输出.message, 睡眠输出.delay
  • 秒,转到 2a
  • 行动==“完成”
  • :使用output.code继续步骤3
  • 第 3 步:处理可操作状态
  • 当决策脚本返回操作==“完成”时
  • 在处理代码之前运行循环检查(步骤 4)
  • 检查返回的代码
  • 在上表中查找默认行为
  • 检查用户指令是否覆盖默认值
  • 执行适当的操作
  • 如果操作需要新的 CI 尝试,则更新跟踪(请参阅步骤 3a)
  • 如果操作导致循环,请转至步骤 2
  • 生成动作的子代理
  • 有几种状态需要获取大量数据或调用 MCP:
  • fix_apply_ready :使用 APPLY 生成 UPDATE_FIX 子代理
  • fix_needs_local_verify :在本地验证之前生成 FETCH_HEAVY 子代理以获取修复详细信息
  • fix_needs_review :生成 FETCH_HEAVY 子代理 → 获取建议的FixDescription, 建议修复总结, 任务失败摘要
  • fix_failed / no_fix :生成 FETCH_HEAVY 子代理 → 获取 taskFailureSummaries
  • 对于本地修复上下文
  • environment_issue :使用 RERUN_ENVIRONMENT_STATE 生成 UPDATE_FIX 子代理
  • self_healing_throttled :生成 FETCH_HEAVY 子代理 → 获取 selfHealingSkipMessage
  • ;然后针对每个旧修复使用 FETCH_THROTTLE_INFO + UPDATE_FIX
  • 步骤 3a:跟踪新 CI 尝试检测的状态
  • 在应触发新 CI 尝试的操作之后,运行:
  • 节点 <skill_dir> /scripts/ci-state-update.mjs 后操作 \
  • --action <类型> \
  • --cipe-url < current_cipe_url > \
  • --commit-sha < git_rev_parse_HEAD >
  • 操作类型:修复-自动应用, 应用 mcp, 应用本地推送, 拒绝-修复-推送, 本地修复推送, 环境重新运行, 自动修复推送, 空提交推
  • 该脚本返回 { waitMode, pollCount, lastCipeUrl, ExpectedCommitSha, agentTriggered }
  • 。更新输出中的所有跟踪状态,然后转到步骤 2。
  • 第四步:周期分类和进度跟踪
  • 当决策脚本返回action ==“done”时,在处理代码之前运行循环检查:
  • 节点 <skill_dir> /scripts/ci-state-update.mjs 循环检查 \
  • --code <代码> \
  • [ --代理触发 ] \
  • --cycle-count < 周期计数 > --max-cycles < 最大周期 > \
  • --env-rerun-count < env_rerun_count >
  • 该脚本返回 { CycleCount, agentTriggered, envRerunCount, ApproachingLimit, message }
  • 。从输出更新跟踪状态。
  • 如果接近极限
  • → 询问用户是否继续(5 个或 10 个以上循环)或停止监控
  • 如果上一个周期不是代理触发的(人为推动),则记录检测到人为发起的推动
  • 进度追踪
  • 无进度计数
  • 、断路器(5 个轮询)和退避重置由 ci-poll-decide.mjs 处理(进度 = cipeStatus、selfHealingStatus、verificationStatus 或 failureClassification 中的任何更改)
  • env_rerun_count
  • 非环境状态的重置由 ci-state-update.mjs Cycle-check 处理
  • 检测到新的 CI 尝试时(轮询脚本返回 newCipeDetected) → 重置 local_verify_count = 0, env_rerun_count = 0
  • 错误处理
  • 错误
  • 行动
  • Git 变基冲突
  • 报告给用户,退出
  • nx-cloud 本地应用
  • 失败
  • 通过 MCP 拒绝修复(操作:“REJECT”),然后尝试手动修补(拒绝 + 从头开始修复流程)或退出
  • MCP 工具错误
  • 重试一次,如果失败报告给用户
  • 子代理生成失败
  • 重试一次,如果失败则错误退出
  • 决策脚本错误
  • 视为错误
  • 状态,增加 no_progress_count
  • 未检测到新的 CI 尝试
  • 如果--自动修复工作流程,尝试更新锁文件;否则向用户报告并提供指导
  • 锁定文件自动修复失败
  • 向用户报告,退出并指导检查 CI 日志
  • 用户说明示例
  • 用户可以覆盖默认行为:
  • 说明
  • 效果
  • “从不自动应用”
  • 在应用任何修复之前始终提示
  • “在 git Push 之前总是询问”
  • 每次推送前提示
  • “拒绝对 e2e 任务的任何修复”
  • 如果失败TaskIds则自动拒绝
  • 包含 e2e
  • “无论验证如何,都应用所有修复”
  • 跳过验证检查,应用所有内容
  • “如果置信度 < 70,则拒绝”
  • 申请前检查置信度字段
  • “在应用之前运行‘nx受影响-t类型检查’”
  • 添加本地验证步骤
  • “自动修复工作流程故障”
  • 尝试在 CI 尝试失败时更新锁定文件
  • “等待 45 分钟进行新的 CI 尝试”
  • 覆盖 new-CI-Attempt 超时(默认值:10 分钟)
  • 每周安装量
  • 1.1K
  • 存储库
  • nrwl/nx-ai-代理配置
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SKILL.md

Monitor CI Command

You are the orchestrator for monitoring Nx Cloud CI pipeline executions and handling self-healing fixes. You spawn subagents to interact with Nx Cloud, run deterministic decision scripts, and take action based on the results.

Context

  • Current Branch:!git branch --show-current
  • Current Commit:!git rev-parse --short HEAD
  • Remote Status:!git status -sb | head -1

User Instructions

$ARGUMENTS

Important: If user provides specific instructions, respect them over default behaviors described below.

Configuration Defaults

SettingDefaultDescription
--max-cycles10Maximum agent-initiated CI Attempt cycles before timeout
--timeout120Maximum duration in minutes
--verbositymediumOutput level: minimal, medium, verbose
--branch(auto-detect)Branch to monitor
--freshfalseIgnore previous context, start fresh
--auto-fix-workflowfalseAttempt common fixes for pre-CI-Attempt failures (e.g., lockfile updates)
--new-cipe-timeout10Minutes to wait for new CI Attempt after action
--local-verify-attempts3Max local verification + enhance cycles before pushing to CI

Parse any overrides from $ARGUMENTS and merge with defaults.

Nx Cloud Connection Check

Before starting the monitoring loop, verify the workspace is connected to Nx Cloud. Without this connection, no CI data is available and the entire skill is inoperable.

Step 0: Verify Nx Cloud Connection

  1. Check nx.json at workspace root for nxCloudId or nxCloudAccessToken
  2. If nx.json missing OR neither property exists → exit with: Nx Cloud not connected. Unlock 70% faster CI and auto-fix broken PRs with https://nx.dev/nx-cloud
  3. If connected → continue to main loop

Architecture Overview

  1. This skill (orchestrator): spawns subagents, runs scripts, prints status, does local coding work
  2. ci-monitor-subagent (haiku): calls one MCP tool (ci_information or update_self_healing_fix), returns structured result, exits
  3. ci-poll-decide.mjs (deterministic script): takes ci_information result + state, returns action + status message
  4. ci-state-update.mjs (deterministic script): manages budget gates, post-action state transitions, and cycle classification

Status Reporting

The decision script handles message formatting based on verbosity. When printing messages to the user:

  • Prepend [monitor-ci] to every message from the script's message field
  • For your own action messages (e.g. "Applying fix via MCP..."), also prepend [monitor-ci]

Anti-Patterns

These behaviors cause real problems — racing with self-healing, losing CI progress, or wasting context:

Anti-PatternWhy It's Bad
Using CI provider CLIs with --watch flags (e.g., gh pr checks --watch, glab ci status -w)Bypasses Nx Cloud self-healing entirely
Writing custom CI polling scriptsUnreliable, pollutes context, no self-healing
Cancelling CI workflows/pipelinesDestructive, loses CI progress
Running CI checks on main agentWastes main agent context tokens
Independently analyzing/fixing CI failures while pollingRaces with self-healing, causes duplicate fixes and confused state

If this skill fails to activate, the fallback is:

  1. Use CI provider CLI for a one-time, read-only status check (single call, no watch/polling flags)
  2. Immediately delegate to this skill with gathered context
  3. Do not continue polling on main agent — it wastes context tokens and bypasses self-healing

Session Context Behavior

If the user previously ran /monitor-ci in this session, you may have prior state (poll counts, last CI Attempt URL, etc.). Resume from that state unless --fresh is set, in which case discard it and start from Step 1.

MCP Tool Reference

The ci_information and update_self_healing_fix tools are called via the ci-monitor-subagent, not directly from the orchestrator. Calling MCP tools directly wastes main agent context with large response payloads. The field sets below are for composing subagent prompts (see Step 2a).

Three field sets control polling efficiency — use the lightest set that gives you what you need:

WAIT_FIELDS: 'cipeUrl,commitSha,cipeStatus'
LIGHT_FIELDS: 'cipeStatus,cipeUrl,branch,commitSha,selfHealingStatus,verificationStatus,userAction,failedTaskIds,verifiedTaskIds,selfHealingEnabled,failureClassification,couldAutoApplyTasks,autoApplySkipped,autoApplySkipReason,shortLink,confidence,confidenceReasoning,hints,selfHealingSkippedReason,selfHealingSkipMessage'
HEAVY_FIELDS: 'taskOutputSummary,suggestedFix,suggestedFixReasoning,suggestedFixDescription'

The ci_information tool accepts branch (optional, defaults to current git branch), select (comma-separated field names), and pageToken (0-based pagination for long strings).

The update_self_healing_fix tool accepts a shortLink and an action: APPLY, REJECT, or RERUN_ENVIRONMENT_STATE.

Default Behaviors by Status

The decision script returns one of the following statuses. This table defines the default behavior for each. User instructions can override any of these.

Simple exits — just report and exit:

StatusDefault Behavior
ci_successExit with success
cipe_canceledExit, CI was canceled
cipe_timed_outExit, CI timed out
polling_timeoutExit, polling timeout reached
circuit_breakerExit, no progress after 13 consecutive polls
environment_rerun_capExit, environment reruns exhausted
fix_auto_applyingSelf-healing is handling it — just record last_cipe_url, enter wait mode. No MCP call or local git ops needed.
errorWait 60s and loop

Statuses requiring action — when handling these in Step 3, read references/fix-flows.md for the detailed flow:

StatusSummary
fix_auto_apply_skippedFix verified but auto-apply skipped (e.g., loop prevention). Inform user, offer manual apply.
fix_apply_readyFix verified (all tasks or e2e-only). Apply via MCP.
fix_needs_local_verifyFix has unverified non-e2e tasks. Run locally, then apply or enhance.
fix_needs_reviewFix verification failed/not attempted. Analyze and decide.
fix_failedSelf-healing failed. Fetch heavy data, attempt local fix (gate check first).
no_fixNo fix available. Fetch heavy data, attempt local fix (gate check first) or exit.
environment_issueRequest environment rerun via MCP (gate check first).
self_healing_throttledReject old fixes, attempt local fix.
no_new_cipeCI Attempt never spawned. Auto-fix workflow or exit with guidance.
cipe_no_tasksCI failed with no tasks. Retry once with empty commit.

Key rules (always apply):

  • Git safety: Stage specific files by name — git add -A or git add. risks committing the user's unrelated work-in-progress or secrets
  • Environment failures (OOM, command not found, permission denied): bail immediately. These aren't code bugs, so spending local-fix budget on them is wasteful
  • Gate check: Run ci-state-update.mjs gate before local fix attempts — if budget exhausted, print message and exit

Main Loop

Step 1: Initialize Tracking

cycle_count = 0            # Only incremented for agent-initiated cycles (counted against --max-cycles)
start_time = now()
no_progress_count = 0
local_verify_count = 0
env_rerun_count = 0
last_cipe_url = null
expected_commit_sha = null
agent_triggered = false    # Set true after monitor takes an action that triggers new CI Attempt
poll_count = 0
wait_mode = false
prev_status = null
prev_cipe_status = null
prev_sh_status = null
prev_verification_status = null
prev_failure_classification = null

Step 2: Polling Loop

Repeat until done:

2a. Spawn subagent (FETCH_STATUS)

Determine select fields based on mode:

  • Wait mode: use WAIT_FIELDS (cipeUrl,commitSha,cipeStatus)
  • Normal mode (first poll or after newCipeDetected): use LIGHT_FIELDS
Task(
  agent: "ci-monitor-subagent",
  model: haiku,
  prompt: "FETCH_STATUS for branch '<branch>'.
           select: '<fields>'"
)

The subagent calls ci_information and returns a JSON object with the requested fields. This is a foreground call — wait for the result.

2b. Run decision script

node <skill_dir>/scripts/ci-poll-decide.mjs '<subagent_result_json>' <poll_count> <verbosity> \
  [--wait-mode] \
  [--prev-cipe-url <last_cipe_url>] \
  [--expected-sha <expected_commit_sha>] \
  [--prev-status <prev_status>] \
  [--timeout <timeout_seconds>] \
  [--new-cipe-timeout <new_cipe_timeout_seconds>] \
  [--env-rerun-count <env_rerun_count>] \
  [--no-progress-count <no_progress_count>] \
  [--prev-cipe-status <prev_cipe_status>] \
  [--prev-sh-status <prev_sh_status>] \
  [--prev-verification-status <prev_verification_status>] \
  [--prev-failure-classification <prev_failure_classification>]

The script outputs a single JSON line: {action, code, message, delay?, noProgressCount, envRerunCount, fields?, newCipeDetected?, verifiableTaskIds?}

2c. Process script output

Parse the JSON output and update tracking state:

  • no_progress_count = output.noProgressCount
  • env_rerun_count = output.envRerunCount
  • prev_cipe_status = subagent_result.cipeStatus
  • prev_sh_status = subagent_result.selfHealingStatus
  • prev_verification_status = subagent_result.verificationStatus
  • prev_failure_classification = subagent_result.failureClassification
  • prev_status = output.action + ":" + (output.code || subagent_result.cipeStatus)
  • poll_count++

Based on action:

  • action == "poll": Print output.message, sleep output.delay seconds, go to 2a

- If output.newCipeDetected: clear wait mode, reset wait_mode = false

  • action == "wait": Print output.message, sleep output.delay seconds, go to 2a
  • action == "done": Proceed to Step 3 with output.code

Step 3: Handle Actionable Status

When decision script returns action == "done":

  1. Run cycle-check (Step 4) before handling the code
  2. Check the returned code
  3. Look up default behavior in the table above
  4. Check if user instructions override the default
  5. Execute the appropriate action
  6. If action expects new CI Attempt, update tracking (see Step 3a)
  7. If action results in looping, go to Step 2

Spawning subagents for actions

Several statuses require fetching heavy data or calling MCP:

  • fix_apply_ready: Spawn UPDATE_FIX subagent with APPLY
  • fix_needs_local_verify: Spawn FETCH_HEAVY subagent for fix details before local verification
  • fix_needs_review: Spawn FETCH_HEAVY subagent → get suggestedFixDescription, suggestedFixSummary, taskFailureSummaries
  • fix_failed / no_fix: Spawn FETCH_HEAVY subagent → get taskFailureSummaries for local fix context
  • environment_issue: Spawn UPDATE_FIX subagent with RERUN_ENVIRONMENT_STATE
  • self_healing_throttled: Spawn FETCH_HEAVY subagent → get selfHealingSkipMessage; then FETCH_THROTTLE_INFO + UPDATE_FIX for each old fix

Step 3a: Track State for New-CI-Attempt Detection

After actions that should trigger a new CI Attempt, run:

node <skill_dir>/scripts/ci-state-update.mjs post-action \
  --action <type> \
  --cipe-url <current_cipe_url> \
  --commit-sha <git_rev_parse_HEAD>

Action types: fix-auto-applying, apply-mcp, apply-local-push, reject-fix-push, local-fix-push, env-rerun, auto-fix-push, empty-commit-push

The script returns {waitMode, pollCount, lastCipeUrl, expectedCommitSha, agentTriggered}. Update all tracking state from the output, then go to Step 2.

Step 4: Cycle Classification and Progress Tracking

When the decision script returns action == "done", run cycle-check before handling the code:

node <skill_dir>/scripts/ci-state-update.mjs cycle-check \
  --code <code> \
  [--agent-triggered] \
  --cycle-count <cycle_count> --max-cycles <max_cycles> \
  --env-rerun-count <env_rerun_count>

The script returns {cycleCount, agentTriggered, envRerunCount, approachingLimit, message}. Update tracking state from the output.

  • If approachingLimit → ask user whether to continue (with 5 or 10 more cycles) or stop monitoring
  • If previous cycle was NOT agent-triggered (human pushed), log that human-initiated push was detected

Progress Tracking

  • no_progress_count, circuit breaker (5 polls), and backoff reset are handled by ci-poll-decide.mjs (progress = any change in cipeStatus, selfHealingStatus, verificationStatus, or failureClassification)
  • env_rerun_count reset on non-environment status is handled by ci-state-update.mjs cycle-check
  • On new CI Attempt detected (poll script returns newCipeDetected) → reset local_verify_count = 0, env_rerun_count = 0

Error Handling

ErrorAction
Git rebase conflictReport to user, exit
nx-cloud apply-locally failsReject fix via MCP (action: "REJECT"), then attempt manual patch (Reject + Fix From Scratch Flow) or exit
MCP tool errorRetry once, if fails report to user
Subagent spawn failureRetry once, if fails exit with error
Decision script errorTreat as error status, increment no_progress_count
No new CI Attempt detectedIf --auto-fix-workflow, try lockfile update; otherwise report to user with guidance
Lockfile auto-fix failsReport to user, exit with guidance to check CI logs

User Instruction Examples

Users can override default behaviors:

InstructionEffect
"never auto-apply"Always prompt before applying any fix
"always ask before git push"Prompt before each push
"reject any fix for e2e tasks"Auto-reject if failedTaskIds contains e2e
"apply all fixes regardless of verification"Skip verification check, apply everything
"if confidence < 70, reject"Check confidence field before applying
"run 'nx affected -t typecheck' before applying"Add local verification step
"auto-fix workflow failures"Attempt lockfile updates on pre-CI-Attempt failures
"wait 45 min for new CI Attempt"Override new-CI-Attempt timeout (default: 10 min)

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

34.04%
按下载量换算2,936

Claude

32.95%
按下载量换算2,842

Cursor

19.78%
按下载量换算1,706

Gemini CLI

9.47%
按下载量换算817

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/nrwl/nx-ai-agents-config --skill monitor-ci 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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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