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next-actionsNext.js actions 搜索

Agent Skill

next-actions 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

442

周安装

19

GitHub Stars

公开资料未说明

下载量

155
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

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:next-actions(Next.js actions 搜索)
来源仓库:https://github.com/michael-menard/monorepo
仓库路径:skills/next-actions
安装命令:
npx skills add https://github.com/michael-menard/monorepo --skill next-actions
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/michael-menard/monorepo --skill next-actions

简介

该技能用于搜索 Next.js 官方文档中的 Actions 相关示例与实现方式。

  • 适用于服务端操作开发、表单处理与异步任务编排场景。
  • 提供代码片段、最佳实践与版本兼容性说明的快速检索功能。
  • 基于 GitHub 公开资源构建,无需额外 API 密钥即可使用基础查询。
  • 返回结果仅供参考,实际实现需结合项目安全与错误处理机制。

SKILL.md

/next-actions — Find Next Actions from KB Database

CRITICAL EXECUTION RULE: Do NOT spawn sub-agents (Task tool) for this skill. Call all MCP tools directly in the main conversation using the mcp__knowledge-base__ prefix. Sub-agents do not have reliable access to the knowledge-base MCP server and will waste time exploring the filesystem instead of making tool calls.

Usage

/next-actions [N] [--tag=TAG] [--plan-status=STATUS]

Examples:

# Show the next 5 actionable items (default)
/next-actions

# Show the next 3 actionable items
/next-actions 3

# Show the next 10 actionable items
/next-actions 10

# Show next actions for plans that improve testing
/next-actions --tag=testing

# Show next actions for lego-ui plans only
/next-actions --tag=lego-ui

# Show next actions from in-progress plans
/next-actions --plan-status=in-progress

# Combine: next 3 elaboration items from in-progress plans
/next-actions 3 --tag=elaboration --plan-status=in-progress

What It Does

Queries the KB database (single source of truth) for stories that need action, resolves dependencies at query time, and maps each story's state to the appropriate workflow command.

Inflight work always appears before new work. Stories being actively worked by an agent (active worktree in DB) are excluded entirely.


Parameters

ParameterRequiredDescription
NNoNumber of items to return (default: 5)
--tag=TAGNoFilter to stories linked to plans with this tag (e.g., testing, lego-ui, elaboration, development, agent-tooling)
--plan-status=STATUSNoFilter to stories linked to plans with this status (draft, accepted, stories-created, in-progress, implemented, superseded, archived)

Priority Order (non-negotiable)

RankCategoryStatesRationale
1Needs QAready_for_qa, in_qaClosest to done — unblock the pipeline first
2Needs Fixfailed_qa, failed_code_review, in_reviewSomething was rejected — fix before starting new work
3Needs Code Reviewready_for_reviewDev complete, waiting on review
4Needs Devready, in_progress (no active worktree)Elaborated and ready to implement
5Needs Elaborationbacklog (story file exists)Story seed exists, not yet elaborated
6Needs Story Creationbacklog (no story file)Only a seed — story not yet generated

Within each rank, sort by priority: critical > high > medium > low.


Execution Steps

Step 1 — Get Active Worktrees

Call mcp__knowledge-base__worktree_list_active directly (no sub-agent):

mcp__knowledge-base__worktree_list_active({})
→ extract list of story_ids where status = 'active' → call these ACTIVE_IDS

If the tool is unavailable, set ACTIVE_IDS = [].

Step 2 — Query the KB Database

Call mcp__knowledge-base__kb_list_stories directly. If --tag or --plan-status were provided, pass them as plan_tag and plan_status:

mcp__knowledge-base__kb_list_stories({
  epic: "platform",
  states: ["ready_for_qa", "in_qa", "failed_qa", "failed_code_review", "in_review",
           "ready_for_review", "ready", "in_progress", "backlog",
           "completed", "cancelled", "deferred"],
  limit: N,
  ...(TAG ? { plan_tag: TAG } : {}),
  ...(PLAN_STATUS ? { plan_status: PLAN_STATUS } : {})
})

If the result contains 0 stories, go to Step 6.

Step 3 — Collect N Candidates via mcp__knowledge-base__kb_get_next_story

Call mcp__knowledge-base__kb_get_next_story up to N times directly (no sub-agent), accumulating excluded IDs. Pass plan_tag and plan_status if provided:

# First call — exclude all active worktrees
mcp__knowledge-base__kb_get_next_story({
  epic: "platform", include_backlog: true, exclude_story_ids: ACTIVE_IDS,
  ...(TAG ? { plan_tag: TAG } : {}),
  ...(PLAN_STATUS ? { plan_status: PLAN_STATUS } : {})
})
→ returns STORY_A

# Second call — exclude active + first result
mcp__knowledge-base__kb_get_next_story({
  epic: "platform", include_backlog: true, exclude_story_ids: [...ACTIVE_IDS, "STORY_A"],
  ...(TAG ? { plan_tag: TAG } : {}),
  ...(PLAN_STATUS ? { plan_status: PLAN_STATUS } : {})
})
→ returns STORY_B

# ... repeat until N items or no more candidates

Stop when mcp__knowledge-base__kb_get_next_story returns story: null or you have N items.

Step 4 — Re-sort by Priority Rank

After collecting candidates, re-sort by the rank table above (QA > Fix > Review > Dev > Elaboration > Creation), then by story priority within each rank:

priority order: critical (1) > high (2) > medium (3) > low (4) > null (5)

Important: kb_get_next_story only returns stories in ready or backlog state. Stories in other actionable states (ready_for_qa, failed_qa, failed_code_review, in_review, ready_for_review) must be pulled from the Step 2 kb_list_stories result and added to the candidates list before sorting.

To build the full candidate set:

  1. From the kb_list_stories result, take all stories in inflight states: ready_for_qa, in_qa, failed_qa, failed_code_review, in_review, ready_for_review
  2. From kb_get_next_story calls, collect up to N stories in ready or backlog state
  3. Remove any ACTIVE_IDS from the combined list
  4. Sort the combined list by rank, then by priority within rank
  5. Take the top N

Step 5 — Map State to Command

For each story in the sorted list, determine the command based on its state field:

StateCategoryCommand Template
ready_for_qaNeeds QA/qa-verify-story {FEATURE_DIR} {STORY_ID}
in_qaNeeds QA/qa-verify-story {FEATURE_DIR} {STORY_ID}
in_reviewNeeds Fix/dev-fix-story {FEATURE_DIR} {STORY_ID}
failed_code_reviewNeeds Fix/dev-fix-story {FEATURE_DIR} {STORY_ID}
failed_qaNeeds Fix/dev-fix-story {FEATURE_DIR} {STORY_ID}
ready_for_reviewNeeds Code Review/dev-code-review {FEATURE_DIR} {STORY_ID}
ready or in_progressNeeds Dev/dev-implement-story {FEATURE_DIR} {STORY_ID}
backlog (elaborated)Needs Elaboration/elab-story {FEATURE_DIR} {STORY_ID}
backlog (no story)Needs Story Creation/pm-story generate {FEATURE_DIR} {STORY_ID}

Distinguishing elaborated vs. not: A backlog story is "elaborated" if it has an elaboration artifact in KB. If no elaboration artifact exists, it needs story creation.

Feature directory derivation from story ID prefix:

PrefixFeature Directory
WINTplans/future/platform/wint
KBARplans/future/platform/kb-artifact-migration
AUDTplans/future/platform/code-audit
TELEplans/future/platform/telemetry
INFRplans/future/platform/infrastructure
LNGGplans/future/platform/langgraph-update
MODLplans/future/platform/model-experimentation
LERNplans/future/platform/learning-loop
SDLCplans/future/platform/sdlc-agents
AUTOplans/future/platform/autonomous-dev
WKFLplans/future/platform/workflow-learning

If the story record has a story_dir field, use that to derive the feature directory (strip the story ID segment). If the prefix is not found in the table above, use plans/future/platform as the feature directory.

Step 6 — Empty Result

If Step 2 returned 0 stories, report that the KB has no stories:

No stories found in KB database.

Run `/pm-bootstrap-workflow <plan-slug>` to seed stories from a plan.

Step 7 — Output

Format the results as a table. If filters were applied, show them in the header. Inflight work appears first (per the rank order):

Next actions (N items):
Filters: tag=testing, plan-status=in-progress    ← only if filters were used

| # | Category          | Story                              | Priority | Command |
|---|-------------------|------------------------------------|----------|---------|
| 1 | Needs QA          | WKFL-002 — Story title             | high     | /qa-verify-story plans/future/platform/workflow-learning WKFL-002 |
| 2 | Needs Fix         | WINT-0030 — Story title            | high     | /dev-fix-story plans/future/platform/wint WINT-0030 |
| 3 | Needs Code Review | WKFL-007 — Story title             | medium   | /dev-code-review plans/future/platform/workflow-learning WKFL-007 |
| 4 | Needs Dev         | WKFL-010 — Story title             | medium   | /dev-implement-story plans/future/platform/workflow-learning WKFL-010 |
| 5 | Needs Elaboration | WKFL-008 — Story title             | low      | /elab-story plans/future/platform/workflow-learning WKFL-008 |

If stories were excluded due to active worktrees, note them below the table:

Excluded (active worktree): WKFL-006, WINT-0100

If no unblocked stories exist:

No actionable stories found.

All remaining stories are blocked by unresolved dependencies.
Use `kb_list_stories` to inspect blocked stories.

Edge Cases

ScenarioBehavior
DB returns 0 storiesReport KB empty, suggest bootstrap
All stories blockedReport "no actionable stories" with count of blocked
Story has no feature prefix matchUse plans/future/platform as feature dir
N > available unblocked storiesReturn all available (less than N)
MCP tools unavailableReport error and suggest retrying
mcp__knowledge-base__worktree_list_active failsTreat ACTIVE_IDS as empty, proceed without exclusion
Story in in_progress with no active worktreeTreat as Needs Dev (rank 4)

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

34.55%
按下载量换算54

Claude

31.47%
按下载量换算49

Cursor

17.05%
按下载量换算26

Gemini CLI

9.89%
按下载量换算15

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/michael-menard/monorepo --skill next-actions 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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