sesori-plan-maker

审查 · 67
已收录

Create or update practical, code-informed plans and trackers. Use ONLY when the user explicitly asks to make or change a plan or tracker. It may also self-invoke while planning a new feature, larger refactor, or other large effort that would benefit from multiple steps or PR spli

Verified installs0
Stars105
版本1.0.0
质量67/100 · 有潜力
信任67/100 · 仅限沙盒
审计79/100 · 需审查

供给资产档案

编程与开发 Agent

代码审查、仓库分析、测试、CI、GitHub、DevOps 与开发工作流 Skill。

浏览赛道

场景

编程 Agent

我需要一个能理解仓库、修改代码并审查 Pull Request 的编程 Agent。

适配 Agent

Claude Code + CLI + Codex

适用于 Codex、Claude Code、Cursor、CLI 或自定义 Agent。

安装

就绪

npx skills add sesori-ai/sesori_apps_monorepo --skill sesori-plan-maker

维护状态

新鲜

今天有推送

风险

需审查

Repository license is detected as NOASSERTION, meaning no clear open-source license is identified. This creates ambiguity about usage and redistribution rights.

GitHub 质量

105

67/100 质量 · 75/100 信任

覆盖标签

编程编程 Agent编程 Agentagent-skill

审查说明

Repository license is detected as NOASSERTION, meaning no clear open-source license is identified. This creates ambiguity about usage and redistribution rights. · Quality score needs review

Agent 采用评分卡

一眼查看信任、审计与安装准备度

这些分数综合公开仓库元数据、OpenAgentSkill 审查信号、维护新鲜度与安装准备度。它用于候选筛选,不替代人工审查。

质量

有潜力
67

有用的候选项,但采用前应与替代方案比较。

信任

仅限沙盒
67

有用但信任信号不足或混杂的候选项。在结果闭环证明任务匹配前,请保持在隔离工作区内使用。

审计

需审查
79

对安装准备度、安全元数据、维护情况与采用风险的机器可读审查。

OpenAgentSkill 信任评分 v5

安装前需人工审查

仅在沙盒中运行,并在用于真实工作前比较接近的替代方案。

CodexClaude CodeCursorOpenAgentSkill CLI

Stars

105 个 GitHub Stars

仓库活跃度

105 个 Star,6 个 Fork

维护状态

今天有推送

许可证

NOASSERTION

安装

npx skills add sesori-ai/sesori_apps_monorepo --skill sesori-plan-maker

安装安全性

标准软件包或运行时安装路径

权限范围

filesystem or document access, database access

Agent 结果

暂未有 Agent 结果数据

文档

README/SKILL.md 上下文充分

风险摘要

生产前审查

  • Repository license is detected as NOASSERTION, meaning no clear open-source license is identified. This creates ambiguity about usage and redistribution rights.
  • Quality score needs review
  • Stars/forks activity: 105 stars, 6 forks; issue activity unavailable in current metadata

安装准备度

安装路径可用

  • 安装路径可用
  • 仓库证据可用
  • 已声明许可证
  • 暂无 Agent 验证结果证据

Agent 可读元数据

这个 Skill 的机器可读决策数据。

使用此区块或内嵌 JSON 判断 Agent 是否应安装该 Skill、选择替代方案,或先请求人工审查。

打开 JSON

适用任务

  • 编程 Agent 工作流
  • Claude Code 团队
  • builders willing to evaluate younger projects
  • Inspect source files

适用 Agent

CodexClaude CodeCursorOpenAgentSkill CLICLI

安装决策

命令
npx skills add sesori-ai/sesori_apps_monorepo --skill sesori-plan-maker
策略
审查
人工审查

信任与风险

信任
67/100
审计
79/100
风险级别
需审查

结果闭环

端点
/api/agent/outcome
事件 ID
resolve
结果
5

安装命令

npx skills add sesori-ai/sesori_apps_monorepo --skill sesori-plan-maker

不适用场景

  • 需要厂商支持 SLA 的团队
  • production agents without a repository review
  • Repository license is detected as NOASSERTION, meaning no clear open-source license is identified. This creates ambiguity about usage and redistribution rights.
  • Quality score needs review
  • Stars/forks activity: 105 stars, 6 forks; issue activity unavailable in current metadata

Agent 安全 v2

59/100 · 安装前审查

已审查并附权限说明审查

可用候选,但 Agent 在安装前应展示权限与审计说明。

在真实工作区安装前需要人工批准。

通过 API 解析

网络访问

Skill 可能访问远程页面、API、仓库或外部服务。

文件系统访问

Skill 可能读取或写入项目文件、文档、生成产物或本地工作区状态。

数据库访问

Skill 可能检查 Schema、查询数据库或处理持久化存储。

  • Repository license is detected as NOASSERTION, meaning no clear open-source license is identified. This creates ambiguity about usage and redistribution rights.

安装目标

在你的 Agent 工作流中安装此 Skill

通过公开安装端点获取命令、安全清单、目标提示词和该 Skill 的规范链接。

skill install

OpenAgentSkill CLI

Resolve policy, run the source installer safely, and report a verified install receipt.

$ npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install sesori-ai-sesori-plan-maker

Agent 解析计划

让 Agent 在安装前验证匹配度。

Resolve API 返回首选 Skill、替代方案、安全策略、审计说明、安装目标和可直接执行的提示词,无需抓取此页面。

打开文本计划

Agent 应检查

  • 从 Resolve API 检查任务匹配与替代方案。
  • 检查审计评分、信任评分和安全策略警告。
  • 检查 Codex、Claude Code、Cursor 或 CLI 的安装目标兼容性。

复制提示词

Task: Use sesori-plan-maker in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20sesori-plan-maker%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/sesori-ai-sesori-plan-maker/install
Install command: npx skills add sesori-ai/sesori_apps_monorepo --skill sesori-plan-maker
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.

Agent 交接

把安装路径交给 Agent,而不是再给一个目录页。

通过公开安装端点获取命令、安全清单、目标提示词和该 Skill 的规范链接。

打开安装 API

Agent 提示词

Use sesori-plan-maker for this task. Review https://www.openagentskill.com/api/skills/sesori-ai-sesori-plan-maker/install, then install with: npx skills add sesori-ai/sesori_apps_monorepo --skill sesori-plan-maker

Registry 元数据

用于自动选择 Skill 的 Agent 可读档案。

本页通过 Registry API 提供相同的决策、信任、审计、场景和安装信号,让 Agent 无需抓取界面即可排序。

打开 Manifest

适配 Agent

67/100

编程 Agent

平台

Claude Code

审计报告

需审查 · 79/100

对安装准备度、安全元数据、维护情况与采用风险的机器可读审查。

查看审计报告查看评估报告

Agent 决策面板

Fallback candidate for Coding agents

先用此 Skill 做原型验证,并保留备选方案。

67
就绪度
原型验证
阶段

栈中角色

备选候选

主要匹配

编程 Agent

信任标签

先做原型验证

安装路径

命令已就绪

适用场景

  • 编程 Agent 工作流
  • Claude Code 团队
  • builders willing to evaluate younger projects

证据

  • 仓库近期活跃
  • 已提供安装命令或 GitHub 仓库
  • 67/100 质量档案
  • 3 个 OpenAgentSkill 交互事件

先审查

  • Repository license is detected as NOASSERTION, meaning no clear open-source license is identified. This creates ambiguity about usage and redistribution rights.

实施路径

  1. 1在沙盒 Agent 中安装它,并端到端完成一次编程 Agent任务。
  2. 2Compare output quality, latency, and failure behavior against at least one alternative.
  3. 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.

信任档案

仅限沙盒

有用但信任信号不足或混杂的候选项。在结果闭环证明任务匹配前,请保持在隔离工作区内使用。

67
OpenAgentSkill 信任评分

GitHub 采用度

信息

105 个 GitHub Stars

Star/Fork 活跃度

检查

105 个 Star,6 个 Fork; 当前元数据中没有议题活跃度信息

近期维护

通过

今天有推送

许可证清晰度

通过

NOASSERTION

积极信号

  • AI 审查已通过
  • 安装路径可用
  • 仓库证据可用
  • 近期维护的仓库
  • 安装命令未发现明显高风险模式
  • 结果闭环已就绪,但需要首次真实 Agent 运行

安装前审查

  • Repository license is detected as NOASSERTION, meaning no clear open-source license is identified. This creates ambiguity about usage and redistribution rights.
  • Quality score needs review
  • Stars/forks activity: 105 stars, 6 forks; issue activity unavailable in current metadata
  • 暂未有真实 Agent 结果报告
  • 无人值守安装前需要人工审查

建议操作

仅在沙盒中运行,并在用于真实工作前比较接近的替代方案。

质量档案

有潜力 适用于 Agent 工作流的候选

有用的候选项,但采用前应与替代方案比较。

67
GitHub Stars
105
新鲜度
今天
安装就绪
许可证
NOASSERTION
安装前审查: Repository license is detected as NOASSERTION, meaning no clear open-source license is identified. This creates ambiguity about usage and redistribution rights.

工作流匹配

在这些场景使用此 Skill

工作流匹配

加入完整工作流

替代方案短名单

安装前对比

可能适合该任务的相近 Skill。

对比全部

概览

--- name: sesori-plan-maker description: Create or update practical, code-informed plans and trackers. Use ONLY when the user explicitly asks to make or change a plan or tracker. It may also self-invoke while planning a new feature, larger refactor, or other large effort that would benefit from multiple steps or PR splits. Do not self-invoke for routine implementation, small fixes, or ordinary single-step work. ---

# Plan Maker

When this skill is loaded, turn a user's goal into a practical implementation plan grounded in the current codebase. Keep the process proportional to the work. Prefer a short useful plan over a large planning system.

## User Direction

The user has final authority. Do not reject a request merely because it is not planning work or is outside this skill's usual duty.

If a request is clearly outside planning and the user has not already acknowledged that, say so briefly and ask once whether they want you to proceed. If they confirm, or if they already explicitly told you to proceed despite the planning context, do the work without questioning the choice again. This includes implementation, tests, configuration, Git tasks, and plan updates when permitted by the active environment.

Follow the user's latest explicit instruction when it conflicts with an older plan or process preference. Explain concrete risks when useful, but do not use the role, a plan, or a reviewer as a reason to overrule a confirmed decision.

## Planning

- Inspect relevant repository instructions, code, tests, history, and external references before making assumptions. - Ask only questions that materially affect the result and cannot be answered from available context. Avoid exhaustive interviews and arbitrary checklists. - Make scope, current behavior, proposed changes, ownership/data flow, important compatibility concerns, and verification concrete enough to implement. - Scale detail to the task. A small change may need only a concise plan in chat; a multi-step effort may benefit from durable files under `.plan/active/<slug>/`. - When updating an existing plan, preserve its useful structure rather than forcing a new schema. Keep its tracker or execution state in sync when needed. - Do not invent stages, waves, PR boundaries, worktrees, or process artifacts unless they help the current work or the user asks for them. - When intentionally splitting any task across multiple PRs, require every PR title to use `<emoji> [<slug>] <description> [step <x>/<y>]`. For durable planned work, `<slug>` is exactly the plan directory name under `.plan`; do not invent a separate series slug. Without a durable plan, choose one stable, lowercase kebab-case slug. Fix the step order/total for the whole series, including each step's complexity emoji, and do not apply the slug/step wrapper to a single-PR task. - Target no more than 1,500 changed lines per PR as a soft cap, counting additions plus deletions, generated code, and tests. Prefer a coherent split before exceeding it; when a smaller independently valid PR is not practical, record the reason for the expected overage in the plan. - For durable planned work, the first PR step always raises the plan under `.plan/active/<slug>/` before implementation begins. The penultimate step reconciles and completes the affected feature documents under `docs/regression/`. The final step runs the level and matrix already recorded in `PLAN.md`, records the result, and retires the plan by moving it to `.plan/completed/<slug>/` only after that coverage passes. Include all three lifecycle steps in the fixed step total.

For a new durable plan, `PLAN.md` should normally capture the goal, scope, relevant current behavior, concrete implementation steps, verification, and material risks or decisions. Add a lightweight `TRACKER.md` or step files only when they will help execution.

The plan must identify affected regression feature documents, the highest coverage level needed for the delivered behavior, and any required plugin, platform, client, packaged, or external-service matrix. Follow the proof-boundary and retirement rules in `docs/regression/README.md`; choose enough coverage to prove every materially delivered behavior through its complete authoritative boundary, never a lower level merely because it is cheaper. Any reduction to the recorded matrix requires explicit user acceptance in `PLAN.md` before retirement.

## Evidence And Proportionality

### Prefer Elegant, Low-State Designs

- Before adding persistence or coordination, inspect existing fields, event shapes, and relevant Git history. Reuse a semantically adequate signal and narrow the product claim when needed rather than duplicating state solely to manufacture perfect provenance for a low-impact heuristic. - Treat every new mutable field, map, queue, registry, timer, subscription, dedupe set, pending state, and lifecycle hook as a new failure point with an ongoing maintenance cost. Count mutable parts explicitly before accepting a design, not only changed lines or PR size. - First find the narrowest existing owner that already knows the authoritative outcome. Prefer one post-success write at that seam over reconstructing intent later from events, payload shapes, timing, or backend-specific classifiers. - A backend-neutral behavior should not require custom production logic in each plugin unless the behavior genuinely depends on backend semantics. If a plan touches every plugin to infer the same product fact, treat that as a design alarm: look for a bridge-core action or normalized contract that already owns the fact, or narrow the promised behavior. - Prefer an honest product limitation over machinery that guesses unobservable provenance. Supporting fewer authoritative flows cleanly is better than claiming broad support through dedupe caches, correlation state, reconnect reconciliation, and plugin-specific heuristics. - Before finalizing a plan, include a complexity budget: name the new persistent and in-memory mutable parts, justify each one, and state which tempting pieces are deliberately not being added. If the feature's coordination machinery is larger than its primary behavior, redesign or ask the user before proceeding. - When review feedback adds mutable coordination one edge case at a time, stop and reconsider the root seam instead of accumulating guards. Do not let a sequence of locally valid findings turn a simple behavior change into a state machine without explicit user approval.

- Classify each planned safeguard as addressing an observed failure, an ordinary reachable user flow, or a theoretical interleaving. A reviewer suggestion or a test that can synthetically force a race is not by itself product evidence. - Before adding coordination, state the concrete flow, user/data consequence, and what happens if nothing changes. Account for existing ordering, retries, recovery, idempotency, and refresh behavior instead of assuming every transient state must be made impossible. - Require observed evidence or a plausible ordinary flow with meaningful impact before adding locks, lanes, registries, provisional states, lifecycle owners, compatibility paths, or exhaustive cross-repository filtering. Explicitly accept bounded transient or self-healing behavior when its impact is minor. - Prefer the coarsest simple mechanism that preserves the required invariant. Do not add per-resource concurrency, parallelism, or bypass closure when a small serialized domain boundary is sufficient and throughput is unproven. - Treat cross-cutting coordination as a scope alarm. If an unobserved safeguard grows into shared state across several owners/layers, materially exceeds its estimate, or becomes comparable in size to the primary feature, stop and ask the user whether that risk justifies the complexity before planning or applying more fixes. - Re-run this proportionality check when architecture review or PR feedback expands scope. Apply findings that protect the approved core behavior, but do not treat architectural completeness as a reason to implement increasingly defensive machinery around a low-impact theoretical edge. - For durable plans, record both the evidence level and any intentionally accepted risk. This keeps later reviewers from reopening a declined theoretical concern without new evidence.

## PR Complexity and Communication

Assign every planned or opened PR one implementation-complexity level represented by its fixed emoji:

- `🌱` — trivial: isolated documentation, copy, or mechanical work; - `🌿` — straightforward: localized implementation with a small blast radius; - `⚙️` — moderate: several files or layers, meaningful state, or notable edge cases; - `🚧` — complex: cross-layer flow, persistence, concurrency, lifecycle, compatibility, or security-sensitive behavior; and - `🚨` — very complex: several coupled high-complexity concerns or a broad, high-stakes migration.

Complexity describes implementation and review difficulty, not risk by itself. Choose it from the actual coupling, state transitions, migration/codegen, concurrency, compatibility, privacy/security, and verification burden; do not rate every PR in a series identically by default.

For a single-PR task, prefix the normal title with `<emoji>`. For a multi-PR task, place the emoji first: `<emoji> [<slug>] <description> [step <x>/<y>]`. Treat the emoji as part of the fixed exact title. If implementation evidence changes the estimate before the PR opens, update the plan/tracker title rather than knowingly publishing a stale rating.

Make every planned PR concrete enough that its eventual PR body can briefly and clearly state:

- **Complexity:** level plus a one-sentence rationale; - **What:** what the PR changes; - **Why:** why that change is needed now; - **Risk and test focus:** risk level, potentially impacted flows, screens, data, integrations, or functionality, and the highest-value checks; and - **Expected result:** what a reviewer should observe after running it, explicitly covering user-visible behavior, persisted/database changes, and pure internal/refactor effects as applicable.

Use an explicit `None` or `No user-visible/database change` rather than omitting a category. Keep these summaries proportional; they are an operational review aid, not a duplicate design document.

Whenever you create or materially update a PR yourself, render those categories as `## Complexity`, `## What`, `## Why`, `## Risk and test focus`, and `## Expected result`, followed by the relevant verification section. Use real multiline Markdown through `--body-file` or stdin.

## Cleanup Assessment

For every feature plan, actively inspect what the new behavior makes obsolete. Consider calculations and data generation, model fields, database columns, transport fields, caches, flags/settings, jobs/watchers/listeners, compatibility paths, UI state, tests, and documentation. Look for causal cleanup such as data that no longer needs to be generated, persisted, transported, or rendered.

Record one honest outcome in the plan:

- include small, safe, directly caused cleanup in the appropriate feature PR; - place a larger but valuable cleanup in its own coherent planned PR; - defer cleanup when migration, compatibility, rollout, or risk requires it and state the reason; or - state that no relevant cleanup was found.

Do not keep obsolete artifacts solely for auditing when Git history already preserves them. Cleanup is still not permission for speculative scope growth: preserve required wire/data compatibility, and explain approximate size and ask the user before planning a considerable refactor.

## Plan Review

Use `architecture-plan-review` only for architecture-bearing production plans, as defined by repository instructions. Ask a sub-agent to perform the review using the skill. Apply valid findings directly and do

技术详情

版本
1.0.0
许可证
NOASSERTION
最近更新
2026年8月23日
发布时间
2026年8月23日

决策摘要

备选候选

67
就绪
原型验证
阶段

仓库近期活跃

审计

安装审查

安装与采用审查

79
需审查
安全性
80/100
维护状态
100/100
安装
92/100
打开完整审计查看评估报告

Agent 验证证据

Agent 验证证据

来自解析、审查、安装和一次小范围运行后的结果报告。

0
已验证
Needs first agent run自动安装: 先审查最近: 未知
成功率
近期失败
结果
0
输出质量
失败
0
不相关
0
安装次数
0
风险拦截
0
需要配置
0
生产环境
0

暂时没有 Agent 结果数据。首次 Agent 执行可以通过 /api/agent/outcome 报告成功、需要设置、风险拦截、失败或不相关。

安装

加入 Agent 工作流

免费且开源. 在生产 Agent 中安装前请先审查报告。

增长闭环

分享工具包

X

为 sesori-plan-maker 准备的场景化草稿,可手动发布到 X。

策展说明
sesori-plan-maker: Create or update practical, code-informed plans and trackers. Use ONLY when the user explicit...

105 stars

https://www.openagentskill.com/skills/sesori-ai-sesori-plan-maker?ref=x
打开 X 草稿
可选:带安装命令的回复
Listing + install path for sesori-plan-maker:
https://www.openagentskill.com/skills/sesori-ai-sesori-plan-maker?ref=x

Install: npx skills add sesori-ai/sesori_apps_monorepo --skill sesori-plan-maker
打开回复草稿

收录来源

Registry 收录

可认领

此列表来自公开来源,维护者认领获批前不会标记为官方。

创作者
sesori-ai
收录方
OpenAgentSkill 社区索引

归属链接指向公开仓库或创作者主页。创作者可认领列表以更新所有权信号。

认领此 Skill

所有者认领

认领此 Skill 页面

这条 Registry 收录 列表归属于 sesori-ai,但尚未标记为官方。认领后可增加已验证所有者信号,使后续发布、安装和审计更新更值得信赖。

创作者外链工具包

将证据徽章加入你的 README

在开发者评估仓库的位置展示规范页面、当前信任与审计信号,以及真实的 Agent 验证证据。

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/sesori-ai-sesori-plan-maker?metric=listed&label=Listed)](https://www.openagentskill.com/skills/sesori-ai-sesori-plan-maker)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/sesori-ai-sesori-plan-maker?metric=trust&label=Trust)](https://www.openagentskill.com/skills/sesori-ai-sesori-plan-maker)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/sesori-ai-sesori-plan-maker?metric=audit&label=Audit)](https://www.openagentskill.com/skills/sesori-ai-sesori-plan-maker/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/sesori-ai-sesori-plan-maker?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/sesori-ai-sesori-plan-maker)

作者

S

sesori-ai

@sesori-ai

平台适配

健康信号

GitHub Stars
105
质量评分
37/100
最近 GitHub 推送
2026年8月23日
框架提示
未知
OpenAgentSkill 浏览量
3
复制安装命令
0
跳转点击
0

社区信号

告诉我们这个 Skill 是否对你的 Agent 工作流有帮助。汇总反馈会持续改善排序。

信任与安全

仅限沙盒

67
  • GitHub 采用度105 个 GitHub Stars信息
  • Star/Fork 活跃度105 个 Star,6 个 Fork; 当前元数据中没有议题活跃度信息检查
  • 近期维护今天有推送通过
  • 许可证清晰度NOASSERTION通过
  • README/SKILL.md 完整度元数据包含足够的用法与工作流上下文通过
  • 依赖与运行时风险database surface通过