Agent skill

Planning

by Towow-ai in Towow-ai/Flowness

M-1.3 planner skill — 把 frozen consensus 翻译成 TaskNode 图 + 依赖边 + 资源 claim。

Apache-2.0Auto-check passedAgent Workflows

Install Planning

skills CLI
$ npx skills add Towow-ai/Flowness --skill planning -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Towow-ai/Flowness planning --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Towow-ai/Flowness.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/planning .claude/skills/planning && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
planning
GitHub stars
107
Token cost
~2.3k tokens
SKILL.md length
787 words
Files
9
Skills in repo
12
Repo updated
First seen
Licence
Apache-2.0

At a glance

M-1.3 planner skill — 把 frozen consensus 翻译成 TaskNode 图 + 依赖边 + 资源 claim。

  • Works in 3 steps: 建图(分解 + 依赖) → 调度(关键路径 + 资源) → 打包 + 冻结(计划的终态)
  • Agent Workflows work in your project
  • SKILL.md covers 我是谁, 我了解的判断世界, 一个"execution… and 我做什么(M-1.3 §12 三 Phase — 缺任一…, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Planning is an agent skill from Towow-ai/Flowness. M-1.3 planner skill — 把 frozen consensus 翻译成 TaskNode 图 + 依赖边 + 资源 claim。

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files (for example `knowledge/cross-plan-coordination-policy.md`, `knowledge/decomposition-policy.md` and `knowledge/dependency-policy.md`).

It sits in Agent Workflows. The repository describes itself as: A work-centered runtime for agentic software engineering. Work persists; agents, context, and graphs assemble around it. The licence is Apache-2.0.

When your agent uses it

  • Agent Workflows work in your project

Example prompts

  • “/planning”

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. 建图(分解 + 依赖)
  2. 调度(关键路径 + 资源)
  3. 打包 + 冻结(计划的终态)

What it can do on your machine

Read from SKILL.md and the folder at commit c9d6abe. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Planning loads about 2.3k tokens when it runs. Until then it costs about 21 tokens; SKILL.md has 787 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~21
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Towow-ai/Flowness at commit c9d6abe, republished under its Apache-2.0 licence (© Towow-ai). 787 words, ~2,250 tokens.

Download SKILL.mdSave it as .claude/skills/planning/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
planning
description
M-1.3 planner skill — 把 frozen consensus 翻译成 TaskNode 图 + 依赖边 + 资源 claim。
capsule_scene_types
planning
shared_knowledge_required
decomposition-policy.md, dependency-policy.md, task-taxonomy.md, task-package-policy.md, parallelization-policy.md, cross-plan-coordination-policy.md…

Planning Skill (M-1.3)

我是谁

我是 planner —— 把 frozen engineering consensus 拆分成可执行 task 图。每个 task 必须 self-contained(零上下文 AI 也能跑)。

我的成功标准不是"看着像计划"——是产出后续 execution fork 能直接拿来跑。

frontmatter 里那 8 份 shared knowledge:若你的装载通道没有 capsule 注入(主会话用 Skill 工具装载即是),它们就在本 skill 目录 knowledge/ 下,按需读。

我了解的判断世界

我把 frozen 共识翻译成任务图,判断的核心是四把尺:

  • task 是产出物,不是动作——"用户能通过 API 创建 batch",不是"写 createBatch 函数"。后者剥夺执行者的实现判断权。
  • 依赖来自证据、固化成边、不留散文——依赖写进 task 描述 = 无 DAG = 无法识别并行组。每条依赖必须 dep-add 成 TaskDependencyEdgeAdded 边。
  • 零上下文自包含是硬尺——一个零上下文 execution fork 拿到 package 能不回头问任何人就跑完吗?不能 = 还没拆到位 / package 不够自包含。
  • freeze 是终态,不是 package-publish——停在发包没冻结 = 计划没做完(GOAL 最爱在这里合法地停)。

一个"execution 能直接跑"的计划长什么样(关键——认住它)

completion_condition:「用户能创建并查询 batch」。

✗ 看着像计划、execution 跑不起来:

建了 task A/B/C、发了 package,依赖写在描述里"B 要在 A 之后做",停在 package-publish。

execution fork 拿 B 去跑:依赖只在散文里 → orchestrator 看不到 DAG → 不知道 B 等 A,并行炸;package 里"参考 A 的输出" → fork 找不到 A 的输出(不自包含)回头问;而且没 freeze、整个计划没过 freeze 的 blocking_check 门,下游不该启动。看着齐全,跑不起来。

✓ execution 能直接跑:

task A/B/C 各零上下文自包含 package;B 依赖 A 经 dep-add 成边(orchestrator 一看 DAG 就知道 A→B 串行、C 可并行);critical-path 已 emit;freeze 跑完全部 blocking_check → PlanFreezed。execution fork 拿任一 ready task 直接跑、不回头问、不撞车。

区别不在"建没建 task、发没发包"(✗ 也建了发了)——在依赖固化成边了吗(能不能并行)、package 零上下文自包含吗(fork 要不要回头问)、freeze 了吗(到没到终态)。

我做什么(M-1.3 §12 三 Phase — 缺任一 Phase = 计划没做完,不是可选)

我读 capsule + frozen ConceptGraph + brief.goal.completion_condition,然后走完整三阶段。 停在 TaskPackagePublished 不算做完——必须走到 PlanFreezed。计划的终态是 freeze,不是发包。

Phase 1 建图(分解 + 依赖)
  1. 读 brief.completion_condition 识别顶层交付物;调 plan-decompose fork 从 completion_condition 反推、递归拆到 primitive task(垂直切片 / 零上下文自包含)
    • 顺手核一眼上游 goal 的真实处理断言:completion_condition 若涉及在真实生产对象上产生副作用 (工位处置 / 数据迁移 / 清单冻结并执行 / 任何"账本上得留一条真事件才算真做"),那么上游 brief 应带 live_target_observables(goal 收口门据它复算账本、拒零证据的假完成)。发现该带却没带 → 这不是我 planner 能补的字段(它在 brief 层),登一条 spec gap 让采访侧补发 / amend,别静默拆成 一堆 task 就冻结——否则执行完、goal 收口门对本 goal 空放行,假完成又溜过去。(plan-freeze 侧的 "强制声明"兜底 debt-b6187ed0d19c 还没落;在它落之前靠这一眼自然发现。)
  2. 每个 task plan task-create(单一 task_type / target_artifacts 明确 write_set 边界 / 自包含描述无隐含 caller context)+ plan read-claim / plan write-claim 显式 claim read/write set
  3. 【强制·并行的地基】调 dependency-analyze fork 从 read/write set + state_machine 推依赖, 每条依赖必须 plan dep-add 固化成 TaskDependencyEdgeAdded 边——不许只写进 task 描述散文。 散文依赖 = 无 DAG = 无法识别并行组 = 无法并行。停止检查:所有 task 拆完后,dependency-analyze proposed 的边全部经 plan dep-add 落账,没有一条只活在描述里
  4. 调 plan-consistency-verify fork:覆盖完整 + 无循环依赖 + 无假依赖
Phase 2 调度(关键路径 + 资源)
  1. 调 critical-path-schedule fork + plan model-tier 给每个 task 分 opus/sonnet(TaskModelTierAssigned)
  2. 【强制】plan critical-path —— 真 emit CriticalPathIdentified(从 dep 图算最长链,不是手填)。 跳过这步,Phase 3 的 plan freeze 会被 fail-closed 门(check_critical_path_identified)挡死
  3. 【强制】调 cross-plan-check fork 查其他活跃 plan 的冲突——无条件跑,不是"觉得有冲突才跑" ("如有"式自由裁量的历史结局:绝大多数冻结从没跑过它,被调的每次都抓出真问题)。 冲突用 cross-plan-coordination-policy 处理;结论包括 none 也要留痕(写进 escalation / debt / freeze 前的会话产出),别让它只活在 tool_result 里——fork 结果会随会话死亡蒸发, 后继棒零感知(判例:会话 1A0AFEB6 交棒蒸发链)
Phase 3 打包 + 冻结(计划的终态)
  1. 调 task-package-assemble 为每个 primitive task 组装零上下文自包含 package → plan package-publish (TaskPackagePublished)
  2. 【强制·终态】plan freeze —— freeze 前必须持有本 plan 的 consistency_result(plan-consistency-verify 的产出);没有 → 先调它。物理门拦形式硬伤,pcv 拦的是机器门够不着的语义层(escalation 语义 / 边方向 / 派发框架纠错),"机器门反正会跑"不构成跳过它的理由——历史上被调的每一次都是"检了才敢冻"。 然后跑全部 blocking_check(几道、哪几道以 run_all_blocking_checks / freeze 运行时输出为准——门随代码增长,别信任何写死的数字),全过才 emit PlanFreezed。分批:无依赖的 task 先 freeze 先跑,前置完成后再 freeze 依赖它的那批(progressive)。freeze 成功 = 计划真做完; 停在 package-publish 没 freeze = 计划没做完。

非首次规划场景(先认路——不是快乐路径,但都真实发生过)

上面的三 Phase 合同假设"新鲜冻结→拆→冻"。下面四类场景各有既定出口,撞上时按判例走,别静默即兴发明:

  • 开工第一步:查同 plan_id 的活体兄弟。 ./tw vitality 看是否已有别的会话在拆同一个 plan (调度器会重复派发,debt-d3e8834466a4)。有活体且它更完整 → 主动让位、登 debt 收口, 别拆到一半撞车(判例:会话 aca008f1)。
  • 零任务 / policy-only:不冻空计划。 若共识落地后确认没有可拆的 execution task(纯 policy / 概念变更),诚实出口不是硬凑 task 或冻空计划——是登 spec gap debt + 以 reason=completion 带完整推理收口(判例:会话 223b4913,debt-5e081e2f0f6b)。"缺任一 Phase = 没做完"只对 有任务可拆的计划成立。
  • 接手 replan:先对账 DAG 再动手。 前任 planner 可能留幽灵边(旧边没撤、新节点没接进来)。 逐条核现存边 vs 当前共识;撤边/改边带审计证据链(引 replan 事件、说明该边为何失效), 不是静默删(判例:会话 6b84913e,手工撤 BATCH-EXECUTE→REVIEW 幽灵边)。
  • 跨 plan 依赖加不上(dep-add 不许跨 plan):已知边界,走 cross-plan-coordination-policy 的既定出口,别硬绕。
  • 交棒时工序骨架列全。 交棒信里的 M-1.3 工序必须含全部六个 fork 工位——漏写哪个,后继棒 就不知道那道工序存在(判例:一封交棒信的骨架漏了 cross-plan-check,前棒派出的 fork 结果 随会话死亡蒸发,后继棒零感知、冻结照过)。已派出未收回的 fork,在交棒信里显式交代。
Show full SKILL.md (352 more words)Show less

我调度的 fork(M-1.3 §13.1-§13.6 — fork analyzes/proposes,主 session asks/commits/decides)

我不亲自做每一步判断——我把判断分发给 6 个专才 fork,拿它们的 decision + confidence + evidence, 我(主 session)决定接受 / 调整 / 拒绝并 commit。这 6 个 fork 是真实部署的 sibling skill (.claude/skills/<name>/SKILL.md,capsule_scene_types=[planning],capsule scene 可调),不是占位:

何时调fork(skill_id)它产什么
Phase 1 建图——从 completion_condition 反推交付物plan-decompose (§13.1)decomposition_candidates + coverage_matrix + gaps
Phase 1 建图——从 read/write set + state_machine 推依赖dependency-analyze (§13.2)edges_proposed(6 类,每条带 evidence)+ conflict_groups
Phase 2 调度——关键路径 + model_tier + 并行组critical-path-schedule (§13.3)critical_path + parallel_groups + model_tier_assignments
Phase 2 调度——查其他活跃 plan 的 6 类冲突cross-plan-check (§13.4)detected_conflicts + recommended_escalations
Phase 3 打包——组装零上下文自包含 packagetask-package-assemble (§13.5,主 session 内工具)assembled_packages + publication_blockers
Phase 3 冻结前——最后一道整体闭合门plan-consistency-verify (§13.6)consistency_result(检查清单以其 skill 文本为准,别抄数字进派发)+ blocking_issues + ready_to_freeze

通道(怎么调):用 Skill 工具按名调用(forked execution;bg 通道由 capsule scene 注入; task-package-assemble 本就是主 session 内工具)。这些 fork 没有同名 subagent_type——用 Agent 工具会报 "Agent type not found";用 general-purpose 冒名、口头转述 fork 的角色 = 它的 合同文本根本不在场,那不是调用,是自问自答。CLI 自己会算的东西(如 plan critical-path emit 时自算最长链/makespan)≠ fork 冗余:fork 的增量在落账前的独立判断——敏感度分析、tier 逐项 评分、纠你自己的前提错。CLI 算过,fork 照调。

给什么(不预填答案):派 fork 时只给输入——brief、冻结共识、read/write set、已建 task 现状、你的疑点。不给预期答案:预填任务清单("预期方向 T1…T9")、预填边集("T4 依赖 T1-T3")、预派 model_tier / opus-factor,全算买通裁判——fork 会顺着锚定走,独立 analyzes/ proposes 的意义就没了(预派 tier 的实战笑话:连词表里不存在的 "haiku" 都写得出来——tier 评分是 fork 的活,主 planner 预派即越权)。你若确有预判,逐条标「待复核」当疑点交出, 让 fork 取证——它敢驳回你,才是它的价值。

怎么收结果:fork 给"我建议这样 + 依据 + 置信度",不是"我已经做好了"。收到先核完整性—— fork 外壳可能把连接中断包装成 completed(结果里出现 "Connection closed mid-response" = 半截提案,重派,别当完整采纳)。主 session 决定接受/调整/拒绝后才走 CLI emit(task-create / dep-add / model-tier / critical-path / package-publish / freeze),并把 fork 提案的要点随 emit 落账留痕(fork 无 session_id 无权 emit,留痕义务在你——别让判断只活在 tool_result 里)。 真正的 fail-closed 物理门在 commit gate + plan freeze 的全部 blocking_check(真相源是 plan_freezed.py 的 run_all_blocking_checks,以运行时输出为准)。

我不做

  • ❌ 不把 task 写成"看着合理"的抽象 — 必须可机械操作
  • ❌ 不用 task 描述代替 concept — task = doing, concept = being
  • ❌ 不漏 dependency — 漏 dependency 等于让 execution fork 自己猜顺序
  • ❌ 不假设资源不冲突 — claim 必须显式(TaskReadSetClaimed / WriteSetClaimed)
  • ❌ 不对新增能力 task 用 grep/file_exists 型 machine_check — 被 @new-capability-task-classifier@v1 判为新增能力(write_set 命中能力性路径、或引新事件类型、或误标 task_type 但命中这两个信号)的 task,其 done_criterion 的 machine_check 必须 test 型且 test_selector 读真账本(EventLog.all_records() 断目标事件存在 + provenance 非交互);grep 型扫不到账本里的 live 事件签名,在这里永远是假做完。

失败模式

  1. task too large — 一个 task 包含 multiple concepts/actions
  2. dependency 漏 capture — 执行时跑乱
  3. package 不 self-contained — execution fork 跑时找不到 context
  4. silent resource conflict — 没 claim → 多 task 并行写同一 artifact

自检

把我的计划交给一个零上下文 execution fork:它能拿任一 ready task 直接跑完、不回头问任何人、不跟兄弟 task 撞车吗?依赖全固化成边(不是散文)吗?走到 PlanFreezed 了(不是停在发包)吗?都 yes → 够;任一 no → 那处没拆到位 / 没固化成边 / 没冻结。

【新增能力 live-fire 自检(INV-A)】 对本次计划里每一个被 @new-capability-task-classifier@v1 三信号判别为新增能力的 task,逐条核:其 done_criteria 里是否至少有一条 machine_check 满足 verification_method=test 且 test_selector 指向的集成测试读 EventLog.all_records() 断目标事件 live 签名?任一新增能力 task 没有这样的 machine_check → 补上,不是降级用 grep 凑数;plan-freeze 的 check_new_capability_tasks_have_livefire_machine_check 门会在冻结时机械拒这种包,提前自检比被门拦回要省。

产 events

  • TaskNodeCreated
  • TaskDependencyEdgeAdded —— Phase 1,依赖必固化成边(不留散文)
  • TaskReadSetClaimed / TaskWriteSetClaimed
  • TaskModelTierAssigned
  • CriticalPathIdentified —— Phase 2,关键路径(freeze 的 fail-closed 前置)
  • TaskPackagePublished
  • PlanFreezed —— Phase 3,计划的终态产物

© Towow-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 8 other files in .claude/skills/planning of Towow-ai/Flowness.

  • SKILL.md
  • knowledge/cross-plan-coordination-policy.md
  • knowledge/decomposition-policy.md
  • knowledge/dependency-policy.md
  • knowledge/model-tier-policy.md
  • knowledge/parallelization-policy.md
  • knowledge/planner-casebook.md
  • knowledge/task-package-policy.md
  • knowledge/task-taxonomy.md

Open the folder on GitHubat commit c9d6abe

Compare with similar skills

Planning next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Planning compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Planning this skillTowow-ai/Flowness107—~2.3kAutomated safety check: PassApache-2.0
MCP Server Builderanthropics/skills180k63 repos~2.3kAutomated safety check: PassApache-2.0
Hook Development for Claude Code Pluginsanthropics/claude-plugins-official38k10 repos~4.1kAutomated safety check: NotesApache-2.0
Using Superpowersfarm-fe/farm5.6k35 repos~1.4kAutomated safety check: PassMIT
Executing Plans Inlineobra/superpowers297k2 repos~5.1kAutomated safety check: PassMIT
Skill CreatorAzure/azqr79689 repos~8.2kAutomated safety check: PassApache-2.0

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    38k GitHub starsUsed in 7 repos~2.8k tokens
    Agent WorkflowsAuto-check passed

More from Towow-ai/Flowness

All 12 skills in this repo
  • Dependency Analyze

    Towow-ai/Flowness

    从 task 的 read/write set + concept statemachine 推导 6 种依赖类型的提案。主 planner 决定边的真实性。派它时只给 read/write set 与疑点、不给预期边集;已有预判逐条标「待复核」交它取证。

    107 GitHub stars~1.6k tokensUpdated 2 mo ago
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  • Downtime Recovery

    Towow-ai/Flowness

    停机后复工的标准安全流程(水位线追平/积压泄流/服务分批重启)。当系统经历过 daemon 停机、性能冲刺减负、事故停摆之后要恢复常驻服务时触发;即使 owner 只说"把服务开回来"、"复工"、"追平水位线",也应触发。核心使命:绝不让"重启"变成"积压喷发"(2026-07-04 实锤:orchestrator 停机后水位线落后 3240 条,直接重启把机器负载打到 22+,owner…

    107 GitHub stars~1.1k tokensUpdated 2 mo ago
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  • Execution

    Towow-ai/Flowness

    M-1.4 execution skill — 跑 single task 产 patch + 提交 envelope。

    107 GitHub stars~2.5k tokensUpdated 2 mo ago
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  • Execution Self Check

    Towow-ai/Flowness

    Pre-submit 自检——envelope 提交 commit gate 前必跑。独立 OPUS fork 逐项判 blocking checks(清单以 dispatch prompt 注入为准),executor 不能 self-assess(运动员不当裁判)。

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  • Fix

    Towow-ai/Flowness

    修复者 — 把一条被发现的问题(finding)按它的闭合合约修干净,修一个不制造下一个。产 FixProposed + 临时的 FixCompleted,不自判问题关闭(那是复查的权)。当 daemon 派一条 finding 来修、或需要闭合一个已发现的问题时用,即使只说"修一下这个 finding""把这个问题闭合"也触发。调用名就是 fix(Skill 工具)或 /fix(命令),没有…

    107 GitHub stars~1.3k tokensUpdated 2 mo ago
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  • Fix Self Check

    Towow-ai/Flowness

    M-1.6 envelope self-check——独立性保证不自欺欺人 (5 blockingcheck)。由 CLI ./tw fix complete --self-check-mode fork(默认即 fork)自动派起,不经 Skill 工具调用;fix 主会话产 FixCompleted 前直读本文,是为理解双层验证关系。

    107 GitHub stars~2.2k tokensUpdated 2 mo ago
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Categories

Questions about Planning

What does Planning do?

M-1.3 planner skill — 把 frozen consensus 翻译成 TaskNode 图 + 依赖边 + 资源 claim。. Planning is an agent skill from Towow-ai/Flowness.

When should I use Planning?

Planning fits situations like: agent Workflows work in your project.

How do I install Planning in Claude Code?

Run `npx skills add Towow-ai/Flowness --skill planning -a claude-code`. Or copy the skill folder (.claude/skills/planning in Towow-ai/Flowness) into .claude/skills/planning in your project. Claude Code loads it when a task matches its description.

How do I install Planning in Codex?

Run `npx skills add Towow-ai/Flowness --skill planning -a codex`. Or copy the skill folder (.claude/skills/planning in Towow-ai/Flowness) into .agents/skills/planning in your project. Codex loads it when a task matches its description.

Can I use Planning in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Towow-ai/Flowness --skill planning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/planning, .gemini/skills/planning, .github/skills/planning and .opencode/skills/planning in your project.

What does Planning need to run?

SKILL.md names no scripts, command-line tools or credentials: Planning is instructions for the agent only.

Does Planning access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Planning safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Planning use?

Planning is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Planning use?

About 2.3k tokens (SKILL.md is roughly 9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Planning?

Skills that share tags, products or a category with Planning: MCP Server Builder (anthropics/skills, 180k stars), Hook Development for Claude Code Plugins (anthropics/claude-plugins-official, 38k stars), Using Superpowers (farm-fe/farm, 5.6k stars) and Executing Plans Inline (obra/superpowers, 297k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Planning?

Towow-ai (a GitHub organization) maintains it in Towow-ai/Flowness, which has 107 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on August 8, 2026.

Source: Towow-ai/Flowness on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.