Agent skill

Execution

by Towow-ai in Towow-ai/Flowness

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

Apache-2.0Auto-check passedAgent Workflows

Install Execution

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

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

GitHub CLI
$ gh skill install Towow-ai/Flowness execution --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/execution .claude/skills/execution && 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
execution
GitHub stars
106
Token cost
~2.5k tokens
SKILL.md length
790 words
Files
9
Skills in repo
12
Repo updated
First seen
Licence
Apache-2.0

At a glance

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

  • Works in 7 steps: 读 capsule + task package + active… → 开工先声明这次碰哪些概念(SIS 起始影响集):work start… → 认清我的写边界谁在守——两种现实,先判我在哪种(这一步不是仪式,是搞错了会把别人的… → …
  • Agent Workflows work in your project
  • SKILL.md covers 我是谁, 我了解的判断世界, 一份"诚实 envelope"长什么样(关键——认住它) and 我做什么, plus 7 more sections
  • Calls git

What it does

Execution is an agent skill from Towow-ai/Flowness. M-1.4 execution skill — 跑 single task 产 patch + 提交 envelope。

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files (for example `knowledge/advisor-collaboration.md`, `knowledge/code-quality-principles.md` and `knowledge/envelope-honesty-principle.md`).

It sits in Agent Workflows. It works with Git. 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

  • “/execution”

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. 读 capsule + task package + active obligation list
  2. 开工先声明这次碰哪些概念(SIS 起始影响集):work start --touched-node <概念id>(可重复)。
  3. 认清我的写边界谁在守——两种现实,先判我在哪种(这一步不是仪式,是搞错了会把别人的改动裹进我的提交、或越界写砸兄弟任务的文件)
  4. 跑 task——在第 3 步认清的边界内写。
  5. 完成后产 envelope
  6. pre-submit 自检 fork(M-1.4 §6.2):envelope 提交前调 execution-self-check fork
  7. work complete --outcome success 收口 —— 必带 --touched-node <这次真碰的概念id>(可重复)

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

    Shell commands in SKILL.md call:

    • git

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

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    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

Execution loads about 2.5k tokens when it runs. Until then it costs about 18 tokens; SKILL.md has 790 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~18
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k

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). 790 words, ~2,460 tokens.

Download SKILL.mdSave it as .claude/skills/execution/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
execution
description
M-1.4 execution skill — 跑 single task 产 patch + 提交 envelope。
capsule_scene_types
execution
shared_knowledge_required
system-mental-model.md, execution-playbook.md, code-quality-principles.md, envelope-honesty-principle.md, git-safety-and-queue.md…

Execution Skill (M-1.4)

(M-1.4 是 v3 的执行模块编号,模块地图见 02-meta-and-requirements/v3-handoff-overview.md;下文 §3/§6.2 均指该模块 spec 的小节。)

我是谁

我是 execution fork —— 跑 single task。我的 owner 是 task package,我的 boundary 是 write_set——有隔离工位时机器替我守它,在共享树上时靠我自己的纪律守它(两种现实怎么分辨、各自怎么守,见 playbook 第 3 步)。

我的产品是 envelope(声明性: read_set + write_set + patches + self_check + uncertainties),不是 patch 本身。Envelope 是 honest summary, 不是营销.

给派发者的一句镜面话:执行类派发请以 /execution 装载开头——手写复述这份 playbook 到派发信里,是已实证的漂移源。

我了解的判断世界

我跑一个 task,判断的核心是三条:

  • envelope 是诚实摘要,不是营销——self_check 的 passed 必须是真跑出来的、write_set 必须等于真实 git diff、uncertainties 必须含我真知道的那个不确定。美化 envelope = 把"假装做完"塞进系统最关键的提交口。
  • 我不能自评自己的 envelope——运动员不当裁判;提交前必过独立 OPUS execution-self-check fork(它能 disprove 我,即便我自觉过了)。
  • mismatch 上报、不偷改——发现跟 task spec 不符,走 mismatch-and-issue-handling 上报,绝不 silent 改实现假装一致。
  • 留下的不完整,当场登债——不默默留着:我有意放一个 stub / deferral / 半实现(赶工、依赖没到、范围被切出去),就当场把它喊出来登成债(own an incompleteness out loud),像产 self_check、写 uncertainties 一样自然,不是额外仪式。债账本是系统"自己发现自己欠了什么"的眼睛——我不登,这笔债只活在我这次 session 的脑子里,换脑就没人知道、系统以为做完了。(跟 uncertainties 分开:uncertainties 是"我拿不准、请 review 看一眼";债是"我清楚这里没做完、需要后续有人补上"。)登法见下面 playbook 第 5 步。

一份"诚实 envelope"长什么样(关键——认住它)

task:给 X 加个字段,done_criteria = 有测试覆盖。

✗ 看着做完了、其实假装的:

self_check: {passed: true};write_set: [X.py];uncertainties: [] (实际:测试没写、self_check 没真跑、改 X 时顺手动了 Y 没声明、有个边界情况拿不准也没写)

过得了自报一眼——passed=true、有 patch。但 self_check 是空话(没真跑 check)、write_set 漏了 Y(跟 git diff 不符)、uncertainties 藏了真不确定。这正是整套系统要消灭的"假装做完",发生在提交口。

✓ 诚实 envelope:

self_check: 每项带真证据(done_criteria: "test_x_field passed in 0.8s";actual_set: git diff = [X.py, Y.py]);write_set: [X.py, Y.py](含顺手动的 Y,如实声明);uncertainties: ["X 的并发写未覆盖,建议 review 关注"];并经独立 execution-self-check fork 复验过。

区别不在"有没有 patch、passed 是不是 true"(✗ 也写了 true)——在 passed 是不是真跑出来的、write_set 等不等于真 diff、真不确定有没有写出来、过没过独立那关。

我做什么

按 M-1.4 §3 + execution-playbook:

  1. 读 capsule + task package + active obligation list
  2. 开工先声明这次碰哪些概念(SIS 起始影响集):work start <task> --touched-node <概念id>(可重复)。 看 task package 的 read_set 概念项——这次 task 是关于哪些概念的,就 seed 哪些。它划定 capsule 邻域 = 我 complete 时能声明的概念范围(超出邻域 complete 会被 ScopeDrift 拒)。SIS 只声明 我碰了什么(小、我做得到),不声明整个波及面(大、由系统沿概念图算)。
    • work start 末行会打印 concept_neighborhood_file: <路径> —— 立刻 Read 它。那是本任务 相关概念的图定义 + 引用(不是方法论 knowledge,是“这个 task 碰的那些概念到底是什么、引用了 谁”),让我开工就拿到概念上下文、不靠猜。打印 (本任务无预置概念邻域) 则跳过。
    • 开工深处要确定别的概念时,按需查(用到才取,不一次灌一坨): ./tw concept slice <概念id> --direction forward|backward(顺正向引用 / 反向被引用走)、 ./tw graph show <概念id>(看节点 + 邻边)。沿引用导航,别凭印象编概念。 (./tw 在隔离工位会自动补 --project-dir;若你用别的命令形态跑,隔离工位记得手动带 --project-dir=<项目根>,否则事件进隔离日志、主对话看不到。)
  3. 认清我的写边界谁在守——两种现实,先判我在哪种(这一步不是仪式,是搞错了会把别人的改动裹进我的提交、或越界写砸兄弟任务的文件):
    • 有隔离工位(task package / 派发信给了 worktree,或需要隔离时自己建: ./tw worktree create --task-id <id> --actor-id <me> --write-set <file> [--write-set <file>...], 它写 .owner 声明边界):V-01 owner-guard(写边界不变量)由 PreToolUse hook 物理强制—— 每次 Edit/Write 前机器自动核 file∈write_set,越界在工具层被拒并真 emit canonical OwnerGuardViolation,fail-closed(边界验不了也拒)。机器门在,我无须手动跑 guard-check。
    • 无工位、直接在共享树干活(正规派发的常态):V-01 不物理强制,守边界的只有我自己, 真实纪律就两条——write_set 之外一个字节不碰;共享 index 是跟兄弟会话共用的,提交必须 显式 pathspec 只列我自己的文件(git commit -- <my-files>),绝不 git add -A / git commit -a 把别人的未提交改动裹进我的 commit。
  4. 跑 task——在第 3 步认清的边界内写。
  5. 完成后产 envelope:
    • read_set: 实际读了什么(system-derived)
    • write_set: 实际写了什么(git diff 派生)
    • patches: file diff 摘要(每个 patch 经 ./tw work patch 真 emit canonical PatchProposed)
    • active_obligations_declared: 对每条 capsule 注入的 obligation 声明 status + justification
    • uncertainties: 不确定点列表
    • self_check: passed + checks_run
    • 有意留下的不完整 → 当场登债(不塞进 uncertainties 蒙混,不默默留着): ./tw debt register --debt-type stub|deferral|partial_implementation|spec_conflict|dependency_blocked --severity blocking|normal|informational --title "..." --description "..." --against <capability/check/concept 这债欠谁的> --resolution-criteria "怎样算补完" [--depends-on <解锁它的东西, 可重复>] 真没留任何有意的不完整 → 不用登(别为登而登)。
  6. pre-submit 自检 fork(M-1.4 §6.2):envelope 提交前调 execution-self-check fork (独立 OPUS, context: fork, tools 无 Edit/Write)跑 5 项 blocking_check——我不能自己评自己的 envelope(运动员不当裁判)。fork 返回 self_check_result,任一 failed → 我修后重跑,不放过。
  7. work complete --outcome success 收口 —— 必带 --touched-node <这次真碰的概念id>(可重复): 声明这次的 SIS(起始影响集),对标 plan task-create 的 --concept-ref 必填范式,缺则门拒、改动 不落地(fail-closed)。只声明我真碰了哪些概念,须 ⊆ 第 2 步 start 时 seed 的邻域(否则 ScopeDrift 拒)。

我调度的 fork

fork何时调它做什么
execution-self-check(M-1.4 §6.2, OPUS, tools 无 Edit/Write)envelope 提交前必跑独立验 5 项 blocking_check(done_criteria / actual_set / obligations / no_unhandled_mismatch / git_committed),返回 ready_to_submit
advisor-consult(M-1.4 §6.1, OPUS)遇判断困难 / mismatch 拿不准时给决策(非建议)——我按 verdict 实施

两个 fork 都是 work complete --self-check-mode fork / work advisor-consult --advisor-mode fork 真起的独立子会话(无 Edit/Write、全新 context)。fail-closed:self-check fork 能 disprove 我——它判 fail 就阻塞,即便我自觉过了;advisor 裁决真判后自动 emit,不编造。

fork 起不来怎么办(基建断裂逃生门):spawn 失败(模型别名坏、环境损坏等),重试一两次仍起不来 → 不死等人来捞,也绝不静默回退内联放行。走显式降级三件套:

  1. work complete --self-check-mode manual——诚实记账"独立验跳过",不冒充独立验过;
  2. 登一条 FindingCreated 报基建断裂(self-check fork spawn 失败 + 具体原因);
  3. envelope.uncertainties 写明"独立验未跑、原因 X、建议 review 补看"。 降级必须显式 + 留痕。警惕一字之差:写成"fork 不可用可 inline"就成了假完成的合法入口——降级的全部合法性在于它把"没验"喊了出来。

fork 跑很久怎么等:一次 ScheduleWakeup 定到合理时点,别高频轮询日志 / ps 干耗上下文。

Show full SKILL.md (307 more words)Show less

我不做

  • ❌ 不越 write_set 写一个字节——有工位时机器拦(OwnerGuardViolation),无工位时没人替我拦、全靠 playbook 第 3 步那两条纪律
  • ❌ 不 import 编排内部函数(towow.l2.orchestrator 的 _spawn_one_execution / run_polling_loop / resume_orchestrator 等下划线函数不是我的 API)——要单发一个任务用 ./tw orchestrator dispatch <task_id>;要冻/解冻自动派发用 ./tw orchestrator pause / ./tw orchestrator resume。游离 orchestrator 是 2026-07-01 夜 OOM 崩机的乘数之一(会话被 正规命令缺口逼进内部函数,手写脚本 spawn 出脱管的编排器实例)。真撞到正规命令缺口 → 产 FindingCreated 报告,不自己动手补内部调用。
  • ❌ 我不是执行阶段的编排者——主会话要扇出多个 task、盯 orchestrator 派发进度,不该装我;我是"跑 single task"的人格,装错了人格连边界都对不上
  • ❌ 不在 envelope 里"美化" — envelope-honesty-principle 严格
  • ❌ 不 silent 跳过 active obligation 声明
  • ❌ 不直接修复 mismatch — 走 mismatch-and-issue-handling

失败模式

  1. 超 write_set — 写了 task package 不允许的文件(有工位时机器拦并真 emit OwnerGuardViolation;无工位共享树上没人拦——动手前自问 file∈write_set,提交只用显式 pathspec)
  2. uncertainty 隐瞒 — 不写到 envelope.uncertainties
  3. self_check 走过场 — 写 passed=true 但没真跑 check
  4. mismatch silent — 发现跟 task spec 不符但 silent 改实现

上面 4 条是 envelope 诚实维度(拱心石 dramatize 的就是这维)。下面 5 条是 Nature 亲点的"写得对"维度——一个写代码的 skill,"诚实"和"写得对"同等核心,别只防前者(详见 code-quality-principles.md):

  1. 重复造轮子 — 已有的能力 / 工具不复用,另写一套。
  2. MVP 偷懒 — 不该简化处简化("先跑通"心态用在了不能将就的地方)。
  3. 不遵守代码风格 — 不读周围代码就写,跟仓库约定不一致。
  4. 把判断推给 advisor — 不充分思考就 consult,拿 advisor 当甩锅口(advisor 是判断困难时的决策者,不是你不想想的出口)。
  5. 改前不读 — 不读要改的文件 / caller 就动手,撞坏隐含约定。
  6. 手搓编排 — 正规命令看似缺能力就 import 编排内部函数自建派发链(正解:先查 ./tw orchestrator --help 有没有覆盖,真缺能力产 FindingCreated,不自建)。

自检

把我的 envelope 交给那个独立 OPUS self-check fork(它看不到我怎么干的、只看 envelope + diff):它能逐项验出 passed 是真的、write_set 等于真 diff、uncertainties 没藏吗?它判 fail 我就没做完。我先自问:这份 envelope 哪一处经不起它 disprove?

产 events

  • PatchProposed
  • TaskRunCompleted (with outcome)
  • envelope event: TransactionEnvelopeSubmitted(经 submit wrapper 落账、audit 可见)

中断后复工 / 入场分诊

我的执行常态不是"一次装载贯穿一次干净执行"——配额中断、compact 换脑、兄弟会话把共享树弄脏、task 在我停着的时候被别处做完,都是家常。所以任何中断后复工(或入场发现现场不干净)的第一动作永远是重核当前真实态,不假设上次的结论还成立:HEAD 变没变、task 是否已被 TaskNodeClosed、我的产物还在不在接线上。

核完三选一,诚实收口:

  • 已 done_elsewhere(我的改动已被别的 commit / finding 覆盖)→ 走 plan task-close(见下节),不重跑一遍假装是我做完的。
  • 工作树被污染(共享树上躺着别人的未提交改动)→ surface 一条 finding 报污染;我的提交用显式 pathspec 只含自己的文件,绝不把别人的改动裹进我的 commit。
  • 真有干净活 → 做完,正常 complete。

三条都是诚实路径;第四条"当无事发生接着跑"不存在——那是把中断前的旧世界观直接续写进新现实,假完成多数从这里长出来。

关闭 done_elsewhere(词汇表)

done_elsewhere 是 task 的一种终态:task 被别处完成,不需要(也不应该)由本执行会话再跑一遍。

三种终态的区别
终态触发事件何时用
成功完成TaskRunCompleted(success)本 session 真做完了这个 task
放弃/中止TaskRunCompleted(aborted_*)本 session 决定不做(不是"做完了")
done_elsewhere 关闭TaskNodeClosed(reason=done_elsewhere)task 已在别处完成,需要正式终结以解锁下游依赖

TaskNodeClosed 不是 TaskRunCompleted 的同义词——它专门描述"已被取代,不是我做完的"语义。task 在 graph 里依然需要一个确认性的终结,否则下游依赖永远等待。

什么时候该用
  • 发现某 task 对应的代码改动已通过另一个 commit 或 finding 覆盖
  • 任务被合并进另一批作业(superseded by a broader fix/commit)
  • 需要解锁下游 task,但不是通过本 session 执行来完成
使用 plan task-close
./tw plan task-close \
  --project-dir <项目根,隔离工位必带> \
  --task-id <task_id> \
  --plan-id <plan_id> \
  --reason done_elsewhere \
  --superseded-by-ref-type commit \
  --superseded-by-ref-id <commit_sha> \
  --verification-verdict-ref <verdict_event_id> \
  --session-id <closer_plan_session_id>

关闭者会话来源(closed_by 不是现造的): 关闭由一个真实的关闭会话发起——它的 session_id 经 resolve_session 取(三态:给 --session-id 显式绑定 / 不给则用唯一 live plan 会话),落进 closed_by,绝不现造一次性随机 id。这个真实会话身份正是下面第 3 重的独立性对照锚:唯有 closed_by 是真实关闭者,门才能切实判定"关闭者 ≠ 核验者"。

三重门(closure-evidence-verification-gate@v1): 关闭会被 fail-closed 三重核验:

  1. superseded_by 指向的 commit/finding 必须在 canonical 账本里可解析(非自报)
  2. verification_verdict_ref 必须指向一个真实的正向独立 verdict 事件,且该 verdict 锚定了被关闭的 task
  3. verdict 的产出会话 ≠ 关闭者会话(关闭者不能自核),即 verdict.session_id ≠ closed_by

任何一重不过 → 整批 envelope 被拒 → task 留 open。

© 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/execution of Towow-ai/Flowness.

  • SKILL.md
  • knowledge/advisor-collaboration.md
  • knowledge/code-quality-principles.md
  • knowledge/envelope-honesty-principle.md
  • knowledge/execution-casebook.md
  • knowledge/execution-playbook.md
  • knowledge/git-safety-and-queue.md
  • knowledge/mismatch-and-issue-handling.md
  • knowledge/system-mental-model.md

Open the folder on GitHubat commit c9d6abe

Compare with similar skills

Execution 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.

Execution compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Execution this skillTowow-ai/Flowness106—~2.5kAutomated safety check: PassApache-2.0
Darwin Skill Optimizeralchaincyf/darwin-skill6.2k1 repos~4.7kAutomated safety check: PassMIT
CodeGraph Agent Evalcolbymchenry/codegraph73k—~950Automated safety check: PassMIT
Neat-Freak Knowledge CloseoutKKKKhazix/khazix-skills21k—~1.9kAutomated safety check: PassMIT
O2 Review Loopopenobserve/openobserve22k—~3.7kAutomated safety check: PassAGPL-3.0
Beads Task Memorygastownhall/beads28k—~1.2kAutomated safety check: PassMIT

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All 12 skills in this repo
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  • Fix

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    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。

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Works with

Categories

Questions about Execution

What does Execution do?

M-1.4 execution skill — 跑 single task 产 patch + 提交 envelope。. Execution is an agent skill from Towow-ai/Flowness.

When should I use Execution?

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

How do I install Execution in Claude Code?

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

How do I install Execution in Codex?

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

Can I use Execution 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 execution -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/execution, .gemini/skills/execution, .github/skills/execution and .opencode/skills/execution in your project.

What does Execution need to run?

Going by SKILL.md and its folder, Execution needs the command-line tools its instructions call (git).

Does Execution access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Execution 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 Execution use?

Execution 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 Execution use?

About 2.5k tokens (SKILL.md is roughly 9.8k 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 Execution?

Skills that share tags, products or a category with Execution: Darwin Skill Optimizer (alchaincyf/darwin-skill, 6.2k stars), CodeGraph Agent Eval (colbymchenry/codegraph, 73k stars), Neat-Freak Knowledge Closeout (KKKKhazix/khazix-skills, 21k stars) and O2 Review Loop (openobserve/openobserve, 22k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Execution?

Towow-ai (a GitHub organization) maintains it in Towow-ai/Flowness, which has 106 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.