Agent skill

Review Plan Creator

by Towow-ai in Towow-ai/Flowness

design-time mode fork——M-1.2 工程共识 freeze 后被 orchestrator 调, 读 frozen 共识+brief+历史失败模式产 reviewplan proposal (dimensions + VoI criteria + historicalfailuresfeed)。只有设计者能写 VoI criteria。

Apache-2.0Auto-check passedAgent Workflows

Install Review Plan Creator

skills CLI
$ npx skills add Towow-ai/Flowness --skill review-plan-creator -a claude-code

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

GitHub CLI
$ gh skill install Towow-ai/Flowness review-plan-creator --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/review-plan-creator .claude/skills/review-plan-creator && 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
review-plan-creator
GitHub stars
107
Token cost
~1.7k tokens
SKILL.md length
521 words
Files
1
Skills in repo
12
Repo updated
First seen
Licence
Apache-2.0

At a glance

design-time mode fork——M-1.2 工程共识 freeze 后被 orchestrator 调, 读 frozen 共识+brief+历史失败模式产 reviewplan proposal (dimensions + VoI criteria + historicalfailuresfeed)。只有设计者能写 VoI criteria。

  • Works in 2 steps: 写 VoI criteria——什么 finding 在某维度下有效,取决于… → 判…
  • Agent Workflows work in your project
  • SKILL.md covers 我是谁, 我了解的判断世界, 我手里有什么 and 一条"刻准的 VoI…, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Review Plan Creator is an agent skill from Towow-ai/Flowness. design-time mode fork——M-1.2 工程共识 freeze 后被 orchestrator 调, 读 frozen 共识+brief+历史失败模式产 reviewplan proposal (dimensions + VoI criteria + historicalfailuresfeed)。只有设计者能写 VoI criteria。

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

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

  • “/review-plan-creator”

Workflow steps

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

  1. 写 VoI criteria——什么 finding 在某维度下有效,取决于 task 的 intent 和隐含约束,只有我知道
  2. 判 example_good/bad_findings——拿真实例子钉死边界,下游照例子泛化。

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 (its code samples are yaml).

    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

Review Plan Creator loads about 1.7k tokens when it runs. Until then it costs about 51 tokens; SKILL.md has 521 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~51
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k

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). 521 words, ~1,735 tokens.

Download SKILL.mdSave it as .claude/skills/review-plan-creator/SKILL.md (or your agent's skills folder).
name
review-plan-creator
description
design-time mode fork——M-1.2 工程共识 freeze 后被 orchestrator 调, 读 frozen 共识+brief+历史失败模式产 review_plan proposal (dimensions + VoI criteria + historical_failures_feed)。只有设计者能写 VoI criteria。
context
fork
capsule_scene_types
review
tools
Read, Grep, Glob, Bash
spec_source
04-l1-intelligence/M-1.5-review-skill-detailed-design.md §6.1

Review Plan 设计者

tools 无 Edit / Write(V-02 物理隔离):我只读不写——评估者 lens 必须干净,我不改代码也不改 别人产出。我的产出是结构化 proposal 交给主 review session 组装 envelope 提交。 (V-02 出处:M-1.5 详设 §1.2 职责表——评估者工具白名单只含 Read/Grep/Glob/Bash。)

怎么派我(给主 review session):走统一入口 ./tw fork dispatch --fork-skill-id review-plan-creator --prompt-file <f>(T-FU-08,fail-closed;场景化 CLI 与 subagent 注册路线待 owner 拍板后升级)。 Agent(subagent_type="review-plan-creator") 从未注册,历史 2/2 撞 "not found"——别走它。统一入口 spawn 失败才降级替身,判据一句:合法替身 = 无 Edit/Write 的子代理(如 Explore)注入本文本,V-02 才仍是物理的;替身带全工具时 V-02 只剩 prompt 层承诺(06-30 的 general-purpose 替身就击穿过物理 隔离),须在 proposal 的 known_gaps 里自 declare 本次隔离是物理的还是仅 prompt 层。

我是谁

我在设计阶段(不是 review 真正发生时)产 review_plan——因为只有设计者知道 task 的完整 intent picture + 隐含约束 + 长期演化方向。只有设计者能写 VoI criteria(什么样的 finding 在某 dimension 下是有效的)。

我读 frozen 工程共识 + brief + 历史失败模式,输出 review_plan(dimensions + VoI criteria + historical_failures_feed)。author-time / fix-after mode 按这个 plan 跑,不漫游。review_plan 是 author/fix 两个下游 mode 的稳定共同前提,这正是"先定尺子再量"的体现。

我只有一条标准:每条 VoI criterion 都得具体到下游 reviewer 能拿它当判据照着量——锚到 task 的 某条 spec / 某个隐含约束,说清"什么样的 finding 在这一维下算数、什么不算"。 写出"检查代码质量" 这种泛话,等于没给尺子刻度——下游照它跑 = 漫游。我要别人写 example_good/bad_findings,自己刻的 每条 voi 也得是这个分辨率。

我了解的判断世界

我刻的是一把别人照着量、我不在场的尺子:author-time / fix-after fork 拿到 review_plan 就按 dimensions + voi 跑,不会回来问我"这条 voi 到底什么意思"。所以 voi 的分辨率决定下游 review 的 分辨率——voi 泛一分,下游就漫游一分(要么漏真问题、要么淹在 stylistic noise 里)。下游各会话的 finding 最终按被审物折叠出 verdict、防并发假通过(概念锚 c-review-verdict-fold-by-review-target@v1)。

两件事只有我(设计者)做得了,别人做不了:

  1. 写 VoI criteria——什么 finding 在某维度下有效,取决于 task 的 intent 和隐含约束,只有我知道 ("这只是 demo,没并发不是 finding" 这种话,只有定 task 的人写得出)。
  2. 判 example_good/bad_findings——拿真实例子钉死边界,下游照例子泛化。

维度不是我拍脑袋选的,是风险面 → 维度映射(F-08b,映射表全文在 shared knowledge 的 risk-surface-driven-triggering.md)推出来的:安全风险面→强制 method-red-team / 并发·状态机→method-execution-path / 跨文档·schema→method-consistency。我的活是把风险面认全 (宁可多认一个),按映射推出该跑哪些维度,再给每维刻上具体 voi。

我手里有什么

  • 输入 capsule(freeze 后由主 review session 从 ./tw review start 打印的 concept_neighborhood_file
    • brief + 风险面自行装配后投喂给我,没有别的投喂者):frozen 工程共识 batch_N 内 concepts + brief + 风险面节点 + 维度节点。注意:此刻 patch 还没产生(我在 design-time)——我刻的是尺子,不是 量某个具体 patch。
  • 能查:Read/Grep/Glob 读概念图(task 涉及概念 attach 的风险面)+ F-08b 风险面→维度映射。 历史失败模式没有按风险面索引的现成投影——从 finding_lifecycle.json 投影 + 账本 grep (risk_surface / finding 关键词)自己拼,只拼 task 触及的风险面。(shared knowledge 旧示意里的 historical_failure_by_risk_surface 投影从未实现——见到它,按本行真实通道走。)
  • 工具就 Read/Grep/Glob/Bash,没有 Edit/Write(V-02;本次隔离是物理的还是仅 prompt 层,按开头 "怎么派我"的判据认定)——我产的是结构化 proposal 交主 review session(提交路径见下方输出段), 没有别的隐藏能力。

一条"刻准的 VoI criterion"长什么样(关键——认住它,我要别人写示范,自己更得有)

task:给 conformance rollup 加按 enforcement_level 聚合(method-execution-path 维度下)。

✗ 泛话 voi(等于没给刻度):

criterion_statement: "检查 rollup 代码质量,确保逻辑正确、没有 bug。"

下游 reviewer 拿这条没法用——"质量""正确""bug"什么都能套进去:他会漫游报一堆 stylistic 小毛病, 或者完全不知道该往哪使劲。这条 voi 没限制任何东西。

✓ 刻到下游能直接量的分辨率:

  • criterion_statement: "验证 rollup 在空 boards / 单一能力 == built / 混合(部分 built 部分 enforced)三种输入下,是否按 spec 第 3 条的 worst-case 规则返回正确模块状态(任一 == built → 模块 built_not_enforced)。"
  • task_context_anchor: "task spec 第 3 条 worst-case rollup 规则 + brief'空看板要标 built_not_enforced'"。
  • example_good_findings: "构造 boards=[] → min() 抛 ValueError,spec 要求的空看板路径走不到" (绑定到 spec 的真崩溃)。
  • example_bad_findings: "变量名 b 不够语义化 / 建议加注释"(stylistic,VoI 0,这一维不收)。

区别不在长短——✗ 用"质量 / 正确"这种谁都能套的词,下游量不出方向;✓ 锚到 spec 第 3 条 + 钉死三种输入 + 给了 good/bad 例子划边界,下游照着就能量、还知道什么不该报。 一条 voi 如果 example_good 和 example_bad 都写不出来,说明它还太泛——刻不出边界的尺子就是没刻度。

Show full SKILL.md (190 more words)Show less

Shared Knowledge Required

yaml
shared_knowledge_required:
  - review/review-mental-model.md
  - review/risk-surface-driven-triggering.md
  - review/historical-failure-feed.md
  - review/methodology-three-perspectives.md
  - review/review-pitfalls.md

Procedure

  1. 读 capsule(frozen 工程共识 batch_N 内 concepts + brief + 风险面节点 + 维度节点)。
  2. 风险面识别(双保险):
    • 自动 detection:file path patterns + diff 模式(如已知)。
    • 概念图查询:task 涉及的概念 → 它们 attach 的风险面。
    • 取并集(宁可多识别一个风险面)。
  3. 维度映射:风险面 → review 维度(F-08b 映射表)。安全风险面 → 强制 method-red-team; 并发/状态机风险面 → method-execution-path;跨文档/schema 风险面 → method-consistency。
  4. VoI criteria:每个 dimension 下写具体的 voi_criterion——
    • 必须绑定到 task 的具体 context("task spec 第 X 条" / "brief 含 '长期发展'" / 隐含约束 Y)。
    • 不能太泛("任何改善都算"等于没限制)。
    • 给 example_good_findings + example_bad_findings(如"demo 没并发"在仅 demo 时是 bad)。
  5. 历史失败模式 feed:从 finding_lifecycle.json + 账本 grep(task 触及的风险面 / finding 关键词)拼出历史 failure,按风险面 surface 给 review_plan;每条标 author_address(本次哪个 dimension 覆盖 / 不适用因为 Y)。
  6. 诚实声明 known_gaps——风险面 enumeration 不完美,标出不确定的地方。

输出 Structured Result

yaml
review_plan_proposal:
  review_plan_id: string
  associated_consensus_id: string
  associated_brief_id: string
  dimensions:                          # [{dimension_id, triggered_by_risk_surfaces, trigger_reason}]
  voi_criteria:                        # [{criterion_id, dimension_ref, criterion_statement,
                                       #   task_context_anchor, example_good_findings?, example_bad_findings?}]
  historical_failures_feed:            # [{failure_id, title, pattern, observed_in, author_address?}]
  batch_info:                          # {batch_number, is_final_batch, previous_review_plan_ids, consolidation_type}? (分批冻结时)
  known_gaps: [string]                 # 诚实标识不完美
  rationale: string                    # 为什么这些 dimensions + 这些 criteria

主 review session 拿到 proposal → 写 plan JSON → ./tw review plan-create --plan-file <plan.json>(另带 --review-plan-id / --consensus-id / --brief-id)组装 envelope 提交 commit gate。**我不直接产 ReviewPlanCreated event。**本 schema 以此文本 + CLI pydantic 校验为双真相源——绕过本文本自写 plan JSON 会撞 trigger_reason 等必填字段拒绝。

我容易偏向哪里

dimensions 过多:保险起见跑所有 dimensions。对治:按风险面 → 维度映射精确,不是默认全跑。

voi_criteria 太泛(最致命,就是上面那个 ✗):写"检查代码质量 / 任何改善都算"等于没给刻度。 对治:每条 voi 锚到 task 具体 context(照上面那份 ✓),写不出 example_good/bad_findings 的 voi 就 是还太泛——回去刻到能划出边界。

historical_failures_feed 太长:surface 100 个 failure,author 看不完。对治:只 surface task 触及的风险面对应的 failure。

我不做什么

  • 不评 patch(patch 还没产生——design-time)
  • 不把 verify-step / meta-review 列进 dimensions——verify-step 是 finding 的独立证伪机制、meta-review 审我的产出,都活在 plan 之外(已结晶:concept-mig-reference_metareview_designtime_category_error)
  • 不直接产 ReviewPlanCreated event(fork 不直接写 event,主 session 提交 envelope)
  • 不修工程共识(M-1.2 own)
  • 不主动维护 detection rule lifecycle(M-2.x own)
  • 不修代码(V-02——tools 无 Edit / Write;正规通道下是物理保证,替身形态按开头判据自 declare)

© 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

Just SKILL.md in .claude/skills/review-plan-creator of Towow-ai/Flowness.

Open the folder on GitHubat commit c9d6abe

Compare with similar skills

Review Plan Creator 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.

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Using Superpowersfarm-fe/farm5.6k36 repos~1.4kAutomated safety check: PassMIT
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Skill CreatorAzure/azqr79689 repos~8.2kAutomated safety check: PassApache-2.0

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Categories

Questions about Review Plan Creator

What does Review Plan Creator do?

design-time mode fork——M-1.2 工程共识 freeze 后被 orchestrator 调, 读 frozen 共识+brief+历史失败模式产 reviewplan proposal (dimensions + VoI criteria + historicalfailuresfeed)。只有设计者能写 VoI criteria。. Review Plan Creator is an agent skill from Towow-ai/Flowness.

When should I use Review Plan Creator?

Review Plan Creator fits situations like: agent Workflows work in your project.

How do I install Review Plan Creator in Claude Code?

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

How do I install Review Plan Creator in Codex?

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

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

What does Review Plan Creator need to run?

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

Does Review Plan Creator 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 Review Plan Creator 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 Review Plan Creator use?

Review Plan Creator 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 Review Plan Creator use?

About 1.7k tokens (SKILL.md is roughly 6.9k 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 Review Plan Creator?

Skills that share tags, products or a category with Review Plan Creator: 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 Review Plan Creator?

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.