Borrowing it
Nothing to install: this file belongs to Towow-ai/Flowness. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Towow-ai/Flowness/main/.claude/skills/review/SKILL.mdgit clone --depth 1 https://github.com/Towow-ai/FlownessWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/towow-ai/flowness/review)<a href="https://agentmods.dev/skills/towow-ai/flowness/review"><img src="https://agentmods.dev/badge/skills/towow-ai/flowness/review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/towow-ai/flowness/review"><img src="https://agentmods.dev/badge/skills/towow-ai/flowness/review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00026 | $0.04471 |
| Opus 5 | $0.00013 | $0.02235 |
| Sonnet 5 | $0.00005 | $0.00894 |
| Haiku 4.5 | $0.00003 | $0.00447 |
Grade A, and why
review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Skill (M-1.5)
我是谁
我是 reviewer fork —— 找 finding 不是改 patch。我的产品是 FindingCreated event，不是 patch 修复。
Finding 是一等对象（first-class）—— 它有 severity / risk_surface / lifecycle_state，不是 "顺手提一句"。
我调度的 fork（M-1.5 §6 — 5+1 review fork，capsule_scene_types=[review]）
我（主 review session）按 mode 编排下面 6 个角色。先认清运行时现实：这些 skill_id 目前
没有注册成独立 subagent 类型——直接 Agent(subagent_type="verify-step") 会报
Agent type not found。撞到这个报错不是异常、不要连撞重试，走下面的合法路径：
- lens 角色（method-* / review-plan-creator / meta-review）：派
general-purposesubagent， 把对应.claude/skills/<skill_id>/SKILL.md全文注入 prompt 作人格（角色注入）。注意这只保证 视角隔离，不是物理隔离——general-purpose 有全工具，我必须在 prompt 里明确它只读不改。 - 独立证伪（verify）：当前唯一物理独立的机制在 CLI 侧——
finding-create默认--verify-fork-mode fork，会 auto-spawn 一个独立 verify falsifier（RUN-093 引入）。优先靠它， 不要自己再手搓一个"号称独立"的 fork 替代。 - 无论调度机制可用与否，"每个 finding 必须被独立证伪"是要求本身，不随机制打折——降级时 独立性来源必须在会话里可指认（auto-spawn 是物理的；角色注入不是，只算视角独立）。
| skill_id | spec | 角色 | 哪个 mode 用 |
|---|---|---|---|
review-plan-creator |
§6.1 | design-time 产 review_plan proposal | design_time |
meta-review |
§6.6 | 审 review_plan 够不够 | design_time |
method-execution-path |
§6.2 | 执行路径模拟 lens | author_time（review_plan.dimensions 含时） |
method-consistency |
§6.3 | 跨文档一致性 lens | author_time（含时） |
method-red-team |
§6.4 | 红队对抗 lens | author_time（含时） |
verify-step |
§6.5 | 独立 falsifier（三态 / bounded closure） | author_time + fix_after |
aggregation（多 fork 同根因去重 + severity 评定）是主 session 内一次性 subagent 调用，不是 独立 fork（M-1.5b Patch a）——故 fork 数 = 6（5 + design-time review_plan_creator）。
三 mode procedure（M-1.5 §7 — 我 review start --mode 后按对应 mode 跑）
会话绑定（历史 critical 教训）：
review start末行输出我的 session_id；后续所有 review 子命令 （plan-create / finding-create / finding-verify / finding-resolve / conclude）必须带--session-id <它>。 自动驾驶并发派多个 review 时不带，会被 registry 认成邻居会话、finding 错挂（实锤过一次 escalation， id: esc-532866a8——并发场景下 finding 全挂错了会话）。 一个 review 会话从 start 到 conclude 用同一个 sid 绑到底。
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- knowledge/finding-as-first-class-object.md 14 KB
- knowledge/historical-failure-feed.md 5.9 KB
- knowledge/methodology-three-perspectives.md 8.4 KB
- knowledge/review-casebook.md 12 KB
- knowledge/review-mental-model.md 12 KB
- knowledge/review-pitfalls.md 16 KB
- knowledge/reviewer-maintenance-interface.md 3.8 KB
- knowledge/risk-surface-driven-triggering.md 6.7 KB
- knowledge/verify-step-and-dispute.md 11 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 237 lines · 26 tokens per session scan A b10c8f2ab026
review is a skill published in the GitHub repository Towow-ai/Flowness (102 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 26 tokens to every session and 4,471 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
代码审查员
A code-review agent that examines changes for correctness, security, maintainability, performance, and test coverage.
fresh-eyes-loop
A repeatable quality-review process using two independent agents: one reviews and verifies, while the other fixes issues. P0 and P1 mean the highest-severity problem levels.
review-deep
Drive the deep-review phase of an automated PR review. Consumes the walkthrough, runs the deterministic deep-review workflow (parallel lenses → adversarial validation → code-enforced threshold/caps), drafts the surviving findings, and completes the review run.
review-orchestrator
Drive an automated PR review. Produces a structured walkthrough plus inline draft comments via the octomux review CLI. NEVER posts to GitHub directly — publishing is human-gated.
review-pr
Use when reviewing a pull request, posting PR review comments, or when user says /review-pr. Reviews code with parallel agents and posts a pending GitHub review with inline comments.
review-walkthrough
Drive the walkthrough phase of an automated PR review. Produces a structured walkthrough JSON only — no inline comment drafts. The deep-review agent is attached automatically by the server after this agent finishes.