pr-body

pr-body is a skill for Claude Code, Codex from alibaba/anolisa. It costs 68 tokens per session (3,068 once invoked), scanned A, original, Apache-2.0.

A tool that reviews all commits on the current branch and creates or updates a pull request title and description. A pull request is a proposed code change submitted for review.

In plain words
What is it for?
Preparing PR text, running a code-review self-check, and recording formatting, lint, architecture, test, and coverage checks in the project's required template.
Why use it?
It turns the actual branch changes, tests, and review findings into a consistent explanation before the code is submitted.

Skill for Claude CodeCodex ✓ vendor

Written for no agent in particular: nothing here depends on one. Also seen: names the AskUserQuestion tool; mentions AGENTS.md.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python3 scripts/check-arch-boundaries.py # 3. 架构边界.

Good fit Preparing PR text, running a code-review self-check, and recording formatting, lint, architecture, test, and coverage checks in the project's required template.

Compare 6 skills from other repositories ↓
About the project

ANOLISA is a server-side operating layer for AI agent workloads that provides terminal access, token-saving tool-output compression, runtime controls, security, observability, skills, memory, and sandbox management. It is for running and supervising agents from a Linux terminal while retaining an existing shell, agent framework, and sandbox. The catalogue add-ons are components of its agent operating environment and workflows.

alibaba/anolisa · 619 stars · on GitHub · agentic-os.sh

Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/alibaba/anolisa
agentmods
npx agentmods add skills/alibaba/anolisa/agentsight-pr-body

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for pr-body

README.md
[![agentmods](https://agentmods.dev/badge/skills/alibaba/anolisa/agentsight-pr-body.svg)](https://agentmods.dev/skills/alibaba/anolisa/agentsight-pr-body)
Your own site
<a href="https://agentmods.dev/skills/alibaba/anolisa/agentsight-pr-body"><img src="https://agentmods.dev/badge/skills/alibaba/anolisa/agentsight-pr-body.svg" alt="Measured on agentmods" height="20"></a>
Per session 68 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,068 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00068 $0.03068
Opus 5 $0.00034 $0.01534
Sonnet 5 $0.00014 $0.00614
Haiku 4.5 $0.00007 $0.00307

Measured 8d ago against content hash 644664ea35b0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

pr-body 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 8d 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.

src/agentsight/develop-skills/agentsight-pr-body/SKILL.md · 265 lines

How it starts

The opening of the file, as written. The whole thing — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.

PR Body 生成器

目标

分析当前分支相对于 main 的全部变更(所有 commit,不仅是最新一条),生成或更新符合 alibaba/anolisa 规范的 PR 标题和正文。

触发时自动执行

步骤 1:收集变更信息

# 当前分支
git branch --show-current

# 全部 commit(从 main 分叉点起)
git log --oneline origin/main..HEAD

# 变更文件列表
git diff --stat origin/main..HEAD

# 完整 diff(用于分析变更内容)
git diff origin/main..HEAD

# 是否已有 PR
gh pr list --head $(git branch --show-current) --repo alibaba/anolisa --state open --json number,title,body

步骤 1.5:Code Review 自检

在生成 PR 描述前,先执行 agentsight-code-review skill 对当前变更进行自检。如果存在 findings,先修复再继续。

步骤 1.6:Preflight 检查(与 CI 门禁逐项对齐)

在分析变更前,运行以下检查并记录结果(后续自动填入 Checklist)。这些检查 镜像 test-agentsight CI job,目的是在 push 前本地拦截会导致 CI 失败的问题 (一次 push + CI ≈ 4 分钟,本地检查 ≈ 30 秒)。

# 在 agentsight 目录下执行(CI 锁定 toolchain 1.89.0;缺则先
# `rustup toolchain install 1.89.0 --component rustfmt --component clippy --component llvm-tools-preview`)
cargo +1.89.0 fmt --all --check                     # 1. 格式
cargo +1.89.0 clippy --all-targets -- -D warnings   # 2. lint
python3 scripts/check-arch-boundaries.py            # 3. 架构边界

# 4. 测试 + 覆盖率(CI 用 llvm-cov 跑测试,不是 cargo test;用默认 toolchain 即可——
#    覆盖率行映射与工具链版本无关,且 +1.89.0 需该工具链装 llvm-tools-preview,
#    dev 机常装在 stable 上。fmt/clippy 上面 pin +1.89.0 是因为 lint 规则版本敏感)
cargo llvm-cov --cobertura --output-path coverage.xml \
  --ignore-filename-regex '(\.skel\.rs|target/debug/build|target/release/build|src/probes/)'

# 5. 增量覆盖率门禁(与 CI 一致:对比 origin/main,阈值 80%)
git fetch origin main
diff-cover coverage.xml --compare-branch=origin/main --fail-under=80
  • 五项全部通过才继续;任一失败按下面处理后重跑。
  • cargo fmt --check 失败 → 跑 cargo +1.89.0 fmt 修复后重新检查。
  • cargo clippy 失败 → 列出告警、修复、重新检查。
  • 架构边界失败 → 按 check-arch-boundaries.py 的提示修正跨层依赖。
  • 覆盖率门禁失败(增量 < 80%)→ 停止,为新增/修改但未覆盖的行补测试 (diff-cover 输出会列出每个文件的 Missing lines);不要靠降阈值绕过。
  • diff-cover / cargo-llvm-cov 未安装 → 安装后再验(pip install diff-coverrustup component add llvm-tools-preview),不要跳过本步却标记"已通过"

6. Commit message 规范检查:对 git log origin/main..HEAD 的每个 commit 逐条核对是否符合 conventional commit(commitlint 是独立的硬门禁,其中 scope 必填 是最容易漏的硬失败;fmt/clippy/覆盖率同样是硬门禁):

Read the full file on GitHub · 265 lines

Changes

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.

  1. 8d ago First seen · 265 lines · 68 tokens per session scan A 644664ea35b0

Subscribe to this mod's changes

pr-body is a skill published in the GitHub repository alibaba/anolisa (619 stars, last pushed today), licensed Apache-2.0. It adds 68 tokens to every session and 3,068 once invoked, about $0.0003 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.