Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add kanfu-panda/pdlc-skills --skill pdlc-reviewgit clone --depth 1 https://github.com/kanfu-panda/pdlc-skillsWrote 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/kanfu-panda/pdlc-skills/pdlc-review)<a href="https://agentmods.dev/skills/kanfu-panda/pdlc-skills/pdlc-review"><img src="https://agentmods.dev/badge/skills/kanfu-panda/pdlc-skills/pdlc-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/kanfu-panda/pdlc-skills/pdlc-review"><img src="https://agentmods.dev/badge/skills/kanfu-panda/pdlc-skills/pdlc-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 19 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- high Prompt Injection · line 19 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00013 | $0.03210 |
| Opus 5 | $0.00006 | $0.01605 |
| Sonnet 5 | $0.00003 | $0.00642 |
| Haiku 4.5 | $0.00001 | $0.00321 |
Grade A, and why
pdlc-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 12d 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 — 252 lines — stays where its author put it; the contents beside it link to each section on GitHub.
代码评审
对指定的服务或应用进行全面的代码评审。
PDLC 前置检查(必须执行,不可跳过)
- 从用户输入中提取功能名称关键词
- 检查实现代码是否存在:在
backend/和frontend/下搜索与该功能相关的源代码文件(非测试文件) - 检查测试是否通过:找到对应的测试代码并运行,确认测试处于绿灯状态(全部通过)
- 未找到实现代码 → 输出以下信息后立即停止,不继续执行:
⛔ PDLC 守卫:未找到与「<功能名>」相关的实现代码。 评审必须基于已有的代码实现。请先运行: 👉 /pdlc-implement <目标> - 测试未通过 → 输出以下信息后立即停止,不继续执行:
⛔ PDLC 守卫:「<功能名>」的测试未全部通过,无法进行评审。 请先确保所有测试通过后再提交评审: 👉 /pdlc-implement <目标>(修复失败的测试) - 检查通过 → 提取功能ID(从相关设计文档或 PRD 中),继续执行
评审流程
- 阅读设计文档: 先阅读
docs/02_design/对应子目录下的相关设计文档 - 阅读编码规范: 阅读
docs/00_standards/coding/目录了解编码规范(未命中 → 报告里提示consider /pdlc-standard add coding/<topic>) - 检查代码实现: 对照设计文档逐一检查实现是否符合
- 检查测试覆盖: 确认测试是否充分覆盖
- 代码质量自动检查与修复(必须执行):
- 按
/pdlc-lint check逻辑运行项目 lint 工具 - 若存在可自动修复的问题,按
/pdlc-lint fix逻辑自动修复 - 记录修复前后的问题数变化
- 按
评审检查项(逐项检查,发现问题立即修复)
设计一致性(对照设计文档)
- 每个 API 接口的 URL、方法、参数是否与设计文档一致
- 数据库表结构、字段名、类型是否与 DB 设计一致
- 响应格式是否统一遵循
{ code, message, data }
代码质量
- 命名是否规范(变量/函数/类遵循项目命名约定)
- 是否有重复代码可提取为公共方法
- 错误处理是否合理(不吞异常、不用空 catch、有意义的错误信息)
- 日志是否充分(关键操作有日志、不打印敏感信息)
安全检查
- SQL 注入:是否使用参数化查询/ORM,无字符串拼接 SQL
- XSS:用户输入是否转义后再输出
- 权限控制:接口是否有鉴权,敏感操作是否有权限校验
- 敏感数据:密码是否加密存储、Token 是否有过期机制、日志不含敏感字段
性能检查
- 数据库查询是否有 N+1 问题
- 列表接口是否有分页
- 是否有不必要的全表扫描(缺失索引)
- 大数据量操作是否有批处理
测试完备性
- 单元测试覆盖率是否达标(覆盖率达标线以项目配置为准:优先取
docs/00_standards/test-commands.yml的 coverage 命令阈值参数(那才是强制点,退出码即判定),其次quality-targets.yml;两者都没有时按 >= 80% 兜底。) - 核心业务路径是否有完整的测试
- CHANGELOG 是否已更新
自动修复(评审中发现的问题,能修则修)
对以下类型的问题直接修复代码,不仅仅记录:
- lint 问题:运行 lint fix 自动修复格式、规范问题
- 命名不规范:自动重命名为符合项目约定的名称
- 缺失错误处理:自动补充 try-catch / 错误码返回
- 缺失日志:在关键操作处自动添加日志语句
- SQL 注入风险:自动改写为参数化查询
- XSS 风险:自动添加输出转义
- 缺失分页:自动为列表接口补充分页逻辑
- 缺失 CHANGELOG:自动追加变更条目
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.
- 12d ago First seen · 252 lines · 13 tokens per session scan A 8233d0edf44a
pdlc-review is a skill published in the GitHub repository kanfu-panda/pdlc-skills (13 stars, last pushed today), licensed MIT. It adds 13 tokens to every session and 3,210 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 2026-08-31.
Other skills, from other repositories
postgres-database-migration
Use this skill for planning, testing, and safely executing PostgreSQL schema migrations — especially when working with production data or shared databases. Trigger when user asks to: Test a schema migration before applying it to production Add, remove, or rename columns safely on a live table Change a column's data…
setup-timescaledb-hypertables
Use this skill when creating database schemas or tables for Timescale, TimescaleDB, TigerData, or Tiger Cloud, especially for time-series, IoT, metrics, events, or log data. Use this to improve the performance of any insert-heavy table. Trigger when user asks to: Create or design SQL schemas/tables AND…
design-postgis-tables
Comprehensive PostGIS spatial table design reference covering geometry types, coordinate systems, spatial indexing, and performance patterns for location-based applications.
migrate-postgres-tables-to-hypertables
Use this skill to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation. Trigger when user asks to: Migrate or convert PostgreSQL tables to hypertables Execute hypertable migration with minimal downtime Plan blue-green migration for large tables Validate…
pgvector-semantic-search
Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. Trigger when user asks to: Store or search vector embeddings in PostgreSQL Set up semantic search, similarity search, or nearest neighbor search Create HNSW or IVFFlat indexes for vectors…
find-hypertable-candidates
Use this skill to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables. Trigger when user asks to: Analyze database tables for hypertable conversion potential Identify time-series or event tables in an existing schema Evaluate if a table would…