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-perfgit 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-perf)<a href="https://agentmods.dev/skills/kanfu-panda/pdlc-skills/pdlc-perf"><img src="https://agentmods.dev/badge/skills/kanfu-panda/pdlc-skills/pdlc-perf/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-perf"><img src="https://agentmods.dev/badge/skills/kanfu-panda/pdlc-skills/pdlc-perf.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 | $0.00008 | $0.00711 |
| Opus 5 | $0.00004 | $0.00356 |
| Sonnet 5 | $0.00002 | $0.00142 |
| Haiku 4.5 | $0.00001 | $0.00071 |
Grade A, and why
pdlc-perf 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.
What it actually says
性能优化
对指定服务或应用进行性能分析并提出优化方案。
分析维度
后端性能
- 数据库层: 慢查询、N+1 问题、缺失索引、全表扫描
- 缓存层: 缓存命中率、缓存策略（过期/淘汰）、缓存穿透/击穿/雪崩
- 接口层: 响应时间、并发处理、连接池配置
- 代码层: 算法复杂度、内存泄漏、不必要的序列化/反序列化
前端性能
- 加载性能: 首屏时间、资源体积、代码分割、懒加载
- 运行时性能: 不必要的重渲染、大列表虚拟滚动、防抖/节流
- 网络优化: 请求合并、资源压缩、CDN 配置
- 缓存策略: 浏览器缓存、Service Worker、本地存储
工作流程
- 阅读代码: 分析目标服务/应用的核心逻辑
- 识别瓶颈: 标注可能存在性能问题的代码段
- 提出方案: 对每个问题给出优化方案和预期收益
- 实施优化: 按优先级实施优化代码
- 【必须创建文件】 在
docs/07_reviews/code/下创建性能优化报告
⚠️ 必须创建文件，不可仅在对话中输出。
报告格式
文件名: YYYYMMDD-<服务名>-perf-report.md
文档顶部包含 PDLC 追溯头：
<!-- PDLC-TRACE -->
<!-- 功能名称: <服务名> -->
<!-- 阶段: 性能优化 -->
<!-- 创建时间: <ISO 8601> -->
报告内容：
| 序号 | 位置 | 问题描述 | 影响程度 | 优化方案 | 预期收益 |
|---|
创建后验证：确认文件已存在于 docs/07_reviews/code/ 目录
要求
- 优化方案按投入产出比排序（性价比高的优先）
- 给出优化前后的代码对比
- 不要为了优化而牺牲代码可读性
优化目标: $ARGUMENTS
本命令的 handoff 输出：
✅ 性能优化报告 完成
📦 产出：docs/04_testing/perf/<feature-id>-report.md
👉 下一步：（本次流程结束，无后续）
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 · 80 lines · 8 tokens per session scan A 77612ed950de
pdlc-perf is a skill published in the GitHub repository kanfu-panda/pdlc-skills (13 stars, last pushed yesterday), licensed MIT. It adds 8 tokens to every session and 711 once invoked, about $0.0000 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…