pdlc-implement

pdlc-implement is a skill for Claude Code from kanfu-panda/pdlc-skills. It costs 29 tokens per session (1,427 once invoked), scanned A, original, MIT.

A guarded coding workflow that implements a feature from its design document and existing tests, then checks the result. It requires tests to be written first, following TDD (test-driven development).

In plain words
What is it for?
Use it to implement a planned feature, run its tests, check the implementation against the design, apply safe lint fixes, verify coverage, update the service changelog, and mark the related task complete.
Why use it?
It prevents code from being written without relevant tests or from drifting away from the design. It also catches missing requirements, lint problems, and insufficient test coverage before the work is finished.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the pdlc plugin — 38 skills shipped together

Good fit Use it to implement a planned feature, run its tests, check the implementation against the design, apply safe lint fixes, verify coverage, update the service changelog, and mark the related task complete.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/kanfu-panda/pdlc-skills/pdlc-implement
Install

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.

Any agent
npx skills add kanfu-panda/pdlc-skills --skill pdlc-implement
Clone the repo
git clone --depth 1 https://github.com/kanfu-panda/pdlc-skills

Made for: Claude Code.

Or install pdlc, the plugin that ships this one along with the rest of its 38 skills.

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 pdlc-implement

README.md
[![agentmods](https://agentmods.dev/badge/skills/kanfu-panda/pdlc-skills/pdlc-implement/github.svg)](https://agentmods.dev/skills/kanfu-panda/pdlc-skills/pdlc-implement)
Your own site
<a href="https://agentmods.dev/skills/kanfu-panda/pdlc-skills/pdlc-implement"><img src="https://agentmods.dev/badge/skills/kanfu-panda/pdlc-skills/pdlc-implement/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.

agentmods 80×15 button for pdlc-implement

Your own site · 80×15
<a href="https://agentmods.dev/skills/kanfu-panda/pdlc-skills/pdlc-implement"><img src="https://agentmods.dev/badge/skills/kanfu-panda/pdlc-skills/pdlc-implement.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 29 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,427 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 warn 7 Sept 2026
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 24
    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 24
    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.
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 $0.00029 $0.01427
Opus 5 $0.00015 $0.00714
Sonnet 5 $0.00006 $0.00285
Haiku 4.5 $0.00003 $0.00143

Measured 10d ago against content hash 1b71d18783a8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

pdlc-implement 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 10d 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.

skills/pdlc-implement/SKILL.md · 110 lines

How it starts

The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.

按设计文档实现代码

严格按照设计文档和已有的测试用例实现功能代码。

PDLC 前置守卫(不可跳过)

  1. 从用户输入提取功能名称关键词
  2. 按下面的规则搜索与该功能相关的测试代码
  1. 按上述四步走完仍未找到测试代码 → 输出以下后立即中止:
    ⛔ PDLC 守卫:未找到与「<功能名>」相关的测试代码。
    实现代码前必须先编写测试(TDD)。请先运行:
    👉 /pdlc-tdd <功能描述>
    
  2. 找到测试 → 运行测试,确认红灯(失败)。若已全绿:
    • 交互模式:提示"测试已全部通过,可能代码已实现,请确认是否需要继续。"
    • --autonomous 模式:视为流程性确认,默认跳过实现直接收尾(写 auto_decisions[] 留痕),current_stage 推进为 implnext_step=pdlc-reviewlast_phase_result.advanced_to=review(下一阶段短名,非命令名)
  3. 提取功能ID(从设计文档或 PRD),继续
  4. 任务状态关联(如 docs/06_tasks/ 存在任务文件):
    • 匹配含功能ID的任务文件
    • ⬜ 未开始 / 🔄 进行中的任务,标为 🔄,追加 <!-- 开始时间: <今日日期> -->

段一:实现代码

  1. 阅读设计文档docs/02_design/ 对应子目录下的文档,逐字理解
  2. 阅读测试用例:对应服务/应用下的测试代码,理解每条意图
  3. 阅读编码规范docs/00_standards/coding/(未命中 → 提示 consider /pdlc-standard add coding/<topic>
  4. 最少量实现:使所有测试通过的最小代码
  5. 运行测试:确认绿灯
  6. 重构优化:测试通过前提下优化代码结构
  7. 更新服务 CHANGELOG
  8. 任务完结:匹配任务由 🔄 改 ✅,追加 <!-- 完成时间: <今日日期> -->

段二:自检(强制)

实现自检清单(必须全部检查)

  1. 设计偏离检查:重读设计文档,确认没有遗漏接口或功能点
    • 遗漏 → 补充实现并确认测试通过
    • 偏离 → 修正代码或补充设计说明
  2. 编码规范快检:运行项目 lint 工具
    • 可自动修复 → 直接修复
    • 修复后重跑测试确认不破坏功能
    • lint fix 导致失败 → 回滚并记录人工处理
  3. 覆盖率验证: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。
    • 不达标 → 补测试用例并确认通过

段三:修复(单次,不递归)

段四:更新状态机 + 交接

本阶段状态机更新

  • current_stage: implnext_step: pdlc-review —— 仅当本阶段成功时才这样写 (所有 checks 通过且未命中 blocked_reason)。
  • 失败/受阻时不得推进ok=false(含 blocked)→ 按 state-update.md 规则 5, current_stage 保持原值不变advanced_to=nullblocked_reason 写明原因。

    ⚠️ 失败也照写 current_stage: impl 是常见错误:那会让 current_stage 不再表示 「最后一个真正完成的阶段」,外层循环的 stuck-stop 因此失效。

  • last_phase_resultchecks.tests_pass / coverage_pass / lint_clean 取自真跑 unit / coverage / lint 的退出码,不得用自检结果冒充。退出码语义与"跑不了"的处理见下(该文件不存在则回退项目既有约定,并提示 consider 建立 docs/00_standards/test-commands.yml)。

Read the full file on GitHub · 110 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. 10d ago First seen · 110 lines · 29 tokens per session scan A 1b71d18783a8

Subscribe to this mod's changes

pdlc-implement is a skill published in the GitHub repository kanfu-panda/pdlc-skills (13 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 1,427 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.