sies-engineering

An engineering workflow for changing software while keeping the work tied to its goal and supported by suitable evidence.

In plain words
What is it for?
Use it when writing, fixing, refactoring, debugging, optimizing, or shipping code, from clarifying the goal through final regression checks.
Why use it?
It helps decide what to investigate, test, refine, and verify based on uncertainty and risk instead of following unnecessary steps.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/zhpeng24/devkit/sies-engineering
Any agent
npx skills add zhpeng24/devkit --skill sies-engineering
Clone the repo
git clone --depth 1 https://github.com/zhpeng24/devkit

Made for: Claude Code, Codex.

Per session 46 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,203 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00046 $0.01203
Opus 5 $0.00023 $0.00602
Sonnet 5 $0.00009 $0.00241
Haiku 4.5 $0.00005 $0.00120

Measured 2d ago against content hash 43c53043e637, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

sies-engineering 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 2d 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/sies-engineering/SKILL.md · 94 lines

How it starts

The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.

SIES Engineering

Overview

SIES(Self-Improving Engineering System)是 Devkit 的默认研发序列。 它用“目标是否被证据支持、关键不确定性是否被关闭”衡量进展,而不是用测试数量或流程动作衡量进展。

测试不会被取消。测试是验证目标一致性、稳定契约和真实风险的重要证据,但 TDD 只是 Engineering 阶段内按需选择的策略。

Start From State

  1. 读取用户目标、仓库状态、现有 Issue/PR/ADR 和相关实现。
  2. 读取已发现的 .devkit/project.jsonprojectsiescommands;参数提供项目默认值,不能降低事实风险对应的 SIES 深度。
  3. 恢复已有 Goal Contract 与证据;不要重新执行已经关闭的阶段。
  4. 判断复杂度、风险和不确定性,决定每个阶段需要多深。
  5. 用户说“直接改”时压缩产物和沟通,不反复确认;仍保留最小目标、风险判断和交付验证。

GitHub 参与任务时,读取 references/github-state.md。选择 Evaluation、测试和回归策略时, 读取 references/evidence-strategy.md

Default Sequence

Phase Required outcome
Goal 明确结果、成功信号、非目标、约束和关键未知项
Explore 找到值得验证的路径;确定性工作可确认现有路径
Prototype 用最小实现或实验回答关键问题
Evaluate 用预先声明的证据判断目标一致性
Refine 采用、修改、回退或停止,并记录理由
Engineer 把选定方向转成生产质量实现
Regress 完整增量后运行目标测试;交付前统一回归
Learn 只沉淀重复、高影响或明确可复用的经验

Evaluation 可以返回 Explore 或 Prototype。证据不支持目标时,不得仅靠增加测试把实现 推进到 Engineering。

Scale, Do Not Replace

Level Depth
L0 阶段内联完成;通常以用户原话、代码定位、diff 或静态检查为证据
L1 简短 Goal Contract;通常验证一个候选方案;Issue 可选
L2 持久化目标、关键未知项、评价契约和决定;必要时比较方案
L3 完整探索与原型证据;重要架构决定写 ADR;高风险验证前移

阶段可以合并。只有不存在会改变决定的未知项时才可以快速通过,不能为了制造流程感创建 无价值文档、Issue、测试或评审。

Testing Contract

  • 每个持久测试必须追溯到成功信号、稳定契约或真实失败模式。
  • 可复现 bug、公共契约、安全、迁移、并发和兼容性风险适合前移针对性测试。
  • 架构、产品方向或体验仍不确定时,先用 Prototype 和 Evaluation;不要让测试过早固化假设。
  • Prototype 的探针和断言默认是临时证据,只有值得长期保护的行为才晋升为回归测试。
  • 完成一个完整逻辑批次后运行目标测试;交付前运行一次与风险匹配的回归。
  • 相关输入和实现未变化时,不重复运行同一套验证。
  • 测试通过但目标证据失败,Evaluation 仍然失败。

Orchestration

  • 产品目标仍模糊时使用 github-product-manager 建立 Goal Contract。
  • 需要 GitHub 状态时使用 github-create-issuegithub-issue-workflow,由当前阶段决定。
  • 使用适合项目语言的 friendly-* 或仓库原生规范。
  • 只有复杂度和未知项确实需要时才调用设计、计划、调试或独立评审 skill。
  • 不自动调用 blanket TDD 流程;先通过 Test Strategy Gate 决定是否采用 TDD。

Completion Contract

交付前确认:

  • 最终结果能映射到 Goal Contract 的成功信号;
  • 关键架构决定有证据,而不是只剩未经验证的偏好;
  • Engineering 没有混入未评价的原型假设;
  • 测试与其他证据覆盖关键目标和风险;
  • 回归在完整批次后执行,未验证范围被明确说明;
  • 只有满足晋升条件的经验进入 Learning Candidate。

Read the full file on GitHub · 94 lines

Files

What ships with it

3 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.

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. 2d ago First seen · 94 lines · 46 tokens per session scan A 43c53043e637

Subscribe to this mod's changes

sies-engineering is a skill published in the GitHub repository zhpeng24/devkit (2 stars, last pushed 1mo ago), licensed MIT. It adds 46 tokens to every session and 1,203 once invoked, about $0.0002 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.

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