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 agentmods add skills/nocodemrli/mini-program-engineering-skill-suite/foundationnpx skills add NocodeMrLi/mini-program-engineering-skill-suite --skill foundationgit clone --depth 1 https://github.com/NocodeMrLi/mini-program-engineering-skill-suiteWrote 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/nocodemrli/mini-program-engineering-skill-suite/foundation)<a href="https://agentmods.dev/skills/nocodemrli/mini-program-engineering-skill-suite/foundation"><img src="https://agentmods.dev/badge/skills/nocodemrli/mini-program-engineering-skill-suite/foundation.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00130 | $0.00638 |
| Opus 5 | $0.00065 | $0.00319 |
| Sonnet 5 | $0.00026 | $0.00128 |
| Haiku 4.5 | $0.00013 | $0.00064 |
Grade A, and why
evidence-first-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 5d 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
/evidence-first-engineering — 证据优先工程基础
本技能是领域无关的工程治理基础：任何领域的 Agent 软件工程套件都可以 vendored 引用本层，再叠加自己的领域事实与平台规则。第一个垂直应用是小程序工程开发套件（mini-program-engineering-suite）。
核心组件
- 证据状态模型：状态只能由匹配证据支持，低级状态不得自动推导为高级状态；交付生命周期与用户验收正交。
- 共享工程门禁：事实、范围、实现与调试、验证与发布、恢复与证据、来源独立性六类门禁。
- 判断与确认规则：按动作影响与可逆性决定直接执行、先给依据还是先确认。
- 脱敏与公开包规则：对外分发的脱敏标准、匿名形式与出包门禁。
- 四个交付模板（项目摸排、实施计划、验证报告、发布清单）统一跨阶段输出结构。
使用方式（对垂直套件作者）
- 把
foundation/目录整体 vendored 到你的套件，或以相对链接引用（单仓场景）。 - 你的领域 Skill 引用上述文档作为门禁与状态语言，不复制正文。
- 领域易变事实（平台规则等）放你自己的事实层，并遵循「执行层即时查官方、内容层受控进化」的保鲜原则。
- 引用本层时保留
foundation-source标记，便于升级时同步。
输出契约（继承，不变）
阶段性或最终汇报至少包含：当前结论与阶段；已完成的动作及证据；未执行、未验证或待确认项；改动边界与残余风险；下一步动作。状态词使用证据状态模型；没有对应证据时诚实标注 unknown，不夸大。
What ships with it
9 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.
- guardrails/decision-and-confirmation-rules.md 918 B
- guardrails/engineering-guardrails.md 3.0 KB
- guardrails/evidence-status-model.md 2.9 KB
- guardrails/redaction-policy.md 3.1 KB
- templates/implementation-plan.md 560 B
- templates/project-intake.md 893 B
- templates/release-checklist.md 982 B
- templates/verification-report.md 595 B
- VALIDATE.md 1.5 KB
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.
- 5d ago First seen · 29 lines · 130 tokens per session scan A d99c64260242
evidence-first-engineering is a skill published in the GitHub repository NocodeMrLi/mini-program-engineering-skill-suite (42 stars, last pushed 4d ago), licensed MIT. It adds 130 tokens to every session and 638 once invoked, about $0.0006 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.
Other skills, from other repositories
nature-statistics
Audit, revise, or draft manuscript statistical reporting for Nature / high-impact journal submissions. Use when the user asks to check statistical analysis sections, p values, confidence intervals, sample size, biological versus technical replicates, randomization, blinding, multiple-comparison correction, model…
baoyu-youtube-transcript
Downloads YouTube video transcripts/subtitles and cover images by URL or video ID. Supports multiple languages, translation, chapters, and speaker identification. Caches raw data for fast re-formatting. Use when user asks to "get YouTube transcript", "download subtitles", "get captions", "YouTube字幕", "YouTube封面"…
to-issues
Decompose a PRD and/or SPEC into implementable, vertically-sliced Issues with real blocking edges, then create them in your chosen platform (GitHub or Local). Use after /prd (and optionally /prd-to-spec) to turn requirements into agent-ready tickets. Triggers on: create issues, to-issues, 创建issue, 拆解issue, 生成卡片, 创建卡片…
code-comment-generator
Generates meaningful comments and documentation for code to improve maintenance and readability. Use when adding documentation to Python or Java code, including function/method docstrings, class documentation, inline explanations for complex logic, and code annotations (TODO, FIXME). Analyzes existing comment style in…
code-smell-detector
Identify and report code smells indicating poor design or maintainability issues in Python code, including duplicate code, magic numbers, hardcoded values, God classes, feature envy, inappropriate intimacy, data clumps, primitive obsession, and long parameter lists. Use when conducting code quality audits, preparing…
coverage-enhancer
Analyze existing test suites and source code to suggest additional unit tests that improve test coverage. Use this skill when working with test files and source code to identify untested code paths, missing edge cases, uncovered branches, untested error conditions, and gaps in test coverage. Supports major testing…