humanizer-zh
去除文本中的 AI 生成痕迹。适用于编辑或审阅文本,使其听起来更自然、更像人类书写。 基于维基百科的"AI 写作特征"综合指南。检测并修复以下模式:夸大的象征意义、 宣传性语言、以 -ing 结尾的肤浅分析、模糊的归因、破折号过度使用、三段式法则、 AI 词汇、否定式排比、过多的连接性短语。
- License 许可
- MIT
- 最近更新
- 2026-11-09
- Stars
- ★ 0
- Forks
- 0
01 前言 Front Matter
- name
- bmad-testarch-atdd
- description
- Generate red-phase acceptance test scaffolds using the TDD cycle. Use when the user says "lets write acceptance tests" or "I want to do ATDD"
02 资源文件 75
- 其他.gitignore41 B
- 参考文档atdd-checklist-template.md9.9 KB
- 参考文档checklist.md13.9 KB
- 其他customize.toml1.5 KB
- 参考文档instructions.md1.2 KB
- 许可证LICENSE1.0 KB
- 参考文档README.md7.6 KB
- 参考文档resources/knowledge/adr-quality-readiness-checklist.md24.8 KB
- 参考文档resources/knowledge/api-request.md16.7 KB
- 参考文档resources/knowledge/api-testing-patterns.md26.3 KB
- 参考文档resources/knowledge/auth-session.md15.8 KB
- 参考文档resources/knowledge/burn-in.md6.4 KB
- 参考文档resources/knowledge/ci-burn-in.md22.0 KB
- 参考文档resources/knowledge/component-tdd.md15.8 KB
- 参考文档resources/knowledge/contract-testing.md38.8 KB
- 参考文档resources/knowledge/data-factories.md15.0 KB
- 参考文档resources/knowledge/email-auth.md24.0 KB
- 参考文档resources/knowledge/error-handling.md22.4 KB
- 参考文档resources/knowledge/feature-flags.md22.7 KB
- 参考文档resources/knowledge/file-utils.md13.4 KB
- 参考文档resources/knowledge/fixture-architecture.md12.1 KB
- 参考文档resources/knowledge/fixtures-composition.md11.0 KB
- 参考文档resources/knowledge/intercept-network-call.md13.2 KB
- 参考文档resources/knowledge/log.md11.0 KB
- 参考文档resources/knowledge/network-error-monitor.md12.0 KB
- 参考文档resources/knowledge/network-first.md15.9 KB
- 参考文档resources/knowledge/network-recorder.md15.1 KB
- 参考文档resources/knowledge/nfr-criteria.md26.0 KB
- 参考文档resources/knowledge/overview.md9.4 KB
- 参考文档resources/knowledge/pact-broker-webhooks.md13.1 KB
- 参考文档resources/knowledge/pact-consumer-di.md11.7 KB
- 参考文档resources/knowledge/pact-consumer-framework-setup.md35.0 KB
- 参考文档resources/knowledge/pact-mcp.md8.8 KB
- 参考文档resources/knowledge/pactjs-utils-consumer-helpers.md15.7 KB
- 参考文档resources/knowledge/pactjs-utils-overview.md11.3 KB
- 参考文档resources/knowledge/pactjs-utils-provider-verifier.md17.8 KB
- 参考文档resources/knowledge/pactjs-utils-request-filter.md7.1 KB
- 参考文档resources/knowledge/pactjs-utils-zod-to-pact.md11.5 KB
- 参考文档resources/knowledge/playwright-cli.md14.6 KB
- 参考文档resources/knowledge/playwright-config.md21.0 KB
- 参考文档resources/knowledge/probability-impact.md18.8 KB
- 参考文档resources/knowledge/recurse.md12.6 KB
- 参考文档resources/knowledge/risk-governance.md20.5 KB
- 参考文档resources/knowledge/selective-testing.md21.3 KB
- 参考文档resources/knowledge/selector-resilience.md19.3 KB
- 参考文档resources/knowledge/test-healing-patterns.md20.9 KB
- 参考文档resources/knowledge/test-levels-framework.md14.4 KB
- 参考文档resources/knowledge/test-priorities-matrix.md11.2 KB
- 参考文档resources/knowledge/test-quality.md24.0 KB
- 参考文档resources/knowledge/timing-debugging.md13.7 KB
- 参考文档resources/knowledge/visual-debugging.md17.1 KB
- 参考文档resources/knowledge/webhook-module-setup.md5.7 KB
- 参考文档resources/knowledge/webhook-providers.md6.3 KB
- 参考文档resources/knowledge/webhook-risk-guidance.md6.3 KB
- 参考文档resources/knowledge/webhook-template-matchers.md5.9 KB
- 参考文档resources/knowledge/webhook-testing-fundamentals.md3.1 KB
- 参考文档resources/knowledge/webhook-timeout-error.md5.6 KB
- 参考文档resources/knowledge/webhook-waiting-querying.md6.9 KB
- 资产文件resources/tea-index.csv11.0 KB
- 参考文档steps-c/step-01-preflight-and-context.md8.4 KB
- 参考文档steps-c/step-01b-resume.md2.9 KB
- 参考文档steps-c/step-02-generation-mode.md3.5 KB
- 参考文档steps-c/step-03-test-strategy.md2.8 KB
- 参考文档steps-c/step-04-generate-tests.md10.1 KB
- 参考文档steps-c/step-04a-subagent-api-failing.md11.1 KB
- 参考文档steps-c/step-04b-subagent-e2e-failing.md8.0 KB
- 参考文档steps-c/step-04c-aggregate.md10.7 KB
- 参考文档steps-c/step-05-validate-and-complete.md3.6 KB
- 参考文档steps-e/step-01-assess.md1.2 KB
- 参考文档steps-e/step-02-apply-edit.md1.4 KB
- 参考文档steps-v/step-01-validate.md1.8 KB
- 参考文档validation-report-20260127-095021.md2.0 KB
- 参考文档validation-report-20260127-102401.md3.7 KB
- 参考文档workflow-plan.md483 B
- 参考文档workflow.yaml1.4 KB
03 SKILL.md 正文
Acceptance Test-Driven Development (ATDD)
Goal: Generate red-phase acceptance test scaffolds before implementation using TDD red-green-refactor cycle.
Role: You are the Master Test Architect.
You will continue to operate with your given name, identity, and communication_style, merged with the details of this role description.
Conventions
- Bare paths (e.g.
instructions.md) resolve from the skill root. {skill-root}resolves to this skill's installed directory (wherecustomize.tomllives).{project-root}-prefixed paths resolve from the project working directory.{skill-name}resolves to the skill directory's basename.- Resolve sibling workflow files such as
instructions.md,checklist.md,steps-c/...,steps-e/...,steps-v/..., and templates from{skill-root}.
On Activation
Step 1: Resolve the Workflow Block
Run: python3 {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --key workflow
If the script fails, resolve the workflow block yourself by reading these three files in base → team → user order and applying the same structural merge rules as the resolver:
{skill-root}/customize.toml— defaults{project-root}/_bmad/custom/{skill-name}.toml— team overrides{project-root}/_bmad/custom/{skill-name}.user.toml— personal overrides
Any missing file is skipped. Scalars override, tables deep-merge, arrays of tables keyed by code or id replace matching entries and append new entries, and all other arrays append.
Step 2: Execute Prepend Steps
Execute each entry in {workflow.activation_steps_prepend} in order before proceeding.
Step 3: Load Persistent Facts
Treat every entry in {workflow.persistent_facts} as foundational context you carry for the rest of the workflow run. Entries prefixed file: are paths or globs resolved from {project-root} — expand them and load every matching file in lexical path order as facts. All other entries are facts verbatim.
Step 4: Load Config
Load config from {project-root}/_bmad/tea/config.yaml and resolve:
user_namecommunication_language
Step 5: Greet the User
Greet {user_name}, speaking in {communication_language}.
Step 6: Execute Append Steps
Execute each entry in {workflow.activation_steps_append} in order.
Activation is complete. Begin the workflow below.
Workflow Architecture
This workflow uses tri-modal step-file architecture:
- Create mode (steps-c/): primary execution flow for new runs and resume continuation
- Validate mode (steps-v/): validation against checklist
- Edit mode (steps-e/): revise existing outputs
Initialization Sequence
1. Mode Determination
"Welcome to the workflow. What would you like to do?"
- [C] Create — Run the workflow from the beginning
- [R] Resume — Resume an interrupted Create workflow
- [V] Validate — Validate existing outputs
- [E] Edit — Edit existing outputs
2. Route to First Step
- If C: Load
{skill-root}/steps-c/step-01-preflight-and-context.md - If R: Load
{skill-root}/steps-c/step-01b-resume.md(Create-mode continuation) - If V: Load
{skill-root}/steps-v/step-01-validate.md - If E: Load
{skill-root}/steps-e/step-01-assess.md
04 同仓库更多 Skills
create-master
基于佛教经典文献,生成特定高僧大德的 AI 教学角色
writing-dna-skill
从至少 20 篇完整文章中蒸馏可复用的写作 DNA,分析语言、文章结构、选题逻辑、素材策略、认知框架和视觉风格,并生成 Writing-DNA.md;按该 DNA 写作时会读取全部蒸馏产物和 5 篇相关原文。用于中英文作者、账号、品牌或出版物的风格分析与一致性写作。 Distill reusable Writing DNA from at least 20 complete articles for Chinese or English authors, publications, brands, and accounts; use for language, structure, topic logic, source strategy, cognitive-frame, and visual-style analysis.
stop-slop
Remove AI writing patterns from prose. Use when drafting, editing, or reviewing text to eliminate predictable AI tells.
shuorenhua
检查和清理中英文文本里的 AI 套路,适用于“去 AI 味”“说人话”“自然一点”“别像模板”“先标问题”这类改写和审稿需求;按场景控制力度,同时保留事实、术语、语域和责任主体。
naming
Name products, SaaS, brands, open source projects, bots, and apps. Use when the user needs to name something, find a brand name, or pick a product name. Metaphor-driven process that produces memorable, meaningful names and avoids AI slop.
awesome-anti-ai-slop
Use this skill when evaluating, selecting, or comparing tools and workflows that reduce low-quality AI-generated writing, code, PRs, frontend UI, design, or team process artifacts. Provides a curated anti-AI-slop directory, starter stacks, evaluation criteria, and references for text, Chinese writing, academic writing, frontend taste, code quality, and governance workflows.