kb-tricks

Agent-native skill suite for building, maintaining, querying, reviewing, testing, migrating, and onboarding codebases with AI.

A collection of composable AI skills designed around Cognitive Mapping, Adversarial Validation, and Mixture-of-Experts patterns. These skills cover the full knowledge lifecycle: plan → build → maintain → query → audit → review design → review code → review tests → incident postmortem → migrate → changelog → onboard.

Design Direction

kb-tricks is evolving from a "KB generation" skill suite into a controllable, auditable, incrementally maintainable AI development context system.

The KB is an auxiliary context layer, not the authority for a repository. Source code, configuration, tests, release artifacts, and maintained human-facing docs remain authoritative. The KB should route questions, compress context, explain cross-module contracts, and expose uncertainty.

The next-phase design is tracked in ROADMAP.md. The most important operating contracts are:

Bounded by default: kb-build and kb-update process small slices unless the user explicitly requests full execution.
Clean source for authoritative KB: dirty or untracked source content is blocked from formal KB writes by default and belongs in draft or impact artifacts.
Artifact boundaries: projects can declare include/exclude/release-excluded paths through .agent/kb/config.yaml.
Dirty-aware fingerprints: KB metadata records Git state and content hashes, not only commit IDs.
Existing docs comparison: KB planning should compare existing docs before creating a parallel documentation island.
Provenance-first answers: query results distinguish KB, source fallback, existing docs, and inference.

Specs And Tooling

spec/KB_SPEC.md: stable artifact schema for .agent/kb/config.yaml, KB_PLAN.md, KB frontmatter, validation files, glossary, and index.json.
RELEASE.md: release boundary, install smoke, and packaging verification notes.
templates/: starter templates for config, manifest, KB docs, validation artifacts, and query answers.
tools/kb_scaffold.py: dependency-free scaffold helper for installing starter config, manifest, and reserved KB directories into a target repository.
tools/kb_manifest.py: dependency-free selector for bounded KB_PLAN.md task execution.
tools/kb_docs.py: dependency-free existing-docs inventory, manifest comparison coverage, dead-link reporting, and duplicate-hint helper.
tools/kb_audit.py: dependency-free deterministic audit helper for setup checks, hashes, dirty-state checks, links, manifest coverage, validation artifacts, policy exit codes, and optional index.json generation.
tools/kb_fingerprint.py: dependency-free helper for generating and checking dirty-aware source fingerprints.
tools/kb_impact.py: dependency-free diff-first changed-file to Manifest task mapper for kb-update.
tools/kb_update_plan.py: dependency-free read-only planner that turns impact data into bounded update actions, blockers, docs reviews, and new KB candidates.
tools/kb_query_lint.py: dependency-free provenance linter for kb-query answers.
tools/release_smoke.py: local/CI release smoke runner for tests, CLI import checks, scaffold dry-run, query lint, and whitespace checks.

Installed CLI example:

kb self-check --json
kb scaffold --repo /path/to/project --dry-run
kb manifest --repo /path/to/project --slice 1 --json
kb docs --repo /path/to/project --check-manifest --check-links
kb impact --repo /path/to/project --staged --json
kb impact --repo /path/to/project --worktree --json
kb impact --repo /path/to/project --base main --json
kb impact --repo /path/to/project --since HEAD~1 --json
kb update-plan --repo /path/to/project --staged --json
kb update-plan --repo /path/to/project --worktree --draft --json
kb query-lint --repo /path/to/project templates/query-answer.md
kb audit --repo /path/to/project --fail-on stale --fail-on dead-links --min-score B
kb fingerprint --repo /path/to/project --check .agent/kb/release/packaging.md

Source checkout fallback:

python3 tools/kb_audit.py --repo /path/to/project --write-index .agent/kb/index.json
python3 tools/kb_impact.py --repo /path/to/project --files src/cli/release.ts --json
python3 tools/kb_update_plan.py --repo /path/to/project --since HEAD~1 --json
python3 tools/kb_query_lint.py templates/query-answer.md

Minimal CI smoke chain:

python3 -B tools/release_smoke.py

Skills

🚀 kb-init — One-Click Orchestration Pipeline

An orchestrator meta-skill that autonomously drives the kb-plan -> Human Confirmation -> kb-build pipeline, providing a seamless "Plan & Execute" experience.

🗺️ kb-plan — Knowledge Base Manifest Planning

Scans the repository to identify high-signal boundaries and generates a structured long-lived KB_PLAN.md manifest.

Macro Discovery: Reads manifest files and directory trees without deep-diving into code
Artifact Boundary Config: Respects .agent/kb/config.yaml include/exclude/releaseExcluded rules
Existing Docs Comparison: Compares existing docs before creating a parallel KB topic
Signal-to-Noise Isolation: Explicitly filters out boilerplate, tests, and dependencies
Task Chunking: Breaks down the documentation process into manageable, file-by-file tasks

🧠 kb-build — Manifest Execution & KB Construction

Executes the KB_PLAN.md manifest in bounded slices to build a high-signal knowledge base.

Bounded Execution: Defaults to slice 1; full execution requires explicit until-complete
Manifest Execution: Iteratively processes tasks from the manifest ensuring no loss of context
Cognitive Mapping: Captures cross-module contracts and design trade-offs, not boilerplate
Mermaid Diagrams: Mandatory for complex API interaction chains
Semantic Glossary: Terms + synonyms as retrieval anchors for RAG
Context-Cleared Validation: 3-D adversarial questions scored against KB-only access to prevent hallucination
Dirty-Aware Fingerprinting: Git state + content hashes for rot detection

🔄 kb-update — Incremental Knowledge Maintenance

Keeps an existing KB fresh via fingerprint diffing and chunked scoped rewrites.

Diff-First Scope: Starts from staged, worktree, base, since, or files scopes when provided, then falls back to full fingerprint scan
Fingerprint Diff: Detects stale/orphaned KB files by comparing recorded Git state and content hashes
Impact Analysis: Classifies changes as Patch / Breaking / New Module
Cascade Check: Traces SSOT links to find ripple effects
Chunked Execution: Processes 1-2 stale docs per iteration to prevent context overflow
Manifest Sync: Keeps KB_PLAN.md lifecycle state in sync when files are added, updated, merged, or deprecated
Context-Cleared Validation: Reduced-scope self-evaluation (1-2 questions per changed doc)

🔎 kb-query — Knowledge Base Query & Source Fallback

Anti-hallucination knowledge retrieval with automatic source code verification.

Glossary-Driven Lookup: Semantic trigger matching via GLOSSARY.md for precise document routing
Graph Walk: Traverses SSOT links to collect full context — never scans the entire KB
Source Code Fallback: When KB lacks concrete API signatures or logic details, automatically reads source code to verify — never fabricates
Provenance Contract: Every factual answer line marks KB, source fallback, or existing docs; inference is isolated and lintable
Blindspot Reporting: Honestly reports gaps instead of hallucinating

🩺 kb-audit — Knowledge Base Health Check

Token-efficient KB health dashboard using metadata-only scanning.

Coverage Check: Cross-references KB_PLAN.md tasks against actual KB files
Freshness Check: Batch dirty-aware fingerprint validation via Frontmatter, Git state, and content hashes
Link Integrity: Validates all SSOT internal links for dead references
Glossary Coverage: Checks glossary entries point to existing files
Health Report: A/B/C/D/F scoring with actionable recommendations

📐 moe-design — Mixture-of-Experts Design Review

Shift-left architecture review before any code is written.

Layer 1 (5 Design Experts, parallel): Feasibility · Scalability · Complexity Risk · Security & Compliance · Operational Cost
Layer 2 (Domain Expert): Dynamically generated per project paradigm
Layer 3 (KB Consistency Expert, conditional): Cross-checks design proposals against existing architectural patterns in KB
Aggregator: Design Readiness Rating (🔴 Blocked → 🟠 Major Revisions → 🟡 Minor Revisions → 🟢 Ready → ✅ Excellent)

🔍 moe-cr — Mixture-of-Experts Code Review

Multi-dimensional code review using specialized expert prompts.

Diff Triage Filter: Pre-classifies files as 🟢 Trivial / 🟡 Standard / 🔴 Critical to skip noise and boost critical findings
Layer 1 (6 Base Experts, parallel): Architecture · Logic Boundary · Security · Performance · Testability · Maintainability
Layer 2 (Domain Expert): Dynamically generated per project paradigm (REST API, compiler, data pipeline, etc.)
Layer 3 (KB Expert, conditional): Cross-checks diffs against KB knowledge chains with Freshness Hard Gate — skips if KB is stale
- Direct Impact → KB-Action: UPDATE (auto)
- Indirect Impact → KB-Action: REVIEW (user decision)
Aggregator: Dedup → Critical File Boost → Conflict Resolution → Risk Rating (🔴🟠🟡🟢✅)

🎓 kb-onboard — Knowledge-Driven Onboarding

Generates guided learning paths for new team members from existing KB.

Topology-Sorted Reading Path: Orders KB docs by dependency graph (foundations first)
Core Concept Summaries: 2-3 sentence "What You Need to Know" per module
Comprehension Quizzes: Architecture / Design Intent / Boundary questions with reference answers
Personalization: Optional focus on specific modules for targeted roles

🧪 moe-test — Mixture-of-Experts Test Review

Multi-dimensional test quality review with KB contract cross-check.

Layer 1 (4 Test Experts, parallel): Coverage Gaps · Assertion Quality · Test Maintainability · Boundary Conditions
Layer 2 (Framework Expert): Dynamically generated per test framework (Jest, pytest, Go testing, etc.)
Layer 3 (KB Contract Coverage, conditional): Cross-checks KB-documented contracts against actual test coverage
Aggregator: Test Health Rating (🔴 Unsafe → 🟠 Weak → 🟡 Adequate → 🟢 Good → ✅ Excellent)

🚑 moe-postmortem — Mixture-of-Experts Incident Postmortem

Structured incident analysis with root cause tracing and KB fault propagation mapping.

Layer 1 (5 Postmortem Experts, parallel): Root Cause (5 Whys) · Blast Radius · Timeline Reconstruction · Defense Gap Analysis · Systemic Fix Recommendations
Layer 2 (KB Fault Propagation, conditional): Traces fault path through KB knowledge chains with Mermaid visualization
Report: Standardized postmortem with MTTD/MTTR, 5 Whys, action items, and propagation diagram

🔀 kb-migrate — Large-Scale Migration Planning

KB-driven architecture migration planning with safe execution ordering.

Impact Matrix: Classifies each module as ⬜ Unaffected / 🟡 Adaptable / 🟠 Rewrite / 🔴 Deprecate
Dependency-Sorted Execution: Leaves-first, core-last migration sequence
Migration Blueprint: MIGRATION_PLAN.md with phased execution plan
Post-Migration Guidance: Auto-triggers kb-update + kb-audit + moe-test

📝 kb-changelog — Knowledge Base Changelog

Auto-generates human-readable KB change summaries after updates.

Diff-First Strategy: Reads only git diff output, never full file content
Semantic Summarization: 1-2 sentence per-file change descriptions
Incremental Append: Appends new entries to CHANGELOG.md (newest first)

How They Connect

                      ┌──── moe-design ◄── RFC / 设计文档
                      │
kb-init (Orchestrator) ──drives──┐
                                 ▼
kb-plan ──manifest──→ kb-build ──fingerprints──→ kb-update ──→ kb-changelog
                           │                          ↑
                           ├──── KB ────→ moe-cr ─────┘ (KB-Action: UPDATE)
                           ├──── KB ────→ moe-test (契约 ↔ 测试交叉验证)
                           ├──── KB ────→ moe-postmortem (故障传播追踪)
                           ├──── KB ────→ kb-migrate (迁移影响分析)
                           ├──── KB ────→ kb-query (查询 + 源码回退)
                           ├──── KB ────→ kb-audit (健康体检)
                           └──── KB ────→ kb-onboard (新人引导)

License

MIT © Glen Li

kb-tricks

面向 Agent 原生设计的技能套件：用 AI 构建、维护、查询、审查、测试、迁移知识库并引导新人入门。

一组可组合的 AI 技能，围绕认知地图（Cognitive Mapping）、**对抗性验证（Adversarial Validation）和混合专家（Mixture-of-Experts）**模式设计。覆盖知识全生命周期：规划 → 构建 → 维护 → 查询 → 体检 → 设计审查 → 代码审查 → 测试审查 → 事故复盘 → 迁移规划 → 变更日志 → 新人入门。

设计方向

kb-tricks 正在从“生成 KB 的 skill 套件”演进为一个可控、可审计、可增量维护的 AI 开发上下文系统。

KB 不是代码仓库的权威来源。源码、配置、测试、发布产物和面向人的维护文档仍然是权威。KB 的职责是路由问题、压缩上下文、解释跨模块契约，并显式暴露不确定性。

下一阶段设计记录在 ROADMAP.md。最重要的运行契约是：

默认小步执行：kb-build 和 kb-update 只处理小切片，除非用户明确要求全量执行。
正式 KB 只基于干净源码：dirty 或 untracked 源码默认不能写入正式 KB，只能进入草稿或影响分析产物。
产物边界配置：项目可以通过 .agent/kb/config.yaml 声明 include、exclude 和 releaseExcluded 路径。
dirty-aware 指纹：KB 元数据记录 Git 状态和内容哈希，而不只是 commit ID。
已有文档对比：规划 KB 前先比较现有文档，避免制造第二套文档孤岛。
来源优先回答：查询结果必须区分 KB、源码回退、现有文档和推断。

规范与工具

spec/KB_SPEC.md：定义 .agent/kb/config.yaml、KB_PLAN.md、KB frontmatter、验证文件、词汇表和 index.json 的稳定结构。
RELEASE.md：记录 release 边界、安装 smoke 和打包验证流程。
templates/：提供 config、Manifest、KB 文档、验证产物和查询回答模板。
tools/kb_scaffold.py：无依赖的初始化辅助工具，用于把 starter config、Manifest 和 KB 保留目录安装到目标仓库。
tools/kb_manifest.py：无依赖的 KB_PLAN.md 小步任务选择器，用于落实 bounded execution。
tools/kb_docs.py：无依赖的现有文档清单与 Manifest Docs Comparison 覆盖率检查工具。
tools/kb_audit.py：无依赖的确定性审计辅助工具，负责 hash、dirty 状态、链接、Manifest 覆盖、验证产物、策略 exit code 和可选 index.json 生成。
tools/kb_fingerprint.py：无依赖的 dirty-aware source fingerprint 生成与检查工具。
tools/kb_impact.py：无依赖的 diff-first changed files 到 Manifest 任务影响面映射工具，供 kb-update 使用。
tools/kb_update_plan.py：无依赖的只读更新规划工具，把 impact 结果转换为 bounded actions、阻塞项、docs review 和新 KB 候选。
tools/kb_query_lint.py：无依赖的 kb-query 回答来源类型与推断隔离检查工具。
tools/release_smoke.py：本地/CI release smoke 入口，统一运行测试、CLI import 检查、scaffold dry-run、query lint 和 whitespace 检查。

安装后的 CLI 示例：

kb self-check --json
kb scaffold --repo /path/to/project --dry-run
kb manifest --repo /path/to/project --slice 1 --json
kb docs --repo /path/to/project --check-manifest
kb impact --repo /path/to/project --staged --json
kb impact --repo /path/to/project --worktree --json
kb impact --repo /path/to/project --base main --json
kb impact --repo /path/to/project --since HEAD~1 --json
kb update-plan --repo /path/to/project --staged --json
kb update-plan --repo /path/to/project --worktree --draft --json
kb query-lint templates/query-answer.md
kb audit --repo /path/to/project --fail-on stale --fail-on dead-links --min-score B
kb fingerprint --repo /path/to/project --check .agent/kb/release/packaging.md

源码 checkout fallback：

python3 tools/kb_audit.py --repo /path/to/project --write-index .agent/kb/index.json
python3 tools/kb_impact.py --repo /path/to/project --files src/cli/release.ts --json
python3 tools/kb_update_plan.py --repo /path/to/project --since HEAD~1 --json
python3 tools/kb_query_lint.py templates/query-answer.md

最小 CI smoke chain：

python3 -B tools/release_smoke.py

技能一览

🚀 kb-init — 一键编排流水线

一个"元技能 (Meta-Skill)"，它负责自主编排 kb-plan -> 人类确认 -> kb-build 这一完整的"规划与执行 (Plan & Execute)"流水线，提供顺滑的交互体验。

🗺️ kb-plan — 知识库 Manifest 规划

通过宏观扫描代码库，区分高信噪比边界，并生成结构化、可长期维护的 KB_PLAN.md Manifest。

宏观探索：阅读配置和目录树，不深陷具体代码细节
产物边界配置：遵守 .agent/kb/config.yaml 的 include/exclude/releaseExcluded 规则
已有文档对比：创建 KB 主题前先比较现有 docs，避免文档孤岛
信噪比隔离：显式过滤样板代码、测试文件和外部依赖
任务分块：将文档化过程拆解为可管理、防上下文溢出的逐文件任务

🧠 kb-build — Manifest 执行与知识库构建

按小切片执行 KB_PLAN.md Manifest，构建高信噪比、低维护成本的知识库。

默认小步执行：默认 slice 1；全量执行必须显式声明 until-complete
Manifest 执行：迭代式处理 Manifest 中的任务，确保不丢失上下文
认知地图：记录跨模块契约和设计权衡，而非样板代码
Mermaid 图谱：复杂 API 交互链路强制要求可视化
语义触发词典：术语 + 同义词，作为 RAG 检索锚点
清空上下文验证：架构/设计意图/边界三维提问，仅允许基于 KB 作答以防幻觉
dirty-aware 源码指纹：Git 状态 + 内容哈希用于知识腐烂检测

🔄 kb-update — 增量知识维护

通过指纹比对和分块范围性重写，保持知识库的时效性。

差异优先范围：优先从 staged、worktree、base、since 或 files 范围入口计算影响面，再回退到全量指纹扫描
指纹比对：通过 Git 状态和内容哈希检测过期/孤立的 KB 文件
影响分析：将变更分类为补丁型 / 破坏型 / 新模块
级联检查：追踪 SSOT 链接发现连锁影响
分块执行：每次仅处理 1~2 个过期文档，防止上下文溢出
Manifest 同步：新增、更新、合并或废弃文件时同步更新 KB_PLAN.md 生命周期状态
清空上下文验证：缩小范围的自我评估（每个变更文档 1-2 个问题）

🔎 kb-query — 知识库查询与源码回退

带有反幻觉机制的知识库检索，不完整时自动回退到源码验证。

词汇表驱动检索：通过 GLOSSARY.md 语义触发匹配精准定位文档
图谱遍历：沿 SSOT 链接按需展开上下文——从不全量扫描 KB
源码回退：当 KB 缺乏具体 API 签名或逻辑细节时，自动读取源码验证——绝不捏造
来源契约：每条事实回答标注 KB、源码回退或现有 docs；推断隔离并可被 lint
盲区上报：诚实报告知识空白，而非幻觉填充

🩺 kb-audit — 知识库健康体检

省 Token 的元数据扫描式健康仪表盘。

覆盖率检查：交叉对比 KB_PLAN.md 任务与实际 KB 文件
新鲜度检查：基于 Frontmatter、Git 状态和内容哈希的 dirty-aware 批量指纹校验
链接完整性：验证所有 SSOT 内部链接是否存在死链
词汇表覆盖：检查词汇表条目是否指向存在的文件
健康报告：A/B/C/D/F 评级 + 可操作的改进建议

📐 moe-design — 混合专家设计审查

在代码编写之前对架构提案进行"左移 (Shift-Left)"审查。

Layer 1（5 个设计专家，并行）：可行性 · 可扩展性 · 复杂度风险 · 安全与合规 · 运维成本
Layer 2（领域专家）：根据项目范式动态生成
Layer 3（KB 一致性专家，条件触发）：将设计提案与 KB 中现有的架构模式进行交叉验证
聚合器：设计就绪度评级（🔴 返工 → 🟠 重大修改 → 🟡 小幅修改 → 🟢 就绪 → ✅ 优秀）

🔍 moe-cr — 混合专家代码审查

使用专业化的专家提示词进行多维度代码审查。

差异分级过滤器：预先将文件分为 🟢 琐碎 / 🟡 标准 / 🔴 关键，跳过噪音并提升关键发现的严重级别
Layer 1（6 个基础专家，并行）：架构 · 逻辑边界 · 安全性 · 性能 · 可测试性 · 可维护性
Layer 2（领域专家）：根据项目范式动态生成（REST API、编译器、数据管道等）
Layer 3（KB 专家，条件触发）：将 diff 与 KB 知识链路交叉检查，并带有新鲜度硬性门控 — KB 过期则直接跳过
- 直接影响 → KB-Action: UPDATE（自动更新）
- 间接影响 → KB-Action: REVIEW（由用户决定）
聚合器：去重 → 关键文件提升 → 冲突仲裁 → 风险评级（🔴🟠🟡🟢✅）

🎓 kb-onboard — 知识库驱动新人引导

利用现有知识库为新团队成员生成有引导性的学习路径。

拓扑排序阅读路径：按知识依赖图排序（基础优先）
核心概念速览：每个模块 2-3 句"你需要知道什么"摘要
理解检验测验：架构 / 设计意图 / 边界条件问题 + 参考答案
个性化扩展：可选针对特定角色聚焦相关模块

🧪 moe-test — 混合专家测试审查

多维度测试质量审查，与 KB 契约交叉验证。

Layer 1（4 个测试专家，并行）：覆盖率缺口 · 断言质量 · 测试可维护性 · 边界条件
Layer 2（框架专家）：根据测试框架动态生成（Jest、pytest、Go testing 等）
Layer 3（KB 契约覆盖，条件触发）：将 KB 记录的契约与实际测试交叉比对
聚合器：测试健康度评级（🔴 不安全 → 🟠 薄弱 → 🟡 合格 → 🟢 良好 → ✅ 优秀）

🚑 moe-postmortem — 混合专家事故复盘

结构化事故分析：根因追踪 + KB 故障传播路径映射。

Layer 1（5 个复盘专家，并行）：根因分析 (5 Why) · 影响面评估 · 时间线重建 · 防御缺失分析 · 系统性修复建议
Layer 2（KB 故障传播，条件触发）：沿 KB 知识链追踪故障传播路径 + Mermaid 可视化
报告：标准化复盘文档，含 MTTD/MTTR、5 Why、行动项和传播图

🔀 kb-migrate — 大规模迁移规划

基于 KB 的架构迁移规划，安全排序执行。

影响矩阵：将每个模块分类为 ⬜ 无影响 / 🟡 可适配 / 🟠 需重写 / 🔴 需废弃
依赖排序执行：叶子优先、核心最后的迁移顺序
迁移蓝图：MIGRATION_PLAN.md 分阶段执行计划
迁移后联动：自动触发 kb-update + kb-audit + moe-test

📝 kb-changelog — 知识库变更日志

更新后自动生成人类可读的 KB 变更摘要。

差异优先策略：仅读取 git diff 输出，从不精读全文
语义摘要：每个文件 1-2 句变更描述
增量追加：新条目追加到 CHANGELOG.md 顶部（最新在前）

技能之间的关联

                    ┌──── moe-design ◄── RFC / 设计文档
                    │
kb-init (编排器) ──驱动──┐
                        ▼
kb-plan ──Manifest──→ kb-build ──指纹──→ kb-update ──→ kb-changelog
                      │                    ↑
                      ├──── KB ────→ moe-cr ─┘ (KB-Action: UPDATE)
                      ├──── KB ────→ moe-test (契约 ↔ 测试交叉验证)
                      ├──── KB ────→ moe-postmortem (故障传播追踪)
                      ├──── KB ────→ kb-migrate (迁移影响分析)
                      ├──── KB ────→ kb-query (查询 + 源码回退)
                      ├──── KB ────→ kb-audit (健康体检)
                      └──── KB ────→ kb-onboard (新人引导)

许可证

MIT © Glen Li

Name		Name	Last commit message	Last commit date
Latest commit History 22 Commits
.agent/kb		.agent/kb
.github/workflows		.github/workflows
kb-audit		kb-audit
kb-build		kb-build
kb-changelog		kb-changelog
kb-init		kb-init
kb-migrate		kb-migrate
kb-onboard		kb-onboard
kb-plan		kb-plan
kb-query		kb-query
kb-update		kb-update
kb_tricks		kb_tricks
moe-cr		moe-cr
moe-design		moe-design
moe-postmortem		moe-postmortem
moe-test		moe-test
spec		spec
templates		templates
tests		tests
tools		tools
.gitignore		.gitignore
KB_PLAN.md		KB_PLAN.md
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.md		README.md
RELEASE.md		RELEASE.md
ROADMAP.md		ROADMAP.md
pyproject.toml		pyproject.toml

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kb-tricks

Design Direction

Specs And Tooling

Skills

🚀 kb-init — One-Click Orchestration Pipeline

🗺️ kb-plan — Knowledge Base Manifest Planning

🧠 kb-build — Manifest Execution & KB Construction

🔄 kb-update — Incremental Knowledge Maintenance

🔎 kb-query — Knowledge Base Query & Source Fallback

🩺 kb-audit — Knowledge Base Health Check

📐 moe-design — Mixture-of-Experts Design Review

🔍 moe-cr — Mixture-of-Experts Code Review

🎓 kb-onboard — Knowledge-Driven Onboarding

🧪 moe-test — Mixture-of-Experts Test Review

🚑 moe-postmortem — Mixture-of-Experts Incident Postmortem

🔀 kb-migrate — Large-Scale Migration Planning

📝 kb-changelog — Knowledge Base Changelog

How They Connect

License

kb-tricks

设计方向

规范与工具

技能一览

🚀 kb-init — 一键编排流水线

🗺️ kb-plan — 知识库 Manifest 规划

🧠 kb-build — Manifest 执行与知识库构建

🔄 kb-update — 增量知识维护

🔎 kb-query — 知识库查询与源码回退

🩺 kb-audit — 知识库健康体检

📐 moe-design — 混合专家设计审查

🔍 moe-cr — 混合专家代码审查

🎓 kb-onboard — 知识库驱动新人引导

🧪 moe-test — 混合专家测试审查

🚑 moe-postmortem — 混合专家事故复盘

🔀 kb-migrate — 大规模迁移规划

📝 kb-changelog — 知识库变更日志

技能之间的关联

许可证

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