upgrade

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Chinese

Runtime Configuration (Step 0 — before any processing)

运行时配置（步骤0——任何处理开始前）

Read these files to configure domain-specific behavior:

ops/derivation-manifest.md
— vocabulary mapping, platform hints
- Use
```
vocabulary.notes
```
  for the notes folder name
- Use
```
vocabulary.note
```
  /
```
vocabulary.note_plural
```
  for note type references
- Use
```
vocabulary.reduce
```
  for the extraction verb
- Use
```
vocabulary.reflect
```
  for the connection-finding verb
- Use
```
vocabulary.reweave
```
  for the backward-pass verb
- Use
```
vocabulary.verify
```
  for the verification verb
- Use
```
vocabulary.rethink
```
  for the meta-cognitive verb
- Use
```
vocabulary.topic_map
```
  for MOC references
ops/config.yaml
— processing depth, domain context
ops/derivation.md
— derivation state and engine version

If these files don't exist, use universal defaults.

读取以下文件以配置特定领域的行为：

ops/derivation-manifest.md
— 词汇映射、平台提示
- 使用
```
vocabulary.notes
```
  指定笔记文件夹名称
- 使用
```
vocabulary.note
```
  /
```
vocabulary.note_plural
```
  引用笔记类型
- 使用
```
vocabulary.reduce
```
  指定提取动词
- 使用
```
vocabulary.reflect
```
  指定关联查找动词
- 使用
```
vocabulary.reweave
```
  指定反向处理动词
- 使用
```
vocabulary.verify
```
  指定验证动词
- 使用
```
vocabulary.rethink
```
  指定元认知动词
- 使用
```
vocabulary.topic_map
```
  引用MOC（思维导图）
ops/config.yaml
— 处理深度、领域上下文
ops/derivation.md
— 推导状态与引擎版本

若这些文件不存在，则使用通用默认值。

EXECUTE NOW

立即执行

Target: $ARGUMENTS

Parse immediately:

If target contains a specific skill name (e.g., "upgrade reduce"): check only that skill
If target contains "--all": check all generated skills
If target is empty: check all generated skills (same as --all)

START NOW. Reference below defines the upgrade process.

目标: $ARGUMENTS

立即解析：

若目标包含特定技能名称（例如"upgrade reduce"）：仅检查该技能
若目标包含"--all"：检查所有已生成技能
若目标为空：检查所有已生成技能（与--all效果相同）

立即开始。以下定义了升级流程的参考标准。

Why Consultation, Not Hashing

为何选择咨询而非哈希对比

Skills do not upgrade through hash comparison against a generation manifest. Hash comparison answers a narrow question: "Has this file changed?" Meta-skill consultation answers the right question: "Is this skill's approach still the best approach given what we know?"

A skill could be unchanged but outdated because the knowledge base has grown. Or a skill could be heavily edited by the user but already incorporate the latest thinking through a different path. Reasoning about methodology is more valuable than diffing bytes.

技能升级并非通过与生成清单的哈希对比来实现。哈希对比只能回答一个狭隘的问题："这个文件是否被修改过？"而元技能咨询则能回答更关键的问题："基于我们当前的认知，该技能的实现方式是否仍是最优选择？"

某个技能可能未被修改，但由于知识库的扩充而变得过时。或者，用户可能对技能进行了大量编辑，但已通过不同路径融入了最新理念。对方法论进行推理比对字节差异更有价值。

Two Upgrade Paths

两种升级路径

Generated skills and meta-skills follow fundamentally different upgrade mechanisms:

Category	Skills	Upgrade Mechanism
Generated skills	/{vocabulary.reduce}, /{vocabulary.reflect}, /{vocabulary.reweave}, /{vocabulary.verify}, /ralph, /next, /remember, /{vocabulary.rethink}, /stats, /graph, /tasks, /refactor, /learn, /recommend, /ask	Runtime consultation with knowledge graph
Meta-skills	/setup, /architect, /health, /reseed, /add-domain, /help, /tutorial, /upgrade	Plugin release cycle — update the plugin itself

/upgrade evaluates generated skills. It cannot evaluate itself or other meta-skills — that is the plugin maintainers' responsibility.

已生成技能和元技能遵循完全不同的升级机制：

类别	技能	升级机制
已生成技能	/{vocabulary.reduce}, /{vocabulary.reflect}, /{vocabulary.reweave}, /{vocabulary.verify}, /ralph, /next, /remember, /{vocabulary.rethink}, /stats, /graph, /tasks, /refactor, /learn, /recommend, /ask	运行时参考知识图谱
元技能	/setup, /architect, /health, /reseed, /add-domain, /help, /tutorial, /upgrade	插件发布周期 — 更新插件本身

/upgrade仅评估已生成技能。无法评估自身或其他元技能 — 这是插件维护者的职责。

Step 1: Inventory Current System

步骤1：盘点当前系统状态

Gather the vault's current state:

Read
```
ops/derivation.md
```
for:
- Original derivation state
- Engine version that generated the system
- Domain description and dimensional positions
Read
```
ops/generation-manifest.yaml
```
(if exists) for:
- Skill versions and generation timestamps
- Which plugin version generated each skill

List all installed skills:

bash

# Find all skill directories with SKILL.md
for dir in .claude/skills/*/; do
  skill=$(basename "$dir")
  version=$(grep '^version:' "$dir/SKILL.md" 2>/dev/null | head -1 | awk -F'"' '{print $2}')
  gen_from=$(grep '^generated_from:' "$dir/SKILL.md" 2>/dev/null | head -1 | awk -F'"' '{print $2}')
  echo "$skill  v$version  (from $gen_from)"
done

Read
```
ops/config.yaml
```
for current dimensional positions

Check for user modifications:

bash

# Detect skills modified after generation
for dir in .claude/skills/*/; do
  skill=$(basename "$dir")
  file="$dir/SKILL.md"
  [[ ! -f "$file" ]] && continue
  # Check git status — modified files indicate user customization
  git_status=$(git status --porcelain "$file" 2>/dev/null)
  if [[ -n "$git_status" ]]; then
    echo "MODIFIED: $skill"
  fi
done

Present inventory:

--=={ upgrade : inventory }==--

System: {domain description}
Engine: arscontexta-{version}
Skills: {count} installed ({modified_count} user-modified)

  Skill               Version  Generated From    Modified
  /{vocabulary.reduce}    1.0  arscontexta-v1.6  no
  /{vocabulary.reflect}   1.0  arscontexta-v1.6  yes
  ...

收集知识库的当前状态：

读取
```
ops/derivation.md
```
获取：
- 初始推导状态
- 生成系统所使用的引擎版本
- 领域描述与维度定位
读取
```
ops/generation-manifest.yaml
```
（若存在）获取：
- 技能版本与生成时间戳
- 每个技能由哪个插件版本生成

列出所有已安装技能：

bash

# 查找所有包含SKILL.md的技能目录
for dir in .claude/skills/*/; do
  skill=$(basename "$dir")
  version=$(grep '^version:' "$dir/SKILL.md" 2>/dev/null | head -1 | awk -F'"' '{print $2}')
  gen_from=$(grep '^generated_from:' "$dir/SKILL.md" 2>/dev/null | head -1 | awk -F'"' '{print $2}')
  echo "$skill  v$version  (from $gen_from)"
done

读取
```
ops/config.yaml
```
获取当前维度定位

检查用户修改情况：

bash

# 检测生成后被修改的技能
for dir in .claude/skills/*/; do
  skill=$(basename "$dir")
  file="$dir/SKILL.md"
  [[ ! -f "$file" ]] && continue
  # 检查git状态 — 修改过的文件表示用户自定义
  git_status=$(git status --porcelain "$file" 2>/dev/null)
  if [[ -n "$git_status" ]]; then
    echo "MODIFIED: $skill"
  fi
done

展示盘点结果：

--=={ upgrade : inventory }==--

系统: {领域描述}
引擎: arscontexta-{版本号}
技能: {数量}个已安装（{修改数量}个被用户修改）

  技能名称               版本号  生成来源    是否被修改
  /{vocabulary.reduce}    1.0  arscontexta-v1.6  否
  /{vocabulary.reflect}   1.0  arscontexta-v1.6  是
  ...

Step 2: Consult Knowledge Base

步骤2：参考知识库

For each generated skill (or the specific skill if targeted), consult the plugin's bundled knowledge base to evaluate whether the skill's current approach reflects current best practices.

对于每个已生成技能（或目标指定的特定技能），参考插件内置的知识库，评估该技能当前的实现方式是否符合当前最佳实践。

Knowledge Base Tiers

知识库层级

Read from the plugin's four content tiers:

Tier	Path	What It Contains
Methodology graph	`${CLAUDE_PLUGIN_ROOT}/methodology/`	All content — filter by `kind:` field (research/guidance/example)
Reference docs	`${CLAUDE_PLUGIN_ROOT}/reference/`	WHAT — structured reference documents and dimension maps

Notes in

methodology/

are differentiated by their

kind:

frontmatter field:

```
kind: research
```
— WHY: principles and cognitive science grounding (213 claims)
```
kind: guidance
```
— HOW: operational procedures and best practices (9 docs)
```
kind: example
```
— WHAT IT LOOKS LIKE: domain compositions (12 examples)
```
type: moc
```
— Navigation: topic maps linking related notes (15 maps)

从插件的四个内容层级读取信息：

层级	路径	包含内容
方法论图谱	`${CLAUDE_PLUGIN_ROOT}/methodology/`	所有内容 — 按 `kind:` 字段过滤（research/guidance/example）
参考文档	`${CLAUDE_PLUGIN_ROOT}/reference/`	内容规范 — 结构化参考文档与维度映射

methodology/

中的笔记通过

kind:

前置字段区分：

```
kind: research
```
— 理论依据：原则与认知科学基础（213项结论）
```
kind: guidance
```
— 操作指南：执行流程与最佳实践（9份文档）
```
kind: example
```
— 示例参考：领域组合案例（12个示例）
```
type: moc
```
— 导航：关联相关笔记的主题图谱（15个图谱）

Consultation Process Per Skill

单个技能的评估流程

For each skill being evaluated:

Read the current vault skill — understand its complete approach, quality gates, edge case handling
Read relevant knowledge base documents:
- Research claims about this skill's domain (e.g., for /{vocabulary.reduce}: claims about extraction methodology)
- Guidance docs about processing pipeline best practices
- Reference docs about the skill's operational patterns
Compare methodology, not text:
- Does the skill implement the quality gates the knowledge base recommends?
- Does it handle edge cases the knowledge base identifies?
- Does it use the discovery/search patterns the knowledge base recommends?
- Has the knowledge base added new techniques since this skill was generated?

Classify each finding:

Classification	Meaning	Example
Current	Skill reflects knowledge base best practices	No action needed
Enhancement	Knowledge base adds technique the skill lacks	New quality gate, better search pattern
Correction	Knowledge base contradicts skill's approach	Outdated methodology, known anti-pattern
Extension	Knowledge base covers scenario skill ignores	New edge case, new domain pattern

Check user modifications: If the skill has been modified by the user, read both the current (user-modified) version and evaluate whether:
- The user's changes already incorporate the improvement (skip it)
- The user's changes are orthogonal to the improvement (can coexist)
- The user's changes conflict with the improvement (flag for side-by-side review)

对于每个待评估的技能：

读取当前知识库技能 — 理解其完整实现方式、质量校验机制、边缘情况处理逻辑
读取相关知识库文档：
- 该技能领域的研究结论（例如，对于/{vocabulary.reduce}：提取方法论相关结论）
- 处理流水线最佳实践的指南文档
- 技能操作模式的参考文档
对比方法论而非文本：
- 该技能是否实现了知识库推荐的质量校验机制？
- 是否处理了知识库指出的边缘情况？
- 是否使用了知识库推荐的发现/搜索模式？
- 自该技能生成以来，知识库是否新增了新技术？

对每个发现进行分类：

分类	含义	示例
当前最优	技能符合知识库最佳实践	无需操作
功能增强	知识库新增了技能未具备的技术	新增质量校验、优化搜索模式
修正改进	知识库与技能的实现方式相悖	过时方法论、已知反模式
功能扩展	知识库覆盖了技能未考虑的场景	新边缘情况、新领域模式

检查用户修改情况： 若技能已被用户修改，需同时读取当前（用户修改后）版本，并评估：
- 用户的修改是否已包含该改进（可跳过）
- 用户的修改与该改进是否正交（可共存）
- 用户的修改与该改进是否冲突（需标记为并排对比）

Step 3: Generate Upgrade Plan

步骤3：生成升级计划

For each skill with available improvements, create a structured proposal:

Skill: /{domain:skill-name}
Status: {current | enhancement | correction | extension}
User-modified: {yes | no}

Current approach:
  {2-3 sentences describing what the skill currently does}

Proposed improvement:
  {2-3 sentences describing what would change}

Research backing:
  {Specific claims from the knowledge base that support this change}
  - "{claim title}" — {how it applies}
  - "{claim title}" — {how it applies}

Impact: {what changes for the user's workflow}
Risk: low | medium | high
Reversible: yes (previous version archived to ops/skills-archive/)

对于每个可改进的技能，创建结构化建议：

技能: /{domain:skill-name}
状态: {当前最优 | 功能增强 | 修正改进 | 功能扩展}
用户修改: {是 | 否}

当前实现方式:
  {2-3句话描述技能当前的功能}

建议改进:
  {2-3句话描述变更内容}

研究依据:
  {支持该变更的知识库具体结论}
  - "{结论标题}" — {应用场景}
  - "{结论标题}" — {应用场景}

影响: {对用户工作流的变更}
风险: 低 | 中 | 高
可回滚: 是（旧版本将归档至ops/skills-archive/）

Risk Assessment

风险评估

Risk Level	Criteria
Low	Additive change (new quality gate, better logging). Existing behavior unchanged.
Medium	Modified behavior (different extraction strategy, changed search pattern). Output quality affected.
High	Structural change (different phase ordering, changed handoff format). Pipeline coordination affected.

风险等级	判定标准
低	增量式变更（新增质量校验、优化日志）。现有行为不受影响。
中	修改现有行为（变更提取策略、调整搜索模式）。会影响输出质量。
高	结构性变更（调整阶段顺序、修改交接格式）。会影响流水线协同。

Side-by-Side for User-Modified Skills

用户修改技能的并排对比

When a skill has been modified by the user AND an upgrade is available, show a side-by-side comparison:

Skill: /{domain:skill-name} (USER-MODIFIED)

Your version:                     Recommended:
  [relevant section excerpt]        [what knowledge base suggests]

Your customization:
  {description of what the user changed and why it appears intentional}

Options:
  (a) Keep your version unchanged
  (b) Apply upgrade, preserving your customizations
  (c) Apply upgrade, replacing your version (archived to ops/skills-archive/)

Option (b) requires the upgrade to be compatible with the user's changes. If they conflict, explain why and recommend (a) or (c).

当技能已被用户修改且存在可用升级时，展示并排对比：

技能: /{domain:skill-name}（用户已修改）

你的版本:                     推荐版本:
  [相关片段节选]        [知识库建议内容]

你的自定义修改:
  {描述用户修改的内容及意图}

选项:
  (a) 保留你的版本不变
  (b) 应用升级，保留你的自定义内容
  (c) 应用升级，替换你的版本（旧版本归档至ops/skills-archive/）

选项(b)要求升级与用户修改兼容。若存在冲突，需解释原因并推荐(a)或(c)。

Step 4: Present Plan

步骤4：展示升级计划

--=={ upgrade }==--

Plugin: arscontexta-{current_version}
Knowledge base: {count} research claims, {count} guidance docs
Skills checked: {count}

Upgrades available: {count}
  Enhancements: {n}  |  Corrections: {n}  |  Extensions: {n}

  1. /{domain:skill-name}
     Type: Enhancement
     Change: {one-line summary}
     Research: "{claim title}"
     Risk: low

  2. /{domain:skill-name} (USER-MODIFIED)
     Type: Correction
     Change: {one-line summary}
     Research: "{claim title}", "{claim title}"
     Risk: medium
     Note: Side-by-side comparison available

  ...

{If no upgrades:}
  All {count} skills reflect current best practices.
  No upgrades needed.

Apply all? Select specific upgrades (e.g., "1, 3")?
Or "show 2" for side-by-side detail on a specific skill.

Wait for user response. Do NOT proceed without explicit approval.

--=={ upgrade }==--

插件: arscontexta-{当前版本}
知识库: {数量}项研究结论，{数量}份指南文档
已检查技能: {数量}

可用升级: {数量}
  功能增强: {n}  |  修正改进: {n}  |  功能扩展: {n}

  1. /{domain:skill-name}
     类型: 功能增强
     变更: {一句话摘要}
     研究依据: "{结论标题}"
     风险: 低

  2. /{domain:skill-name}（用户已修改）
     类型: 修正改进
     变更: {一句话摘要}
     研究依据: "{结论标题}", "{结论标题}"
     风险: 中
     备注: 可查看并排对比

  ...

{若无可用升级:}
  所有{数量}个技能均符合当前最佳实践。
  无需升级。

是否应用全部升级？或选择特定升级（例如"1, 3"）？
或输入"show 2"查看特定技能的并排对比详情。

等待用户响应。未经明确批准，不得继续执行。

Step 5: Apply Approved Upgrades

步骤5：应用已批准的升级

For each approved upgrade:

对于每个已批准的升级：

5a. Archive Current Version

5a. 归档当前版本

bash

mkdir -p ops/skills-archive
SKILL_NAME="{skill-name}"
DATE=$(date +%Y-%m-%d)
cp ".claude/skills/${SKILL_NAME}/SKILL.md" \
   "ops/skills-archive/${SKILL_NAME}-${DATE}.md"

bash

mkdir -p ops/skills-archive
SKILL_NAME="{skill-name}"
DATE=$(date +%Y-%m-%d)
cp ".claude/skills/${SKILL_NAME}/SKILL.md" \
   "ops/skills-archive/${SKILL_NAME}-${DATE}.md"

5b. Generate Updated Skill

5b. 生成更新后的技能

Read the skill's generation block from the plugin (if available)
Apply the specific improvements identified in Step 2
Preserve the user's vocabulary transformation from
```
ops/derivation-manifest.md
```
Preserve the user's dimensional positions from
```
ops/config.yaml
```
For user-modified skills with option (b): merge the user's customizations into the updated skill

读取插件中的技能生成模块（若可用）
应用步骤2中确定的具体改进
保留
```
ops/derivation-manifest.md
```
中的用户词汇转换规则
保留
```
ops/config.yaml
```
中的用户维度定位
对于选择选项(b)的用户修改技能：将用户自定义内容合并到更新后的技能中

5c. Update Version Tracking

5c. 更新版本跟踪

Update the skill's frontmatter:

yaml

---
version: "{incremented}"
generated_from: "arscontexta-{current_plugin_version}"
---

更新技能的前置信息：

yaml

---
version: "{递增后的版本号}"
generated_from: "arscontexta-{当前插件版本}"
---

5d. Update Generation Manifest

5d. 更新生成清单

ops/generation-manifest.yaml

exists, update the entry for this skill:

yaml

skills:
  {skill-name}:
    version: "{new_version}"
    upgraded: "{ISO 8601 UTC}"
    upgrade_source: "knowledge-graph-consultation"
    changes: "{brief description of what changed}"

若

ops/generation-manifest.yaml

存在，更新该技能的条目：

yaml

skills:
  {skill-name}:
    version: "{新版本号}"
    upgraded: "{ISO 8601 UTC格式时间}"
    upgrade_source: "knowledge-graph-consultation"
    changes: "{变更内容简要描述}"

Step 6: Validate

步骤6：验证

After applying all approved upgrades:

Kernel validation — run kernel checks to confirm structural invariants hold:

bash

# Verify skill files are valid
for dir in .claude/skills/*/; do
  [[ -f "$dir/SKILL.md" ]] || echo "MISSING: $dir/SKILL.md"
done

Context file check — verify all skill references in the context file still resolve

Vocabulary check — confirm upgraded skills use domain vocabulary consistently:

bash

# Spot-check that vocabulary markers were resolved
grep -l '{vocabulary\.' .claude/skills/*/SKILL.md 2>/dev/null
# Should return nothing — all markers should be resolved

Pipeline compatibility — if pipeline skills were upgraded (/{vocabulary.reduce}, /{vocabulary.reflect}, /{vocabulary.reweave}, /{vocabulary.verify}), verify handoff format compatibility with /ralph

应用所有已批准的升级后：

内核验证 — 运行内核检查以确认结构一致性：

bash

# 验证技能文件有效
for dir in .claude/skills/*/; do
  [[ -f "$dir/SKILL.md" ]] || echo "MISSING: $dir/SKILL.md"
done

上下文文件检查 — 验证上下文文件中所有技能引用仍可解析

词汇一致性检查 — 确认升级后的技能一致使用领域词汇：

bash

# 抽查词汇标记是否已解析
grep -l '{vocabulary\.' .claude/skills/*/SKILL.md 2>/dev/null
# 应无输出 — 所有标记均应已解析

流水线兼容性 — 若流水线技能被升级（/{vocabulary.reduce}, /{vocabulary.reflect}, /{vocabulary.reweave}, /{vocabulary.verify}），验证与/ralph的交接格式兼容性

Final Report

最终报告

--=={ upgrade complete }==--

Applied: {N} upgrades
Archived: {N} previous versions to ops/skills-archive/
Skipped: {N} (user-modified, kept as-is)

Changes:
  - /{skill}: {what changed} (Research: "{claim}")
  - /{skill}: {what changed} (Research: "{claim}")

Validation: {PASS | FAIL with details}

{If any validation failed:}
  WARNING: Validation issue detected.
  Previous versions available in ops/skills-archive/
  for manual rollback.

Note: Run /{vocabulary.verify} on a recent {vocabulary.note}
to confirm upgraded skills work correctly in practice.

--=={ upgrade complete }==--

已应用: {N}项升级
已归档: {N}个旧版本至ops/skills-archive/
已跳过: {N}项（用户修改的技能，保持不变）

变更内容:
  - /{skill}: {变更描述}（研究依据: "{结论}"）
  - /{skill}: {变更描述}（研究依据: "{结论}"）

验证结果: {通过 | 失败，附详情}

{若验证失败:}
  警告：检测到验证问题。
  旧版本可在ops/skills-archive/中找到，用于手动回滚。

备注：在最近的{vocabulary.note}上运行/{vocabulary.verify}，以确认升级后的技能在实际场景中正常工作。

INVARIANT

不变原则

/upgrade never auto-implements. The upgrade plan is always presented to the user first. The user decides which upgrades to apply. This prevents the cognitive outsourcing failure mode where the system changes itself without human understanding.

All upgrades are advisory. The user owns the files.

/upgrade绝不会自动执行。升级计划始终先展示给用户，由用户决定应用哪些升级。这避免了系统在无人类理解的情况下自行变更的认知外包失效模式。

所有升级均为建议性内容。用户拥有文件的所有权。

Edge Cases

边缘情况

No improvements available: Report "All skills reflect current best practices. No upgrades needed." with the count of skills checked.

No generation manifest: Treat all skills as version 0 (unknown generation state). Compare methodology against current knowledge base. This is fine — consultation reasons about approach, not version numbers.

Skill has been user-modified: Present the side-by-side comparison. Offer three options: keep user version, merge upgrade with customizations, or replace (with archive). Never silently overwrite.

No ops/derivation-manifest.md: Use universal vocabulary for all output.

Plugin knowledge base unavailable: Report that knowledge base consultation requires the Ars Contexta plugin. Without the plugin's bundled methodology/ and reference/ directories, /upgrade cannot evaluate skills.

User rejects upgrades consistently: This is a signal, not an error. Note the pattern — it may indicate the knowledge base recommendations don't match this user's domain. Log to ops/observations/ if it persists across multiple /upgrade runs.

Correction conflicts with user modification: When the knowledge base identifies a correction (not just enhancement) but the user has modified the skill, explain the conflict clearly. The user may have modified the skill precisely because the original approach was wrong — their fix may already address the correction. Show both and let the user decide.

Multiple skills share a change: If the same knowledge base improvement applies to several skills (e.g., a new search pattern), present it as a single conceptual change affecting multiple skills rather than listing it redundantly per skill.

无可用改进： 报告“所有技能均符合当前最佳实践。无需升级。”并注明已检查的技能数量。

无生成清单： 将所有技能视为版本0（未知生成状态）。对比当前知识库的方法论即可 — 评估的是实现方式，而非版本号。

技能已被用户修改： 展示并排对比。提供三个选项：保留用户版本、合并升级与自定义内容、或替换（并归档）。绝不静默覆盖。

无ops/derivation-manifest.md： 所有输出使用通用词汇。

插件知识库不可用： 报告知识库评估需要Ars Contexta插件。若无插件内置的methodology/和reference/目录，/upgrade无法评估技能。

用户持续拒绝升级： 这是一个信号而非错误。记录该模式 — 可能表示知识库建议不符合该用户的领域需求。若多次/upgrade请求均如此，记录至ops/observations/。

修正改进与用户修改冲突： 当知识库指出需要修正改进（不仅是功能增强）但用户已修改技能时，需清晰解释冲突。用户修改技能可能正是因为原始方法存在问题 — 他们的修复可能已解决了修正改进的问题。展示双方内容并由用户决定。

多个技能共享同一变更： 若同一知识库改进适用于多个技能（例如，新搜索模式），应将其作为单个概念变更影响多个技能展示，而非在每个技能下重复列出。