Cc Version Analysis logo

Cc Version Analysis

Organization
ww-w-ai
cc-version-analysis

CC CLI version upgrade impact analysis — research changes, analyze bkit impact, generate report. Triggers: cc-version-analysis, CC upgrade, version analysis

Overview

Publisherww-w-ai
Repositorybkit-claude-code
Skill namecc-version-analysis
Stars
601
Forks
154
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by ww-w-ai on GitHub. Read the source before you install it.

Installation

Install the Cc Version Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ww-w-ai/bkit-claude-code.git /tmp/bkit-claude-code
mkdir -p .claude/skills
cp -r /tmp/bkit-claude-code/skills/cc-version-analysis .claude/skills/cc-version-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cc Version Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cc Version Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cc Version Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

CC Version Analysis — Claude Code CLI 버전 영향 분석 워크플로우

CC CLI 버전 업그레이드 시 bkit plugin에 대한 영향을 체계적으로 조사, 분석하고 개선 기회를 도출하는 전문 워크플로우 스킬.

Overview

이 스킬은 CC CLI의 새 버전이 출시되었을 때 다음을 자동화합니다:

  1. Phase 1 (Research): CC 변경사항 심층 조사
  2. Phase 2 (Analyze): bkit 아키텍처 영향 분석
  3. Phase 3 (Brainstorm): Plan Plus 브레인스토밍으로 개선안 도출
  4. Phase 4 (Report): 종합 영향 분석 보고서 작성

Agent Team 구성:

  • cc-version-researcher: CC 버전 변경사항 외부 조사
  • bkit-impact-analyst: bkit 내부 아키텍처 영향 분석
  • report-generator: 최종 보고서 생성

HARD-GATE

NEW (v2.1.16 errata learning — added 2026-05-20): Do NOT advance from Phase 1 → Phase 2 without completing the Phase 1.5 Raw Source Verification Gate. The cc-version-researcher agent's quantitative claims (bullet count, issue counts, file counts) MUST be cross-checked against ≥2 raw sources (GitHub release tag URL + raw CHANGELOG.md URL) before being adopted into the report. The v2.1.145 cycle leaked an unverified bullet count of 21 (actual: 20) and an unverified agent count of 36 (actual: 34) because this gate did not exist. If raw and agent disagree, raw wins.

Invocation

/cc-version-analysis                    # Auto-detect: installed vs latest
/cc-version-analysis 2.1.78 2.1.85     # Specific version range
/cc-version-analysis to 2.1.85         # From current installed to target

Process Flow

┌─────────────────────────────────────────────────────┐
│                CC Version Analysis                   │
├─────────────────────────────────────────────────────┤
│                                                      │
│  Phase 0: Setup & Version Detection                  │
│  ├── Detect installed CC version (claude --version)  │
│  ├── Determine target version (args or latest)       │
│  ├── Create Task tracking structure                  │
│  └── Load previous analysis from memory              │
│                                                      │
│  Phase 1: Research (cc-version-researcher agent)     │
│  ├── Official docs (code.claude.com)                 │
│  ├── GitHub (anthropics/claude-code)                 │
│  │   ├── Releases & changelog                        │
│  │   ├── Issues (open & recently closed)             │
│  │   ├── PRs (merged in version range)               │
│  │   └── Commits (significant changes)               │
│  ├── npm registry (@anthropic-ai/claude-code)        │
│  ├── Technical blogs & community                     │
│  └── Output: CC Change Report (structured)           │
│                                                      │
│  Phase 1.5: Raw Source Verification Gate (MANDATORY) │
│  ├── WebFetch https://github.com/anthropics/         │
│  │   claude-code/releases/tag/v{to}                  │
│  ├── WebFetch https://raw.githubusercontent.com/     │
│  │   anthropics/claude-code/main/CHANGELOG.md        │
│  ├── Cross-check agent's bullet count vs raw count  │
│  ├── If mismatch → raw wins, record errata           │
│  ├── Spot-check ≥3 random Fixed bullets verbatim     │
│  └── Block Phase 2 if any mismatch unresolved        │
│                                                      │
│  Phase 2: Analyze (bkit-impact-analyst agent)        │
│  ├── Map CC changes → bkit components                │
│  ├── Identify ENH opportunities                      │
│  ├── File impact matrix                              │
│  ├── Philosophy compliance check                     │
│  ├── Test impact assessment                          │
│  └── Output: bkit Impact Analysis (structured)       │
│                                                      │
│  Phase 3: Brainstorm (Plan Plus methodology)         │
│  ├── Intent discovery (핵심 목표/리스크/기회)         │
│  ├── Alternative exploration                         │
│  ├── YAGNI review (각 ENH 필요성 검증)               │
│  ├── Priority assignment (P0~P3)                     │
│  └── Output: Prioritized ENH roadmap                 │
│                                                      │
│  Phase 4: Report Generation                          │
│  ├── Merge all phase outputs                         │
│  ├── Generate from template                          │
│  │   (cc-version-analysis.template.md)               │
│  ├── Save to docs/04-report/features/                │
│  ├── Update MEMORY.md (version history)              │
│  └── Output: Final Impact Report (Korean)            │
│                                                      │
└─────────────────────────────────────────────────────┘

Phase Details

Phase 0: Setup & Version Detection

1. Detect installed CC version:
   $ claude --version

2. Determine target version:
   - If args provided: use specified versions
   - If no args: search for latest available version

3. Create Task structure:
   TaskCreate: "[CC-Version-Analysis] CC v{from} → v{to}"
     ├── Task: "Phase 1: CC 변경사항 조사"
     ├── Task: "Phase 2: bkit 영향 분석"
     ├── Task: "Phase 3: Plan Plus 브레인스토밍"
     └── Task: "Phase 4: 보고서 작성"

4. Load previous analysis context:
   - Read memory/cc_version_history_*.md
   - Read last ENH number from MEMORY.md
   - Read existing PDCA status from .bkit/state/pdca-status.json

Phase 1: Research (Agent: cc-version-researcher)

Input: from_version, to_version Output: Structured CC Change Report

Launch the cc-version-researcher agent with:

Research CC CLI changes from v{from} to v{to}.
Sources: official docs, GitHub (issues/PRs/releases), npm, blogs.
Categorize by: Breaking/Feature/Fix/Performance/SystemPrompt/Hook/Config.
Rate impact: HIGH/MEDIUM/LOW.
Flag bkit-relevant changes.
Output structured markdown tables.

Parallel research tasks (when using Agent Team):

  • Task 1: GitHub releases + changelog
  • Task 2: GitHub issues (open + recently closed)
  • Task 3: Official docs changes
  • Task 4: System prompt diff analysis

Phase 1.5: Raw Source Verification Gate (MANDATORY — added 2026-05-20)

Why: cc-version-researcher's output may paraphrase, summarize, or miscount. The v2.1.145 cycle revealed that a single WebFetch (via the model-processed release tag page) under-counted by 1 bullet (reported 6 Added, actual 7). A second fetch against raw CHANGELOG.md is required to catch this.

Protocol (main session, not delegated):

  1. Fetch raw GitHub release tag page:
    WebFetch https://github.com/anthropics/claude-code/releases/tag/v{to_version}
  2. Fetch raw CHANGELOG.md (authoritative):
    WebFetch https://raw.githubusercontent.com/anthropics/claude-code/main/CHANGELOG.md
    prompt: "Show the FULL v{to_version} section verbatim. List every bullet exactly as written, in order, under the original headings (Added/Fixed/ Improved/Breaking/etc). Do NOT summarize."
  3. Compare to cc-version-researcher's reported counts:
    • Bullet count (total / per-heading)
    • Breaking count
    • HIGH/MEDIUM/LOW split
    • Key item presence (security fixes, OTEL, hook input changes)
  4. If raw and agent disagree → raw wins. Record discrepancy in report under §3.0 "Verification Notes" with both numbers.
  5. Spot-check ≥3 random Fixed bullets — verify verbatim text in agent report matches raw.
  6. Block Phase 2 progression if any mismatch is unresolved.

Output: §3.0 Verification table appended to Phase 1 output:

FieldAgent reportedRaw verifiedSource URLVerdict
AddedNMraw CHANGELOGmatch / errata
FixedNMraw CHANGELOGmatch / errata
ImprovedNMraw CHANGELOGmatch / errata
BreakingNMraw CHANGELOGmatch / errata
Total bulletsNMsummatch / errata

Phase 1.6: Opaque Release Protocol (MANDATORY when triggered — ENH-420)

Trigger: the range's CHANGELOG carries ≤ 1 bullet, OR its bullets are non-specific ("Bug fixes and reliability improvements"). CC v2.1.226 was the first instance and will not be the last.

Why this exists: an opaque release makes CHANGELOG-based analysis worth exactly nothing, and the cycle either invents an answer or measures one. Cycle #35 measured, and the measurement carried the whole report — but it took 15+ manual tool calls to re-derive a procedure nobody had written down, and it produced a false positive along the way (ERRATA-35-1) before catching it.

Procedure (main session, not delegated):

  1. Run node scripts/cc-binary-equivalence.js <from> <to> (ENH-421). It reports file size, sha256, Mach-O segment sizes, and exact grep -a -o -F counts for every hook-contract marker, in one pass per build.
  2. Read the segment table before the marker table. Identical __TEXT / __DATA_CONST / __DATA with a moving __BUN means a payload swap, not a native rebuild — a distinction the file size alone hides.
  3. If no marker moved, the range is equivalent on the measured surfaces, and that is the finding. State the surfaces; do not generalize past them.
  4. Transfer rule: when two builds are equivalent, every judgement made about the earlier one carries forward unchanged — compatibility, risk, tracked-issue state. Nothing needs re-evaluating, and saying so explicitly is more useful than re-deriving it.
  5. "No change" is itself a reason RECOMMENDED_VERSION cannot rise. An equivalent release resolves nothing, so a hold stays a hold.

Method rules, each of which cost a cycle:

  • ERRATA-35-1: a strings set diff is NOT evidence. Trailing-byte artifacts alone produced 1,315 added / 1,226 removed on two builds that turned out to be equivalent. Only exact counts on the raw file count.
  • ERRATA-37-2: a needle beginning with - is parsed as an option by ugrep and silently returns 0, which reads as absence. Use grep -a -o -F -e '<needle>'.
  • ERRATA-37-6: a misspelled needle also returns a silent 0. Measure spelling variants together — runInBackground was 0 in three builds while run_in_background was 49.
  • ERRATA-33-6: the CHANGELOG paraphrases the implementation. A count of 0 for a term lifted from the release notes does not mean the fix is absent.
  • ERRATA-36-6: cmp -l over two ~300 MB binaries exceeds a two-minute budget, and cmp -n / -i return wrong results on this artifact. Do not use them.

Binary provenance (ENH-422) — record these in the report and in the cycle memory, for every build examined. They scope the claim honestly and make cross-platform gaps trackable rather than invisible:

FieldWhere from
architecturefile <binary> (e.g. Mach-O 64-bit x86_64)
file sizestat / the script's table
sha256the script's table
GIT_SHA, BUILD_TIMEthe build banner inside the bundle
platforms NOT examinedstated explicitly

The last row is the one that matters. GIT_SHA moving while the bundle does not means upstream commits exist; the honest claim is "those commits did not change the shipped macOS x86_64 bundle", never "there were no commits".

Phase 2: Analyze (Agent: bkit-impact-analyst)

Input: Phase 1 CC Change Report PLUS Phase 1.5 verification table Output: bkit Impact Analysis

Pre-flight check before launching the analyst agent: the analyst will measure bkit architecture stats (agents/skills/hooks counts) via Bash. If the analyst proposes a numeric correction to memory (e.g. "agents 34 → 36"), the main session MUST re-run the measurement command independently before accepting it. See "Numeric Correction Protocol" in bkit-impact-analyst.md.

Launch the bkit-impact-analyst agent with:

Analyze bkit impact from these CC changes: {phase1_output}
Map each change to bkit components (agents/skills/hooks/lib/scripts).
Identify ENH opportunities starting from ENH-{last+1}.
Check philosophy compliance (Automation First, No Guessing, Docs=Code).
Assess test impact per ENH.

Analysis scope:

  • 29 agents: frontmatter compatibility
  • 31 skills: allowed-tools, hooks compatibility
  • 12 hook events: new events, changed behavior
  • 210 lib exports: API compatibility
  • 50 scripts: stdin/stdout protocol
  • 1,186 TCs: test coverage gaps

Phase 3: Brainstorm (Plan Plus Methodology)

Input: Phase 2 Impact Analysis Output: Prioritized ENH Roadmap

Apply Plan Plus brainstorming phases:

3.1 Intent Discovery

Ask and answer:

  • 이 CC 업그레이드에서 bkit이 얻을 수 있는 최대 가치는?
  • 놓치면 안 되는 critical change는?
  • 기존 workaround를 대체할 수 있는 native 기능은?
3.2 Alternative Exploration

For each HIGH/MEDIUM ENH:

  • 구현 방법 A vs B vs C 비교
  • 최소 구현 (MVP) vs 완전 구현 trade-off
  • 다른 ENH와의 의존성/시너지
3.3 YAGNI Review

Each ENH must pass:

  • ✅ 현재 사용자가 실제로 필요로 하는가?
  • ✅ 구현하지 않으면 어떤 문제가 발생하는가?
  • ✅ 다음 CC 버전에서 더 나은 방법이 나올 가능성은?
  • ❌ YAGNI fail → P3 강등 또는 제거
3.4 Priority Assignment

Final priority based on:

  • P0: Core PDCA workflow 직접 개선 또는 known pain point 해결
  • P1: 중요한 새 기능 활성화 또는 major DX 개선
  • P2: Nice-to-have, 문서 업데이트
  • P3: Cosmetic, minor optimization, future consideration

Phase 4: Report Generation

Input: All phase outputs Output: Final Korean report in docs/

  1. Generate report from cc-version-analysis.template.md
  2. Save to: docs/04-report/features/cc-v{from}-v{to}-impact-analysis.report.md
  3. Also create Plan (if ENH count > 0): docs/01-plan/features/cc-v{from}-v{to}-impact-analysis.plan.md
  4. Update MEMORY.md:
    • CC version history section
    • ENH number range
    • Consecutive compatible releases count
    • Open/closed GitHub issues
  5. Update memory file: memory/cc_version_history_v{from}_v{to}.md

Task Management Protocol

All work MUST be tracked via Task Management System:

[CC-Version-Analysis] CC v{from} → v{to}          # Parent task
├── [Research] Phase 1: CC 변경사항 조사             # cc-version-researcher
│   ├── GitHub releases 조사
│   ├── GitHub issues 조사
│   ├── 공식 문서 변경 조사
│   └── 시스템 프롬프트 변경 분석
├── [Analyze] Phase 2: bkit 영향 분석                # bkit-impact-analyst
│   ├── 컴포넌트 매핑
│   ├── ENH 기회 식별
│   ├── 파일 영향 매트릭스
│   └── 철학 준수 검증
├── [Brainstorm] Phase 3: 브레인스토밍               # Plan Plus
│   ├── 의도 탐색
│   ├── 대안 탐색
│   └── YAGNI 검토
└── [Report] Phase 4: 보고서 작성                    # report-generator
    ├── 템플릿 기반 보고서 생성
    ├── MEMORY.md 업데이트
    └── 최종 검토

Agent Team Configuration

When invoked with CTO Team (/pdca team):

RoleAgentModelTask
Leadcto-leadfableOverall orchestration
Researchercc-version-researcheropusPhase 1: CC research
Analystbkit-impact-analystopusPhase 2: bkit analysis
Reporterreport-generatorhaikuPhase 4: Report writing

Parallel execution:

  • Phase 1 tasks can run in parallel (GitHub, docs, npm)
  • Phase 2 depends on Phase 1 completion
  • Phase 3 depends on Phase 2 completion
  • Phase 4 depends on Phase 3 completion

Quality Checklist

Before completing, verify:

  • All CC changes from version range are captured
  • Every change has impact classification (HIGH/MEDIUM/LOW)
  • Every ENH has priority (P0/P1/P2/P3)
  • Philosophy compliance checked for all ENH items
  • File impact matrix is complete
  • Test impact assessed for all ENH items
  • Report is written in Korean
  • MEMORY.md is updated
  • Task tracking shows all items completed
  • Executive Summary includes 4-perspective value table

Raw Verification Checklist (NEW — Phase 1.5 gate, v2.1.16 errata learning)

  • Raw GitHub release tag URL fetched (releases/tag/v{to})
  • Raw CHANGELOG.md URL fetched (raw.githubusercontent.com)
  • Bullet counts cross-verified (agent vs raw, both numbers reported)
  • ≥3 spot-check bullets confirmed verbatim against raw
  • Numeric corrections re-verified via direct Bash measurement before adoption
  • §3.0 Verification table included in report (5 rows: Added/Fixed/Improved/Breaking/Total)
  • Errata entries recorded in memory file under "Known Errata" section if any mismatches occurred

Known Errata Log (errata learning archive)

When raw verification catches a discrepancy with agent output, record it here to prevent repeat-mistakes and to feed future skill improvements.

Cycle v2.1.145 (2026-05-20)

FieldAgent reportedRaw verifiedRoot cause
Bullet count21 (Features 7 + Fixes 13 + Improved 1)20 (Added 7 + Fixed 12 + Improved 1)Agent over-counted Fixed by 1; first WebFetch on model-processed release page under-reported Added by 1 (raw CHANGELOG was authoritative)
Agents directory count36 (proposed correction from 34)34 (ls -1 agents/ | wc -l)Analyst proposed unverified numeric correction; main session adopted without re-measurement
F7-145 background_tasks/session_crons"extension surface" (no clear source citation)Confirmed in raw CHANGELOG Added #7 verbatimSingle-source WebFetch had originally omitted this bullet; second raw fetch recovered it

Lessons applied to skill (this commit):

  1. Phase 1.5 Raw Verification Gate now mandatory before Phase 2
  2. cc-version-researcher: verbatim bullet quotation required
  3. bkit-impact-analyst: direct-measurement-first; Numeric Correction Protocol
  4. SKILL.md: 7-item Raw Verification Checklist added
  5. bkit-impact-analyst.md: stale architecture snapshot removed, replaced with mandatory Bash measurement protocol

Previous Analysis Reference

This skill builds on established analysis patterns:

  • docs/04-report/features/claude-code-v2172-impact-analysis.report.md
  • docs/04-report/features/claude-code-v2178-impact-analysis.report.md
  • memory/cc_version_history_v2134_v2172.md

Always read previous reports first to maintain consistency in:

  • ENH numbering (continue from last used number)
  • Report structure and depth
  • Consecutive compatible release tracking
  • GitHub issues monitoring continuity

Frequently asked questions

What does the Cc Version Analysis AI skill do?

CC CLI version upgrade impact analysis — research changes, analyze bkit impact, generate report. Triggers: cc-version-analysis, CC upgrade, version analysis

Why use Cc Version Analysis on TypingMind?

Because you install it once and use it with any model. Cc Version Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cc Version Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ww-w-ai/bkit-claude-code/tree/main/skills/cc-version-analysis. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Cc Version Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cc Version Analysis?

As many as you like. As long as a model supports skills, you can use Cc Version Analysis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cc Version Analysis AI skill free?

Yes. It is published on GitHub by ww-w-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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