
Wisegit
io.github.Sandip124由 GitHub 账号 Sandip124 发布v0.1.1更新于 Nov 3, 2026
Extracts decision intent from git history and protects intentional code from AI modification.
概览
本地 MCP 服务器,从 git 历史中提取决策意图并给出冻结评分,让助手在改代码前尊重有意的实现。
- 可以做什么
- 查看文件的决策清单,含冻结评分、意图历史、恢复等级与覆盖状态
- 查询单个函数的评分与信号明细,或完整的按时间排列的决策记录
- 按关键词搜索历史决策,并查找解决类似问题的已有函数
- 预测修改某函数会影响哪些函数,并读取文件周边的编码约定
- 列出理论缺口与分支合并历史,并为冻结函数创建覆盖记录
- 借助宿主模型为低信息量提交补提意图
- 适用场景
- 适合历史较长或遗留代码库,其中过往的修复、变通做法和刻意的设计容易被 AI 误判为无用代码。当你希望助手在重构或“找 bug”前先确认哪些是有意为之,最值得安装。
- 运行要求
- 通过 npx 以 stdio 本地运行,需要 Node.js 20 或更高版本。初始化会索引仓库的 git 历史,并生成本地 SQLite 数据库和项目配置文件。可选的 issue 补充需要 GitHub 或 GitLab 令牌;可选的本地模型提取需要 Ollama 实例。
安装
在 SourceWeft 中
- 打开 控制台中的 Wisegit,将其添加到工作区。
- 为需要使用其工具的对话启用该服务。
Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。
其他 MCP 客户端
参照 仓库 中的启动说明。
README
wisegit
[npm] [MCP Registry] [License: MIT]
"Don't take a fence down until you know the reason it was put up." — G.K. Chesterton
wisegit is a local MCP server that extracts decision intent from git history and protects intentional code from AI modification.
When Claude Code (or any MCP-compatible agent) is about to edit a file, wisegit injects a decision manifest showing which functions are frozen, stable, or open — so the AI respects what was intentional, not just what compiles.
Zero config. Zero external services. Everything local.
Install
Published on
- npm: @sandip124/wisegit
- MCP Registry: io.github.Sandip124/wisegit
- GitHub: Sandip124/wisegit
The Problem
LLMs have no concept of intentional code. A manually-tested fix and a broken stub look identical — both are just text. Real scenario:
- You fix a Stripe race condition with
sleep(350)— manually tested, committed. - Next session: "find bugs." Claude removes
sleep(350)— looks like dead code. - Production incident.
Root cause: git history contains proof of intention. Nobody extracts it.
How It Works
- Indexes your git history — walks every commit, parses diffs at the AST level (function boundaries, not line counts)
- Classifies commits — STRUCTURED (
fix:,feat:), DESCRIPTIVE (plain sentences), or NOISE (wip,x) - Extracts intent — rule-based for structured/descriptive commits, LLM for noise (Phase 2)
- Computes freeze scores — 0–1 per function, derived from protection signals (git, issue, code structure, test, structural, Naur, Aranda) minus adaptive obsolescence penalty (8 signals, entropy-calibrated). Age uses Weibull survival [18]; expertise uses DOE model [19]
- Serves decision manifests via MCP — Claude Code calls
get_file_decisionsbefore editing any file
What the AI Sees
- FROZEN (score >= 0.80): Do not modify without explicit user approval
- STABLE (score 0.50-0.79): Proceed with caution, review intent first
- OPEN (score < 0.50): Safe to modify freely
Quick Start
Prerequisites
- Node.js >= 20
That's it. No Docker, no PostgreSQL, no external services.
1. Set Up a Repository (one command)
This single command:
- Creates a local SQLite database at
~/.wisegit/wisegit.db - Indexes your entire git history (462 commits in ~13 seconds)
- Creates
.mcp.jsonfor Claude Code auto-discovery - Creates
CLAUDE.mdrules that instruct AI to check before editing - Adds
.mcp.jsonto.gitignore
2. Enrich with Issue Context (optional)
This fetches referenced issues (e.g., #134 in commit messages), detects Won't Fix / By Design decisions, and boosts freeze scores for functions linked to those issues.
3. Done
Open the repo in Claude Code. It will automatically:
- Start the wisegit MCP server (via
.mcp.json) - Read the protection rules (via
CLAUDE.md) - Call
get_file_decisionsbefore editing any file
MCP Tools
MCP Resource: wisegit://manifest/{filePath} — decision manifest as auto-discoverable resource
MCP Prompt: check_before_edit — mandatory workflow prompt that returns the decision manifest before editing any file
LLM Intent Extraction Strategy
wisegit uses a smart fallback chain for extracting intent from NOISE commits:
Inside Claude Code, call extract_intent to retroactively recover intent for NOISE commits — uses Claude itself, no Ollama installation needed.
CLI Commands
Configure for Claude Code
Option A: Per-repo (recommended)
Run npx @sandip124/wisegit setup in any repo. It creates .mcp.json automatically.
Option B: Global registration
Option C: Manual .mcp.json
Create .mcp.json in your repo root:
Supported Languages
More languages can be added via Tree-sitter grammar configs in src/ast/languages/.
Issue Enrichment
A commit saying fix: handle null token #134 points to an issue containing reproduction steps, root cause, and explicit decision rationale — everything the commit message never says.
Supported platforms: GitHub, GitLab (Azure DevOps, Jira, Bitbucket planned)
Auth tokens: GITHUB_TOKEN / GH_TOKEN for GitHub, GITLAB_TOKEN for GitLab. Never stored by wisegit.
Issue-derived freeze signals
Freeze Score Signals
The freeze score is never stored directly — it's derived by replaying the event stream for each function. Signal categories:
Freeze score formula:
Protection signals produce the base score (weighted average of present signal categories, not additive sum); obsolescence signals produce the penalty. Obsolescence weights are adaptive — calibrated per-repository using Shannon entropy and Bayesian feedback. Falls back to hardcoded defaults when < 20 functions have signals. Age signal uses Weibull survival model [18] (k=0.7, lambda=2.4y); contributor expertise uses DOE model [19] (4 variables: contribution share, recency, duration, frequency).
Academic grounding: 24 published papers. See REFERENCE.md for full citations.
Cross-Repo Validation
Tested on 3 real-world open-source codebases:
Flask's core APIs (__init__, run, wsgi_app, url_for) correctly scored FROZEN (0.87+). Express routing primitives (paramCallback, Route, Router) correctly scored FROZEN/STABLE. Scores adapt to each codebase's history rather than producing uniform distributions.
See REFERENCE.md for detailed validation findings and implementation changes.
Legacy Codebase Evolution
wisegit is designed for codebases that have accumulated years of intentional decisions. The freeze score doesn't mean "never change this" — it means "understand these decisions before you change it."
Progressive migration, not shiny rewrites. Per Távora [12]: the business rules in messy code are correct and valuable. The technical debt is in the structure, not the decisions. wisegit protects the decisions while you fix the structure.
See REFERENCE.md for the full legacy evolution section with academic grounding (24 published papers).
Team Support
wisegit uses a three-layer architecture — no separate "team mode" needed:
JSONL format — one JSON object per line. Concurrent appends produce no git merge conflicts.
After a teammate pushes .wisegit/ changes, run wisegit sync to import them into your local cache.
See TEAM-ROADMAP.md for the full team architecture design.
Architecture
Environment Variables
Security
- Everything runs locally — only issue enrichment makes outbound API calls (opt-in via
wisegit enrich) - Append-only event store — decisions are never deleted, only added
- SQLite database stored at
~/.wisegit/wisegit.db— no network exposure - MCP tool inputs validated with strict Zod schemas (path traversal protection, length limits)
- Error messages sanitized before returning to MCP clients
- File writes check for symlinks before writing
- Config files parsed with allowlisted keys only (no prototype pollution)
Roadmap
- Phase 1 — Event store, AST chunking, commit classification, intent extraction, MCP server, CLI
- Phase 1.5 — Issue enrichment (GitHub, GitLab) with Won't Fix/By Design detection, freeze boost signals
- Phase 2 — Full freeze score: call graph + PageRank, theory gap detection (Naur death, forgotten patterns), co-change signals, Aranda signals, Ollama client, Go + Rust support
- Phase 4 — Override system (mandatory reason, time-boxed expiry, audit trail), branch context preservation (post-merge hook, snapshot storage, recovery)
- Phase A — Shared team knowledge layer:
.wisegit/directory with JSONL files for enrichments, overrides, branch contexts, and team config - Phase B — Team-aware manifests: theory holder tracking, risk levels (healthy/fragile/critical), team status + health commands
- Phase C — AI-era adaptations: commit origin detection (HUMAN/AI_REVIEWED/AI_UNREVIEWED), origin-weighted freeze scores
- Phase D — Override approval workflow, team health metrics
- Phase E — Adaptive obsolescence calibration: 8 obsolescence signals, entropy-calibrated weights, Bayesian feedback,
wisegit calibrateCLI
License
MIT
来源:README.md,提交 75fab44
工具
0版本历史
1- v0.1.1最新Sep 16, 2026
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