Apache-2.0、零依賴的 Git 故障經驗記憶庫（290 則課程），透過專屬 MCP 轉接器接入 DeepSeek Harness，提供 deepseek.recovery.* 故障復原工具。
DSH 整合
相容
作者聲明
安全稽核
未稽核
最後核實
2026-08-21
授權條款
Apache-2.0
01它能幫你完成什麼？
Give DeepSeek Harness a shared failure-memory so it searches verified debugging lessons instead of re-debugging known errors
BM25 keyword matches across 290 indexed lessons (problem → root cause → fix → verify), served through the DeepSeekHarness MCP adapter as `deepseek.recovery.*` tools
DeepSeekHarness users who want harness-level failure recovery without a server, database or extra dependencies
Search 290 verified failure-recovery lessons locally from the command line
Zero-dependency BM25 keyword search results over Markdown lessons, runnable with Python stdlib only after `git clone`
Developers and agents debugging real failures (DCO, pip, token, MCP, encoding, CI) who want an offline-first lesson lookup
Feed unmatched failures back into the network without a GitHub account or email
Redacted intake submitted via the `misakanet_submit_intake` remote MCP tool, becoming a maintainer-visible GitHub issue labeled `intake`, `mcp-intake`, `pending-review`
Agents whose task stalls on a failure with no matching lesson; no GitHub account, email, Bearer token or browser required
02如何將外掛接入 DeepSeek Harness？
先決條件
- Python 3.10+ (badge: python-3.10+) — the core engine is zero-dep, pure Python stdlib
- git — lessons ship as Markdown files in the repository
安裝步驟
- 01
Clone the repo: `git clone https://github.com/Ikalus1988/MisakaNet.git && cd MisakaNet`
$ git clone https://github.com/Ikalus1988/MisakaNet.git && cd MisakaNet
- 02
For DeepSeekHarness, run the recovery adapter: `python3 scripts/mcp_deepseek_adapter.py`
- 03
Alternative: run the standard MCP server `python3 scripts/mcp_server.py` and add it to your MCP config
- 04
Optional local search: `python3 search_knowledge.py "your error here"`
驗證整合成功
- Run the smoke check: `python3 scripts/misakanet_cli.py smoke` — the README's 'Contribute in 3 minutes' flow uses it to verify it works
03DSH 整合程度與能力邊界
MCP-compatible adapter (`python3 scripts/mcp_deepseek_adapter.py`) exposing `deepseek.recovery.*` tools for harness-level failure recovery
DeepSeekHarness recovery adapter
harness-level failure events from DeepSeekHarness→`deepseek.recovery.*` MCP tools for failure recovery inside the harness
runs a local MCP adapter process (`python3 scripts/mcp_deepseek_adapter.py`)Zero-dependency BM25 lesson search
an error message or query string→matching failure-recovery lessons (problem → root cause → fix → verify) via pure-Python BM25 retrieval
MCP server for any MCP client
MCP config of Claude / Codex / local agents→lesson search and intake tools, e.g. ask: "Search MisakaNet for pip install timeout"
runs a local MCP server process (`python3 scripts/mcp_server.py`)Remote MCP intake (no account)
a redacted failure description→`misakanet_submit_intake` call to `https://misakanet.org/mcp` creating a maintainer-visible GitHub issue for review
network call to the remote endpoint https://misakanet.org/mcpcreates a GitHub issue labeled intake / mcp-intake / pending-review; submissions must be redacted (no secrets, no raw logs)
04適合誰？何時不該用？
適合
- DeepSeekHarness users who want harness-level failure recovery without a server, database or extra dependencies
- Developers and agents debugging real failures (DCO, pip, token, MCP, encoding, CI) who want an offline-first lesson lookup
- Agents whose task stalls on a failure with no matching lesson; no GitHub account, email, Bearer token or browser required
不適合
- Lessons are community-contributed, so retrieved commands must be reviewed before execution — the README tells users to always sandbox their agent before running retrieved commands.
05相容性、維護與安全提醒
- Lessons are community-contributed, so retrieved commands must be reviewed before execution — the README tells users to always sandbox their agent before running retrieved commands.
- MisakaNet is purpose-built for failure-recovery knowledge: it is not a general-purpose memory system, not an agent runtime, and not a vector database or RAG system — BM25 keyword search only.
- Local clone + search is fully offline, but the remote MCP endpoint (https://misakanet.org/mcp) and lesson intake require network access; intake submissions are reviewed by maintainers before publication.
Apache-2.0 · actively maintained (latest release v2.17.1, 2026-08-16; last push 2026-08-19)
06常見問題
MisakaNet 如何接入 DeepSeek Harness？
透過專屬 DeepSeekHarness MCP 轉接器：複製儲存庫後執行 `python3 scripts/mcp_deepseek_adapter.py`。這是一個 MCP 相容轉接器，暴露 `deepseek.recovery.*` 工具用於 harness 層級的故障復原。完整的安裝、驗證與降級策略請參閱 docs/integration/deepseek-harness.md。
它是 dsh 原生整合還是 MCP 工具？
以 MCP 工具集形式整合，而非原生執行環境。標準 MCP 伺服器（`python3 scripts/mcp_server.py`）同樣相容於 Claude、Codex 等本機 agent；也可直接使用託管端點 https://misakanet.org/mcp，不需複製儲存庫。
使用前需要安裝什麼？
Python 3.10+ 與 git。核心引擎零依賴——純 Python 標準函式庫，無向量資料庫、無伺服器、無資料庫。可選擴充功能透過 `pip install misakanet[semantic|hub|feishu]` 安裝。
資料會上傳嗎？
本機複製與搜尋完全離線：`git clone` + `python3 search_knowledge.py`，不會外洩提示詞、不儲存原始日誌。只有選擇性的遠端 MCP 提交會存取 https://misakanet.org/mcp，且提交內容必須去識別化，由維護人員審核後才有可能轉為課程。
有什麼限制？
MisakaNet 只做故障復原知識這一件事——不是通用記憶系統、不是 agent 執行環境、也不是 RAG 系統。課程由社群貢獻，執行其中檢索到的指令前務必自行審查，並讓 agent 在沙盒環境中執行。
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08資料與來源
**DeepSeekHarness Adapter** | MCP-compatible adapter exposing `deepseek.recovery.*` tools for harness-level failure reco…
| DeepSeekHarness | `python3 scripts/mcp_deepseek_adapter.py` |
此頁面根據專案公開文件、儲存庫中繼資料與 DSH Plugins 的結構化解析所產生；最後核實於 2026-08-21。發現錯誤？提交更正。