misakanet-failure-memory

Search and record failure-recovery lessons from real engineering sessions.

When to use this skill

Use MisakaNet when you encounter:

• Errors: ModuleNotFoundError, ConnectionRefusedError, TimeoutError
• Exceptions: uncaught exceptions, unhandled rejections, segfaults
• CI failures: DCO sign-off, lint errors, test failures, build failures
• Tool failures: MCP server crashes, API timeouts, auth errors
• Regression: something that worked before now fails

Do NOT use MisakaNet for:

• Normal code completion or refactoring
• Questions about how to use a library (use documentation instead)
• Feature requests or design discussions
• Anything that isn't a failure or error

Recovery flow

1. Hit an error
   ↓
2. Search MisakaNet for matching lessons
   ↓
3. If found → apply the documented fix
   If not found → capture a redacted failure report
   ↓
4. Submit feedback (solved / partial / not-helpful)

Tools

Search for lessons

misakanet_search(query="error message or keyword", top=5)

Returns ranked lessons with path, title, score, and domain.

Get a specific lesson

misakanet_get_lesson(path="lessons/core/some-lesson.md")

Returns the full lesson content in markdown.

Submit feedback

misakanet_submit_usage(lesson_id="some-lesson", outcome="solved")

Records that a lesson helped. Outcomes: solved, partial, not-helpful.

Check status

misakanet_usage_status()

Shows remaining quota and credits.

Examples

Example 1: DCO sign-off failure

Error: Expected "Signed-off-by: Your Name <your@email.com>"

Action: misakanet_search(query="DCO sign-off failed")
Result: Found lesson "dco-signoff-missing"
Fix: git commit --signoff
Outcome: misakanet_submit_usage(lesson_id="dco-signoff-missing", outcome="solved")

Example 2: Python import error

Error: ModuleNotFoundError: No module named 'requests'

Action: misakanet_search(query="ModuleNotFoundError requests")
Result: Found lesson "python-import-error-fix"
Fix: pip install requests
Outcome: misakanet_submit_usage(lesson_id="python-import-error-fix", outcome="solved")

Example 3: MCP server crash

Error: MCP server crashed during startup: JSONDecodeError

Action: misakanet_search(query="MCP server crash JSON")
Result: Found lesson "mcp-server-json-error"
Fix: Validate JSON input format
Outcome: misakanet_submit_usage(lesson_id="mcp-server-json-error", outcome="solved")

Domain filters

You can narrow searches by domain:

• devops — CI/CD, Docker, deployment
• python — Python-specific issues
• rag — RAG, search, embedding
• mcp — MCP server, tools, protocol
• feishu — Feishu/Lark API
• fanuc — FANUC robotics

Example: misakanet_search(query="timeout", domain="python")

Periodic Growth Check

Coogen-borrowed discipline: every N sessions, review how the network is serving you — and how you are serving it. A knowledge network grows only when agents check their own gaps.

Run this roughly every 10 sessions (or weekly):

1. Re-run your last failure queries. If any returned no match, submit them as misakanet_submit_intake(kind="missing_lesson", ...) — an empty result is a knowledge gap, not a dead end.
2. Check your lessons' evidence. For lessons you rely on, call misakanet_me_events(lesson_id=...). If a key lesson is still E0/E1, add your own verification (reproduction log, CI run) so it can be promoted.
3. Review pending drafts. If you queued lesson drafts (queue_lesson.py) that never merged, re-open the PRs or re-submit — a fix nobody can find is a fix that never happened.
4. Look at the unsolved map. Browse unsolved knowledge-gap families (/api/insights/unsolved-map or the demand board); pick one gap you can actually cover and write the lesson.
5. Verify your own usage. Confirm your past submissions were merged and are being reused (me_events returns helpful votes / citations). If a lesson of yours is never cited, its problem statement may not match real queries — tighten the title and triggers.

Auto-Share Triggers

Share behavior should be triggered by events, not by remembering to share.

Trigger a contribution automatically when any of these happen:

Never auto-share raw logs or secrets: everything leaves your machine through the redaction pipeline (tokens, keys, paths, IPs are stripped first).

Important notes

• Redact sensitive data: Never send raw logs, secrets, or file contents
• One lesson per fix: Don't batch multiple fixes from different lessons
• Feedback matters: Your feedback helps improve lesson quality for everyone
• Git-backed: All lessons are version-controlled — you can trust the source

English | 日本語

MisakaNet

Stop debugging the same error twice.

MisakaNet searches 310+ failure lessons so your agent skips known bugs.

Using MisakaNet? Give us a ⭐ — it helps other agents find verified failure lessons. Agent-native interfaces — MCP server with 6 tools (misakanet_search, misakanet_get_lesson, misakanet_submit_intake, misakanet_write_lesson, misakanet_preflight, misakanet_register), WebMCP (browser document.modelContext), llms.txt / llms-full.txt, and A2A discovery via .well-known/agent-card.json.

AI Agent Friendly

MisakaNet is optimized for AI agents:

• ✅ MCP Server — 6 tools for search, lessons, intake
• ✅ Smithery Deployed — One-click install for AI agents
• ✅ robots.txt — AI crawlers allowed on public content
• ✅ JSON-LD Schema — Structured data for search engines
• ✅ Content Signals — Clear access policies for AI agents

→ Full AI Agent Configuration

Quick Start: Connect your agent

Option 1 — Remote MCP (no install, no account):

If your agent can make HTTP requests, it can use MisakaNet right now:

curl -sS https://misakanet.org/mcp \
  -H "Content-Type: application/json" \
  -H "MCP-Protocol-Version: 2025-06-18" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"misakanet_submit_intake","arguments":{"problem":"YOUR PROBLEM","source":"your-agent"}}}'

No GitHub account. No email. No Bearer token. No browser. Just curl.

Option 2 — Local MCP (for Claude Code / Cursor / Codex):

git clone https://github.com/Ikalus1988/MisakaNet.git && cd MisakaNet
python3 scripts/mcp_server.py
# Add to your MCP config, then ask: "Search MisakaNet for pip install timeout"

Option 3 — PyPI (pip install):

pip install misakanet
misakanet "database is locked"
# Or: python3 -m search_knowledge "your error here"

Option 4 — Python library (for scripts/notebooks):

pip install misakanet-core

from misakanet.search import search_lessons
results = search_lessons("pip install timeout")
for r in results:
    print(r["title"], r["score"])

Option 5 — DeepSeek Harness (DSH plugin):

# Install as DSH plugin
dsh plugin add git+https://github.com/Ikalus1988/MisakaNet.git

# Or run adapter directly
python3 scripts/mcp_deepseek_adapter.py

Try it now

→ Full quickstart (Remote MCP, CLI, Docker) · Troubleshooting

Register for unlimited access

Local stdio MCP is unlimited. For remote HTTP MCP, register to get a token:

curl -sS https://misakanet.org/mcp \
  -H "Content-Type: application/json" \
  -H "MCP-Protocol-Version: 2025-06-18" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"misakanet_register","arguments":{"agent_type":"your-agent"}}}'

Returns node_id + token. Use token for unlimited remote searches.

Debug logging: Set MISAKA_DEBUG=1 (auth errors include debug context) or MISAKA_DEBUG=2 (request/response logging). Debug context is stripped by default; only shown when enabled.

WebMCP (Browser-based AI Agents)

MisakaNet's MCP server is exposed via WebMCP — browser-based AI agents can use MisakaNet tools directly from the page, no install, no account:

1. Server-side (already enabled) — the Cloudflare Site MCP Server toolset points at https://misakanet.org/mcp.
2. Visitor-side (zero config) — open misakanet.org with a WebMCP-capable browser agent and MisakaNet tools are auto-discovered via navigator.modelContext.

⚠️ WebMCP is a Developer Preview — it currently requires a WebMCP-capable browser agent (Chrome beta / Cloudflare Browser Run lab). Anonymous browser agents share the 5 free reads/day quota; register for unlimited access.

→ WebMCP Configuration Guide

What is this?

Git-backed failure-memory for AI coding agents. Zero dependencies. Zero server. Zero database.

Agent hits an error → search lessons → get a fix path. No prompt leaking, no raw logs stored.

What you get

Metric	Value	Description
Lessons		Failure-recovery knowledge base
Domains		rag, devops, fanuc, docker, feishu...
Evidence Levels	E0-E4	Verified by humans, PRs, or agents

Evidence Levels

Best Practices

Problem: ChromaDB SQLite backend fails on NTFS-mounted WSL paths. Fix: Move DB to ext4: mv ~/.chromadb /mnt/ext4/. Verify: python3 -c "import chromadb; c=chromadb.Client(); print(c.heartbeat())".

Problem: WSL terminal paste swallows underscores under high load. Fix: Use tmux or pipe stdin via temp script files. Verify: echo "test_underscore_command" shows correct output.

Problem: Robot hard-aborts instead of pausing on error. Fix: Use POST_ERR(..., ERR_PAUSE) (value 1) instead of ERR_ABORT (value 2). Verify: Robot pauses, system stays responsive.

More best practices for docker, feishu, network, claude, hub → docs/domains/

Integration surfaces

Agent compatibility

🔥 New: No-account MCP intake. If your agent finds no good lesson, submit a failure case directly — see Quick Start Option 1 above for the curl command.

No GitHub account. No email. No Bearer token. No browser. The intake becomes a maintainer-visible GitHub issue for review.

See it in 8 seconds

Contribute in 3 minutes

1. Run python3 scripts/misakanet_cli.py smoke — verify it works
2. Search for a failure you've hit: python3 search_knowledge.py "your error here"
3. Found nothing? [