deadends-dev

deadends.dev

Stop AI agents from repeating known failures.

When AI coding agents hit an error, they waste tokens retrying approaches that are known to fail. deadends.dev gives agents instant access to what NOT to try, what actually works, and what error comes next — across 2,089 errors in 51 domains.

90% Precision@1 — the top lookup result matches the correct domain 9 out of 10 times. 0.935 MRR — correct results consistently rank first. See the full Data Quality Dashboard.

Website: deadends.dev · MCP Server: Smithery · PyPI: deadends-dev · API: /api/v1/index.json

Why Use This?

Without deadends.dev	With deadends.dev
Agent tries sudo pip install → breaks system Python → wastes 3 retries	Agent sees "dead end: sudo pip — fails 70%" → skips it immediately
Agent googles, reads Stack Overflow, tries 4 approaches	Agent gets the 95%-success workaround on the first call
Agent fixes error A, gets confused by error B	Agent knows "A leads to B 78% of the time" → handles both

What makes this different from asking an LLM?

• Deterministic: Same error → same answer, every time. No hallucination.
• Community-validated: Fix success rates are updated from real outcome reports.
• Error chains: Conditional probabilities (A→B) that LLMs can't provide.
• Sub-millisecond: Local regex matching, no API roundtrip.

Quick Start (30 seconds)

pip install deadends-dev
deadends "CUDA error: out of memory"

MCP Server (Claude Desktop / Cursor)

Add to ~/.claude/claude_desktop_config.json:

{
  "mcpServers": {
    "deadend": {
      "command": "python",
      "args": ["-m", "mcp.server"],
      "cwd": "/path/to/deadends.dev"
    }
  }
}

Or install via Smithery (no local setup):

npx -y @smithery/cli@latest install deadend/deadends-dev --client claude

MCP Unauthorized 빠른 해결 가이드 (사람용)

deadend: calling "initialize": sending "initialize": Unauthorized 에러가 보이면 아래를 순서대로 그대로 실행/확인하세요.

• 로컬 서버 모드인지, 원격(Smithery) 모드인지 하나만 사용

# 로컬 서버 확인 (정상 시 툴 목록이 출력됨)
python -m mcp.server --help

• Claude Desktop 설정 파일 점검 (cwd는 실제 경로여야 함)

cat ~/.claude/claude_desktop_config.json

• 로컬 서버 직접 실행 테스트

cd /path/to/deadends.dev
python -m mcp.server

• Smithery 모드라면 재설치(토큰/설정 꼬임 복구)

npx -y @smithery/cli@latest uninstall deadend/deadends-dev --client claude
npx -y @smithery/cli@latest install deadend/deadends-dev --client claude

• 마지막으로 Claude Desktop 완전 재시작

# macOS 예시
osascript -e 'quit app "Claude"'
open -a Claude

팁: Unauthorized는 보통 잘못된 cwd, 중복 서버 설정(로컬+원격 동시), 또는 만료된 인증 상태에서 발생합니다.

Python SDK

from generator.lookup import lookup, batch_lookup, search

# Single error lookup
result = lookup("ModuleNotFoundError: No module named 'torch'")

# What NOT to try (saves tokens and time)
for d in result["dead_ends"]:
    print(f"AVOID: {d['action']} — fails {int(d['fail_rate']*100)}%")

# What actually works
for w in result["workarounds"]:
    print(f"TRY: {w['action']} — works {int(w['success_rate']*100)}%")

# Batch lookup (multiple errors at once)
results = batch_lookup(["error1", "error2", "error3"])

Example Response

## ModuleNotFoundError: No module named 'X' (Python 3.11+)
Resolvable: true | Fix rate: 0.88

### Dead Ends (DO NOT TRY):
- pip install X with system Python (fails 70%): venv not activated

### Workarounds (TRY THESE):
- Create venv, activate, then pip install (works 95%)
- Use python -m pip install instead of bare pip (works 90%)

MCP Tools (9)

Tool	Description
lookup_error	Match an error message against 2000+ known patterns
get_error_detail	Full canon by ID
list_error_domains	All 51 domains with counts
search_errors	TF-IDF keyword search across all domains
list_errors_by_domain	All errors in a domain
batch_lookup	Look up multiple errors at once (max 10)
get_domain_stats	Domain quality metrics and confidence levels
get_error_chain	Traverse the error transition graph
report_outcome	Report whether a workaround worked (feeds back into success rates)

API Endpoints

Endpoint	Description
/api/v1/match.json	Lightweight regex matching (fits in context window)
/api/v1/index.json	Full error index with metadata
/api/v1/{id}.json	Individual ErrorCanon
/api/v1/openapi.json	OpenAPI 3.1 spec
/api/v1/stats.json	Dataset quality metrics by domain
/api/v1/errors.ndjson	NDJSON streaming
/llms.txt	LLM-optimized listing (llmstxt.org)
/dashboard/	Data quality dashboard

Covered Domains (51)

Domain	Errors	Examples
Python	88	ModuleNotFoundError, TypeError, KeyError, MemoryError, RecursionError
Node	70	ERR_MODULE_NOT_FOUND, EACCES, EADDRINUSE, heap OOM, ERR_REQUIRE_ESM
Docker	65	no space left, exec format error, bind address in use, healthcheck
Kubernetes	61	CrashLoopBackOff, ImagePullBackOff, OOMKilled, RBAC forbidden, HPA
Git	60	failed to push, merge conflicts, detached HEAD, stash apply, tags
CUDA	57	OOM, device-side assert, NCCL, cuDNN, tensor device mismatch
Go	54	nil pointer, unused import, interface conversion, slice out of range
Java	54	NullPointerException, ClassNotFound, OutOfMemoryError, connection pool
Database	52	deadlock, connection pool, slow query, replication lag
AWS	51	AccessDenied, S3 NoSuchBucket, Lambda timeout, CloudFormation rollback
.NET	50	NullReferenceException, LINQ translation, DI circular, EF concurrency
ROS 2	50	node spin, launch error, QoS mismatch, tf2 transform
TypeScript	49	TS2307, TS2322, TS2345, TS2532, TS7053
Rust	48	E0382 borrow, E0308 mismatch, E0277 trait, E0106 lifetime
+ 37 more domains	40+ each	CI/CD, PHP, Terraform, Networking, Next.js, React, pip, Android, ...

Data Quality

All metrics are publicly available on the Data Quality Dashboard:

• 2,089 error entries across 51 domains
• Benchmark: 90% Precision@1, 95% Precision@3, 0.935 MRR
• Error transition graph: 4,330+ edges connecting related errors
• Community feedback loop: report_outcome updates fix success rates from real usage

Contributing

See CONTRIBUTING.md for full details.

• Submit a new error
• Report a workaround result
• Use report_outcome via MCP after trying a workaround

Development

pip install -e ".[dev]"

python -m generator.pipeline          # Full pipeline
python -m generator.build_site        # Build static site
python -m generator.validate          # Validate data + site
python -m pytest tests/ -v            # Run tests
ruff check generator/ tests/          # Lint
python benchmarks/run_benchmark.py    # Run benchmarks

SEO 점검 가이드 (모든 페이지 공통)

아래 명령은 템플릿에 핵심 SEO 신호가 있는지 빠르게 점검합니다.

python - <<'PY'
from pathlib import Path
files=[
  'generator/templates/index.html',
  'generator/templates/domain.html',
  'generator/templates/error_summary.html',
  'generator/templates/page.html',
  'generator/templates/search.html',
  'generator/templates/dashboard.html',
]
required=[
  '<title',
  'meta name="description"',
  'meta name="robots"',
  'link rel="canonical"',
  'meta property="og:title"',
  'meta name="twitter:card"',
]
for f in files:
    txt=Path(f).read_text()
    missing=[r for r in required if r not in txt]
    print(f'✅ {f}' if not missing else f'❌ {f} missing: {", ".join(missing)}')
PY

실제 빌드 결과물까지 확인하려면:

python -m generator.build_site
python -m http.server -d public 8080

그 후 브라우저에서 아래를 점검:

• view-source:http://localhost:8080/search/
• view-source:http://localhost:8080/dashboard/
• canonical / og / twitter / JSON-LD 유효성

PyPI 업로드 매뉴얼 (복붙용)

릴리즈 전 체크:

git pull --rebase
python -m pip install --upgrade pip build twine
python -m pytest -q

배포 버전 갱신(예: pyproject.toml):

# 버전 문자열 검색
rg '^version\\s*=\\s*".*"' pyproject.toml

빌드:

rm -rf dist/ build/ *.egg-info
python -m build

아티팩트 검증:

python -m twine check dist/*

TestPyPI 업로드(권장):

python -m twine upload --repository testpypi dist/*

TestPyPI 설치 검증:

python -m venv .venv-testpypi
source .venv-testpypi/bin/activate
python -m pip install -U pip
python -m pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple deadends-dev
deadends "ModuleNotFoundError: No module named 'torch'"
deactivate

실제 PyPI 업로드:

python -m twine upload dist/*

업로드 후 최종 확인:

python -m venv .venv-release-check
source .venv-release-check/bin/activate
python -m pip install -U pip
python -m pip install deadends-dev
deadends "CUDA error: out of memory"
deactivate

Changelog

v0.8.0

• Benchmark suite: 20 error scenarios, Precision@1=90%, MRR=0.935
• Data quality dashboard at /dashboard/ — transparent metrics
• Outcome feedback loop: report_outcome → aggregated stats → fix_success_rate updates
• Usage analytics: anonymous tool usage tracking (domain/match only, no PII)
• Community contribution: GitHub Issue templates for new errors and workaround reports
• TF-IDF search: improved relevance with smoothed IDF scoring
• Error transition graph: materialized graph with 4,330+ edges, hub node analysis
• 9 MCP tools (added report_outcome)

v0.7.0

• Expanded to 2089 error entries across 51 domains (from 1028/20)
• Added 23 new domains
• Fixed 73 regex patterns that didn't match their own signatures

v0.5.0

• page_url field added to index.json, errors.ndjson, and all SDK/MCP responses
• SEO fixes for canonical summary URLs

v0.4.0

• Initial public release with 1028 error entries across 20 domains

License

MIT (code) · CC BY 4.0 (data)

Ops Docs

• SEO 운영 가이드: docs/SEO_OPERATIONS_GUIDE.md
• PyPI 릴리즈 매뉴얼: docs/PYPI_RELEASE_MANUAL.md