deadends-dev 0.2.0

Structured failure knowledge for AI agents — dead ends, workarounds, error chains

deadends.dev

Structured failure knowledge for AI coding agents.

501 error entries across 14 domains. When AI agents encounter errors, they waste tokens on approaches that are known to fail. deadends.dev tells agents what NOT to try, what actually works, and what error comes next.

Installation

pip install deadends-dev

Requirements: Python 3.10+

MCP Server

The MCP server exposes 3 tools for AI coding agents:

Tool	Description
lookup_error	Match an error message against 143 known patterns. Returns dead ends, workarounds, and error chains.
get_error_detail	Get full details for a specific error by ID (e.g., python/modulenotfounderror/py311-linux).
list_error_domains	List all 14 error domains and their counts.

Local (Claude Desktop / Cursor)

Add to ~/.claude/claude_desktop_config.json:

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

Hosted (Smithery)

Available on Smithery — no local setup required.

Example Response

When an agent encounters ModuleNotFoundError: No module named 'torch', the lookup_error tool returns:

## 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%)

Quick Start — Python SDK

from generator.lookup import 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)}%")

Quick Start — CLI

pip install deadends-dev
deadends "CUDA error: out of memory"
deadends --list  # show all known errors

API Endpoints

Endpoint	Description
/api/v1/match.json	Lightweight regex matching (fits in context window)
/api/v1/index.json	Full error index with all metadata
/api/v1/{domain}/{slug}/{env}.json	Individual error canon
/api/v1/openapi.json	OpenAPI 3.1 spec
/llms.txt	LLM-optimized error listing (llmstxt.org standard)
/llms-full.txt	Complete database dump
/.well-known/ai-plugin.json	AI plugin manifest

Covered Domains (14)

Domain	Errors	Examples
python	86	ModuleNotFoundError, TypeError, KeyError, MemoryError, RecursionError
node	61	ERR_MODULE_NOT_FOUND, EACCES, EADDRINUSE, heap OOM, ERR_REQUIRE_ESM
docker	43	no space left, exec format error, bind address in use, healthcheck
git	34	failed to push, merge conflicts, detached HEAD, stash apply, tags
kubernetes	34	CrashLoopBackOff, ImagePullBackOff, OOMKilled, RBAC forbidden, HPA
go	32	nil pointer, unused import, interface conversion, slice out of range
nextjs	30	hydration failed, dynamic server, searchParams, metadata, Suspense
aws	29	AccessDenied, S3 NoSuchBucket, Lambda timeout, SQS, Secrets Manager
react	28	invalid hook call, too many re-renders, unique key, context, act()
terraform	27	state lock, cycle, provider not found, moved block, backend init
cuda	25	OOM, device-side assert, NCCL, cuDNN, nvcc not found, kernel image
typescript	25	TS2307, TS2322, TS2345, TS2532, TS7053, TS2769, TS18048
pip	24	build wheel failed, conflicting deps, externally-managed, hash mismatch
rust	23	E0382 borrow, E0308 mismatch, E0277 trait, E0106 lifetime, E0507

ErrorCanon Data Format

Each error is a JSON file with:

{
  "error": { "signature": "...", "regex": "...", "domain": "..." },
  "verdict": { "resolvable": "true|partial|false", "fix_success_rate": 0.88 },
  "dead_ends": [{ "action": "...", "why_fails": "...", "fail_rate": 0.75 }],
  "workarounds": [{ "action": "...", "success_rate": 0.92, "how": "..." }],
  "transition_graph": { "leads_to": [...], "preceded_by": [...] }
}

AI Agent Integration

Every page on deadends.dev includes 8 machine-readable formats:

1. JSON API — RESTful error data at /api/v1/{id}.json
2. match.json — Compact regex-only file (load entire DB into context)
3. JSON-LD — Schema.org TechArticle + FAQPage in every <head>
4. ai-summary — <pre id="ai-summary"> with KEY=VALUE pairs
5. llms.txt — llmstxt.org standard
6. OpenAPI — Full API specification
7. ai-plugin.json — Plugin discovery manifest
8. robots.txt — All AI crawlers explicitly welcomed

Development

pip install -e ".[dev]"

# Full pipeline (validate → generate → build → test)
python -m generator.pipeline

# Individual steps
python -m generator.bulk_generate     # Generate canons from seeds
python -m generator.build_site        # Build static site
python -m generator.validate          # Validate data + site
python -m pytest tests/ -v            # Run tests

Contributing

Add error definitions to generator/bulk_generate.py or create JSON files directly in data/canons/.

python -m generator.validate --data-only  # Validate before submitting

License

MIT (code) · CC BY 4.0 (data)