From anthropic-pack
Debugs Claude API issues like context overflow, tool failures, streaming corruption using Python token counting, tool descriptions, and resilient streaming.
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin anthropic-packThis skill is limited to using the following tools:
```python
Diagnoses and fixes Anthropic Claude API errors by HTTP status code, covering invalid requests, authentication, permissions, rate limits, and overloads.
Integrates Anthropic Claude Messages API with @anthropic-ai/sdk for streaming, prompt caching, tool use, vision, and rate limit handling in Node.js and Cloudflare Workers.
Chats with Anthropic Claude models (Opus, Sonnet, Haiku) via API. Supports 200K token contexts, vision, and tool use for code analysis or document summarization.
Share bugs, ideas, or general feedback.
# Symptom: invalid_request_error about token count
# Diagnosis: pre-check with Token Counting API
import anthropic
client = anthropic.Anthropic()
count = client.messages.count_tokens(
model="claude-sonnet-4-20250514",
messages=conversation_history,
system=system_prompt
)
print(f"Input tokens: {count.input_tokens}")
# Claude Sonnet: 200K context, Claude Opus: 200K context
# Fix: truncate oldest messages or summarize
def trim_conversation(messages: list, max_tokens: int = 180_000) -> list:
"""Keep recent messages within token budget."""
# Always keep first (system context) and last 5 messages
if len(messages) <= 5:
return messages
return messages[:1] + messages[-5:] # Crude but effective
# Symptom: Claude responds with text instead of calling tools
# Diagnosis checklist:
# 1. Tool description must clearly state WHEN to use the tool
# 2. User message must match the tool's trigger condition
# BAD description (too vague):
{"name": "search", "description": "Search for things"}
# GOOD description (clear trigger):
{"name": "search_products", "description": "Search the product catalog by name, category, or price range. Use whenever the user asks about products, pricing, or availability."}
# Force tool use if needed:
message = client.messages.create(
model="claude-sonnet-4-20250514",
max_tokens=1024,
tools=tools,
tool_choice={"type": "any"}, # Must call at least one tool
messages=[{"role": "user", "content": "Find products under $50"}]
)
# Symptom: stream ends prematurely or text is garbled
# Cause: network interruption, proxy timeout, or large response
# Fix: implement reconnection with content tracking
def resilient_stream(client, **kwargs):
"""Stream with reconnection on failure."""
collected_text = ""
max_retries = 3
for attempt in range(max_retries):
try:
with client.messages.stream(**kwargs) as stream:
for text in stream.text_stream:
collected_text += text
yield text
return # Success
except Exception as e:
if attempt == max_retries - 1:
raise
# Note: Claude streams are NOT resumable
# Must restart from beginning
collected_text = ""
print(f"Stream interrupted, retrying ({attempt + 1}/{max_retries})")
| Stop Reason | Meaning | Action |
|---|---|---|
end_turn | Normal completion | Expected |
max_tokens | Hit token limit | Increase max_tokens |
stop_sequence | Hit stop sequence | Check stop_sequences array |
tool_use | Wants to call a tool | Process tool call and continue |
# Debug unexpected truncation
msg = client.messages.create(
model="claude-sonnet-4-20250514",
max_tokens=4096, # Was it too low?
messages=[{"role": "user", "content": long_prompt}]
)
print(f"Stop reason: {msg.stop_reason}")
print(f"Output tokens: {msg.usage.output_tokens}")
print(f"Max tokens: 4096")
# If output_tokens == max_tokens, response was truncated
# Checklist for quality issues:
# 1. System prompt too long or contradictory?
# 2. Conversation history too noisy (too many turns)?
# 3. Wrong model for task complexity?
# 4. Temperature too high for deterministic tasks?
# Debug: log the full request for review
import json
request_params = {
"model": model,
"max_tokens": max_tokens,
"system": system[:200] + "...", # Truncated for logging
"message_count": len(messages),
"temperature": temperature,
}
print(f"Request config: {json.dumps(request_params, indent=2)}")
# Test specific model availability
for model in claude-haiku-4-20250514 claude-sonnet-4-20250514 claude-opus-4-20250514; do
echo -n "$model: "
curl -s -o /dev/null -w "%{http_code}" https://api.anthropic.com/v1/messages \
-H "x-api-key: $ANTHROPIC_API_KEY" \
-H "anthropic-version: 2023-06-01" \
-H "content-type: application/json" \
-d "{\"model\":\"$model\",\"max_tokens\":8,\"messages\":[{\"role\":\"user\",\"content\":\"hi\"}]}"
echo
done
For load testing, see anth-load-scale.