clawtrace-self-evolve（爪痕自我进化）

ClawTrace Self-Evolve Skill

You have access to ClawTrace, an observability platform that captures and analyzes all your trajectories. Use this skill to understand your own performance and improve over time.

When to Use This Skill

Invoke this skill when any of the following is true:

• A run just completed and cost more than expected
• A run failed or produced unexpected output
• You've completed 5 or more runs since your last self-analysis
• Your session is ending and you want to log learnings
• You notice repeated failures on the same type of task

How to Call the Endpoint

Send a POST request to https://api.clawtrace.ai/v1/evolve/ask.

Authentication uses your observe key, which is already set in your environment as CLAWTRACE_OBSERVE_KEY.

import httpx
import os

def ask_tracy(question: str, trace_id: str = None, session_id: str = None) -> str:
    """Ask Tracy to analyze trajectories and return recommendations."""
    observe_key = os.environ.get("CLAWTRACE_OBSERVE_KEY", "")
    if not observe_key:
        return "ClawTrace observe key not configured."

    payload = {"question": question}
    if trace_id:
        payload["trace_id"] = trace_id
    if session_id:
        payload["session_id"] = session_id

    full_response = []

    with httpx.stream(
        "POST",
        "https://api.clawtrace.ai/v1/evolve/ask",
        headers={
            "Authorization": f"Bearer {observe_key}",
            "Content-Type": "application/json",
        },
        json=payload,
        timeout=120,
    ) as response:
        event_type = ""
        for line in response.iter_lines():
            if line.startswith("event: "):
                event_type = line[7:]
            elif line.startswith("data: ") and event_type == "text":
                import json
                data = json.loads(line[6:])
                full_response.append(data.get("text", ""))

    return "".join(full_response)

What to Ask — Question Templates

Use these questions for maximum insight. Substitute actual values for placeholders.

After a costly run

"My last run cost significantly more than usual. The trace_id is {trace_id}. 
What caused the cost spike and what should I change to prevent it?"

After a failed run

"My last run failed. The trace_id is {trace_id}. 
What step failed, what was the input that caused it, and how should I handle this case differently?"

Periodic self-analysis (every 5 runs)

"Analyze my last 10 trajectories. What patterns do you see in my failures? 
Where am I spending the most tokens? What one change would have the biggest impact on cost and reliability?"

Before a high-stakes task

"I'm about to execute a complex multi-step task. Based on my recent trajectory history, 
what are my most common failure modes and how should I guard against them?"

Context window management

"Is my context window growing too large across sessions? 
Review my recent traces and tell me if I should trim my history and when."

Tool call efficiency

"Which of my tool calls are taking the longest or failing most often? 
What can I change to make my tool usage more reliable and efficient?"

How to Act on Recommendations

When Tracy responds, follow these rules:

1. Extract the core recommendation. Tracy will identify 1–3 specific changes. Focus on the highest-impact one.

1. Apply changes to this session immediately. If Tracy says your context window is bloated, trim history now before continuing.

1. Log the insight to your MEMORY.md. Record what you learned so future sessions benefit:

   ## ClawTrace Learnings — {date}
   - Issue: {what Tracy found}
   - Fix: {what I changed}
   - Impact: {expected improvement}

1. Confirm the change worked. After applying a recommendation, mention it in your next response: "Based on trajectory analysis, I've adjusted X to prevent Y."

1. Do not ask Tracy more than twice per session unless a new failure occurs. Each call consumes credits and Tracy's value is in patterns across many runs, not micro-optimization within one session.

Example — Full Interaction

# End of a session — periodic self-review
insight = ask_tracy(
    "Analyze my performance over the last 5 trajectories. "
    "What's costing the most, what's failing, and what's the single most important thing to fix?"
)

# Tracy responds with analysis
# You read it, extract the recommendation, and act:
# e.g. "Your context window averages 1,200 messages per LLM call. 
#       Trim to last 50 messages. This would cut input cost by ~90%."

# Apply immediately:
# → truncate conversation history to last 50 messages
# → log to MEMORY.md
# → confirm in next output

Important Notes

• Tracy has access to your real trajectory data. Her analysis reflects actual runs, not simulations.
• Responses stream as SSE — parse the text events to build the full response.
• The session_id returned in the first SSE event can be reused to continue a conversation with Tracy across multiple calls.
• Tracy respects tenant isolation — she can only see your own trajectories.
• If you don't have a specific trace_id, omit it and Tracy will analyze recent trajectories automatically.