Trace Analysis — Single-Session Deep Dive

Use this skill when you want to understand WHY a specific session (cloud or local) behaved the way it did — beyond summary metrics.

When to Use

• A cloud-agent PR's audit comment shows a compliance failure.
• A run wasted sim budget with few gate-passers.
• You want to verify whether a skill/prompt change actually changed agent behavior.
• A local session went down an unexpected path and you want to understand why.

Step 1: Identify the target session

The user provides one of:

• A PR number (e.g., "deep dive PR #21")
• A Cursor agent ID (e.g., "analyze bc-7c8329a6-...")
• A local transcript UUID (e.g., "analyze 474c9e21-...")
• A session directory (e.g., "analyze 20260604-001")
• "the latest cloud run" or "the latest session"

Determine session type

Step 2: Pull the trace

Cloud sessions

uv run python3 scripts/audit_cloud_trace.py \
  --agent-id <bc-id> \
  --output /tmp/trace.json

If the trace is expired (0 events), check the HF storage bucket:

# List available traces in the bucket
hf buckets ls <hf-user>/alpha-mining-traces/traces/

Or in Python:

from huggingface_hub import list_bucket_files
for f in list_bucket_files('<hf-user>/alpha-mining-traces', path_prefix='traces/'):
    if 'bc-' in f.path:
        print(f.path)

If the trace is gone entirely, work from the PR body, audit comment, and git diff.

Local sessions

Read the transcript JSONL directly:

TRANSCRIPT="agent-transcripts/<uuid>/<uuid>.jsonl"
wc -l "$TRANSCRIPT"  # message count

Extract tool calls:

import json
tools = []
with open('<path>.jsonl') as f:
    for line in f:
        d = json.loads(line)
        if d.get('role') == 'assistant':
            for item in (d.get('message',{}).get('content',[]) or []):
                if isinstance(item, dict) and item.get('type') == 'tool_use':
                    tools.append({'name': item['name'], 'input': item['input']})
print(f'{len(tools)} tool calls')
for t in tools:
    name = t['name']
    inp = t['input']
    if name == 'Shell':
        print(f"  Shell: {inp.get('command','')[:120]}")
    elif name == 'Read':
        print(f"  Read: {inp.get('path','')}")
    elif name in ('StrReplace', 'Write'):
        print(f"  {name}: {inp.get('path','')}")
    else:
        print(f"  {name}: {json.dumps(inp)[:120]}")

Also read the session artifacts if they exist:

• data/sessions/<id>/meta.md — strategy and research question
• data/sessions/<id>/results.md — what was tested
• data/sessions/<id>/learnings.md — what the agent concluded

Step 3: Reconstruct the decision timeline

Walk through the trace/transcript chronologically. Focus on:

Context gathering phase:

• What did the agent read first? Did it read mining-session before acting?
• Did it read data/knowledge/rules/ and data/knowledge/dead_zones/?
• Did it check the current data/book/ for the self-corr baseline?
• Did it consult data/factors/ for prior coverage?

Strategy selection:

• What strategy did it choose and why?
• Did it follow the adaptive strategy decision tree in mining-session?

Signal generation phase:

• How many simulation batches were submitted?
• What families/templates did it explore?
• Did it waste sims on families in data/knowledge/dead_zones/?
• Did it respect the "3 variants same BRAIN failure -> pivot" stop condition?

Polling and results:

• Did it use hf_poll.py or ad-hoc --stats loops?
• How long did it wait for results?

Verification:

• Did it run pnl_correlation.py --vs-book for every candidate?
• Did it correctly interpret SELF_CORRELATION: PENDING from brain_check?
• Did it create submit-*.md queue entries for verified candidates?

Reporting:

• Did it follow the experiment-reporting template?
• Was the diff append-only V2 (cloud) or properly scoped (local)?

Step 4: Classify failure modes

Map each issue to an actionable fix:

Step 5: Present findings

Summarize the analysis:

1. Timeline: what the agent did, in what order, with time estimates
2. Budget efficiency: sims used vs gate-passers found
3. Failure modes: classified list with recommended fixes
4. Proposed changes: specific edits to skills, prompt, or knowledge files

The user decides which fixes to apply. Changes are committed in the current local session.