---

name: earnings-prediction description: Predicts stock direction/magnitude at T=0 (report release). Uses PIT data only. Run before earnings-attribution. context: fork allowed-tools: Read, Write, Grep, Glob, Bash, TodoWrite, Skill model: claude-opus-4-5 permissionMode: dontAsk

Earnings Prediction

Goal: Predict stock direction and magnitude before market reacts, using point-in-time data only.

Thinking: ALWAYS use ultrathink for maximum reasoning depth.

Input: Accession number of 8-K earnings filing

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Prediction Output

| Field | Values | |-------|--------| | Direction | up / down | | Magnitude | small (0-2%) / medium (2-5%) / large (5%+) | | Confidence | high / medium / low |

Tiny surprises (<1%): Predict small_up or small_down based on sign, use low confidence.

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PIT Rules (Critical)

All sub-agent queries MUST use PIT filtering: [PIT: {filing_datetime}]

Allowed:

• 8-K filing content (the actual results)
• Historical financials (prior 10-K/10-Q via XBRL)
• Prior earnings transcripts
• Pre-filing news
• Consensus estimates from Perplexity (filtered by article date)

NOT Allowed:

• Return data (daily_stock, hourly_stock) — that's what we're predicting
• Post-filing news reactions
• Post-filing analyst commentary

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Data Isolation Architecture

ALL data queries go through the filter agent. This includes Neo4j AND Perplexity.

YOU (earnings-prediction)
        │
        │ /filtered-data --agent {source} --query "[PIT: X] ..."
        ▼
┌─────────────────────────────────────┐
│       FILTER AGENT                  │
│                                     │
│  Sources: neo4j-*, perplexity-search│
│                                     │
│  Validates:                         │
│  1. Forbidden patterns              │
│  2. PIT compliance (dates <= PIT)   │
│                                     │
│  You NEVER see contaminated data    │
└─────────────────────────────────────┘
        │
        │ Clean data only
        ▼
YOU (continue with clean data)

Why This Matters

Return data (daily_stock, hourly_stock, etc.) is what we're trying to predict. Post-filing articles mention stock reactions. If either enters your context, the prediction is contaminated. The filter agent ensures you never see it.

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Workflow (5 Steps)

Use TodoWrite to track progress. Mark each step in_progress before starting, completed immediately after.

IMPORTANT: Read ./QUERIES.md and execute the exact commands shown there. Do not modify them.

Step 1: Get Filing Metadata

Execute the metadata query from QUERIES.md.

Extract: ticker, filing_datetime (this becomes your PIT).

Step 2: Get Actual Results from Filing

Execute the exhibit query from QUERIES.md.

Extract: Actual EPS, actual revenue, any guidance.

Step 3: Get Historical Context (PIT)

Execute these queries from QUERIES.md: xbrl, transcript, news, entity

Step 4: Get Consensus Estimates

Execute the consensus query from QUERIES.md.

Step 5: Make Prediction

Calculate surprise:

Surprise % = ((Actual - Consensus) / |Consensus|) × 100

Reason from the data. Consider: surprise magnitude, guidance direction, historical patterns, sector context. Output prediction with reasoning.

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CSV Output

File: earnings-analysis/predictions.csv

Columns:

accession_no,ticker,filing_datetime,prediction_datetime,predicted_direction,predicted_magnitude,confidence,primary_reason,actual_direction,actual_magnitude,actual_return,correct

Append each prediction as a new row. Leave actual_* columns empty (filled by attribution later).

CSV Append

1. Read existing CSV with Read tool
2. Append new row with Write tool (rewrite full file)
3. Escape commas in primary_reason with quotes: "reason with, comma"

First run: If CSV doesn't exist, create it with header row first.

---

After Prediction (REQUIRED STEPS)

Step 6: Append to CSV

Append prediction to earnings-analysis/predictions.csv (see CSV Output section).

Step 7: Build Thinking Index (MANDATORY - DO NOT SKIP)

This step is REQUIRED. Execute this exact command:

python3 scripts/build-thinking-index.py {accession_no}

Replace {accession_no} with the actual accession number from your analysis (e.g., 0001234567-24-000001).

This extracts thinking from all sessions and sub-agents and saves to Obsidian.

Step 8: Attribution (Later)

Run /earnings-attribution separately to fill actual_* columns and verify prediction.

---

Toggle Filtering

# In .claude/skills/earnings-prediction/
./enable-filter.sh   # PIT filtering ON (default)
./disable-filter.sh  # Direct mode (no filtering)

State persists until you run the other script.

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Session & Subagent History (Shared CSV)

History file: .claude/shared/earnings/subagent-history.csv (shared with earnings-attribution)

Format: See subagent-history.md for full documentation.

accession_no,skill,created_at,primary_session_id,agent_type,agent_id,resumed_from
0001514416-24-000020,prediction,2026-01-13T09:00:00,aaa11111,primary,,
0001514416-24-000020,prediction,2026-01-13T09:01:05,aaa11111,neo4j-entity,abc12345,

On analysis start:

1. Read CSV (create with header if doesn't exist)
2. Append primary row: {accession},prediction,{timestamp},{session_id},primary,,

Before calling a subagent:

1. Query latest agent ID: grep "{accession}" | grep ",{agent_type}," | tail -1 | cut -d',' -f6
2. If agent ID exists → can use resume: <id> in Task call
3. If want fresh session → proceed without resume

After each subagent completes:

1. Extract agentId from Task response
2. Append row: {accession},prediction,{timestamp},{session_id},{agent_type},{agent_id},{resumed_from}

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Version 2.2 | 2026-01-16 | Made thinking index build mandatory (Step 7)