Fecfile

FEC Filing Analysis

This skill enables analysis of Federal Election Commission campaign finance filings.

Requirements

• uv must be installed
• Python 3.9+

Dependencies are automatically installed when running the script with uv run.

First-Time Check

The first time this skill is invoked in a session, verify that uv is installed by running:

uv --version

If this command fails or uv is not found, do not proceed. Instead, inform the user that uv is required but not installed, and direct them to the installation guide: https://docs.astral.sh/uv/getting-started/installation/

Quick Start

Always start by checking the filing size:

uv run scripts/fetch_filing.py <FILING_ID> --summary-only

Based on the summary, decide how to proceed—see Handling Large Filings below for filtering and streaming strategies. Small filings can be fetched directly; large filings require pre-filtering or streaming.

Fetching data:

uv run scripts/fetch_filing.py <FILING_ID>                   # Full filing (small filings only)
uv run scripts/fetch_filing.py <FILING_ID> --schedule A      # Only contributions
uv run scripts/fetch_filing.py <FILING_ID> --schedule B      # Only disbursements
uv run scripts/fetch_filing.py <FILING_ID> --schedules A,B   # Multiple schedules

The fecfile library is installed automatically by uv.

Field Name Policy

IMPORTANT: Do not guess at field names. Before referencing any field names in responses:

1. For form-level fields (summary data, cash flow, totals): Read references/FORMS.md
2. For itemization fields (contributors, payees, expenditures): Read references/SCHEDULES.md

These files contain the authoritative field mappings. If a field name isn't documented there, verify it exists in the actual JSON output before using it.

Handling Large Filings

FEC filings vary enormously in size. Small filings (like state party monthly reports) may have only a few dozen itemizations and can be used directly. However, major committees like ActBlue, WinRed, and presidential campaigns can have hundreds of thousands of itemizations in a single filing. Do not dump large filing data directly into the context window.

Checking Size

Before pulling full schedules, use --summary-only to assess the filing:

uv run scripts/fetch_filing.py <ID> --summary-only

The summary includes financial totals that help gauge filing size without parsing itemizations:

These are dollar totals, not item counts, but combined with the committee name they help you decide:

• Small state/local party with modest totals: Probably safe to pull full schedules
• ActBlue, WinRed, or presidential campaign with millions in totals: Use streaming or post-filter

If you need to verify exact counts before processing, stream with an early cutoff:

uv run scripts/fetch_filing.py <ID> --stream --schedule A | python3 -c "
import sys
count = 0
limit = 256
for line in sys.stdin:
    count += 1
    if count >= limit:
        print(f'Schedule A: {limit}+ items (stopped counting)')
        sys.exit(0)
print(f'Schedule A: {count} items')
"

If itemization counts are in the hundreds or more, you must post-filter before presenting results. Even smaller filings may benefit from post-filtering to aggregate or focus the output.

Pre-Filtering at Parse Time

Use CLI flags to filter before data is loaded into memory:

Schedules you don't request are never parsed.

Post-Filtering with Pandas

Use Python/pandas to aggregate, filter, and limit results:

cat > /tmp/analysis.py << 'EOF'
# /// script
# requires-python = ">=3.9"
# dependencies = ["pandas>=2.3.0"]
# ///
import json, sys
import pandas as pd

data = json.load(sys.stdin)
df = pd.DataFrame(data.get('itemizations', {}).get('Schedule A', []))
# Aggregate and limit output
print(df.groupby('contributor_state')['contribution_amount'].agg(['count', 'sum']).sort_values('sum', ascending=False).to_string())
EOF

uv run scripts/fetch_filing.py <ID> --schedule A 2>&1 | uv run /tmp/analysis.py

Streaming Mode (Producer/Consumer Model)

For truly massive filings where even a single schedule is too large to hold in memory, use --stream to output JSONL (one JSON object per line):

uv run scripts/fetch_filing.py <ID> --stream --schedule A

Each line has the format: {"data_type": "...", "data": {...}}

How streaming works:

The producer (fetch_filing.py) outputs one record at a time without loading the full filing. A consumer script reads one line at a time and aggregates incrementally. Neither side ever holds all records in memory.

Example streaming aggregation:

uv run scripts/fetch_filing.py <ID> --stream --schedule A | python3 -c "
import json, sys
from collections import defaultdict
totals = defaultdict(float)
counts = defaultdict(int)
for line in sys.stdin:
    rec = json.loads(line)
    if rec['data_type'] == 'itemization':
        state = rec['data'].get('contributor_state', 'Unknown')
        amt = float(rec['data'].get('contribution_amount', 0))
        totals[state] += amt
        counts[state] += 1
for state in sorted(totals, key=lambda s: -totals[s]):
    print(f'{state}: {counts[state]} contributions, \${totals[state]:,.2f}')
"

This processes hundreds of thousands of records using constant memory.

Guidelines

1. Small filings - Can be used directly without filtering
2. Large filings - Pre-filter with --summary-only or --schedule X, then check size
3. Massive results - Post-filter with pandas to aggregate, filter, and limit output
4. Streaming mode - Use --stream with inline Python consumers for constant-memory processing
5. Limit output - Use .head(), .nlargest(), .nsmallest() to cap results

Finding Filing IDs

Filing IDs can be found via:

1. FEC Website: Visit fec.gov and search for a committee
2. Direct URLs: Filing IDs appear in URLs like https://docquery.fec.gov/dcdev/posted/1690664.fec
3. FEC API: Use the FEC API to search for filings

Response Style

When analyzing FEC filings:

• Start with your best judgment about whether this filing has unusual aspects (no activity is not unusual)
• Write in a simple, direct style
• Group related information together in coherent sections
• Avoid excessive formatting or bold text

Form Types

See FORMS.md for detailed guidance on:

• F1/F1A: Committee registration/organization
• F2/F2A: Candidate declarations
• F3/F3P/F3X: Financial reports
• F99: Miscellaneous text filings

Schedules & Field Mappings

See SCHEDULES.md for detailed field mappings for:

• Schedule A: Individual contributions
• Schedule B: Disbursements/expenditures
• Schedule C: Loans
• Schedule D: Debts
• Schedule E: Independent expenditures

Amendment Detection

Check the amendment_indicator field:

• A = Standard Amendment
• T = Termination Amendment
• Empty/None = Original Filing

If it's an amendment, look for previous_report_amendment_indicator for the original filing ID.

Coverage Periods

Use coverage_from_date and coverage_through_date fields.

• Format: Usually YYYY-MM-DD
• Calculate days covered: (end_date - start_date) + 1
• Context: Quarterly reports ~90 days, Monthly ~30 days, Pre-election varies

Financial Summary Fields

For financial filings (F3, F3P, F3X):

• Receipts: col_a_total_receipts
• Disbursements: col_a_total_disbursements
• Cash on Hand: col_a_cash_on_hand_close_of_period
• Debts: col_a_debts_to and col_a_debts_by

Data Quality Notes

• Contributions/expenditures $200+ must be itemized with details
• Smaller amounts may appear in summary totals but not itemized
• FEC Committee ID format is usually C########

Example Queries

Once you have filing data, you can answer questions like:

• "What are the total receipts and disbursements?"
• "Who are the top 10 contributors?"
• "What are the largest expenditures?"
• "What contributions came from California?"
• "How much was spent on advertising?"