openplanter

OpenPlanter — Investigation Methodology Skill

Epistemic framework for cross-dataset investigation, entity resolution, and evidence-backed analysis. Extracted from the OpenPlanter recursive investigation agent and enriched with professional OSINT tradecraft (Admiralty System, ACH, FollowTheMoney schema, intelligence cycle methodology).

Claude Code already has the tools. This skill provides the methodology.

When to Use

• Cross-referencing heterogeneous datasets (corporate registries, campaign finance, lobbying, property records, contracts)
• Entity resolution across datasets with inconsistent naming
• Building evidence chains with provenance and confidence tiers
• Structured OSINT investigations requiring epistemic discipline
• Any analysis where claims need to trace to cited source records

Quick Start

# 1. Initialize workspace
python3 ~/.claude/skills/openplanter/scripts/init_workspace.py /path/to/investigation

# 2. Drop datasets into datasets/
cp campaign_finance.csv lobbying.json corporate_registry.csv /path/to/investigation/datasets/

# 3. Write an investigation plan
# → plans/plan.md (see references/output-templates.md for plan template)

# 4. Resolve entities across datasets
python3 ~/.claude/skills/openplanter/scripts/entity_resolver.py /path/to/investigation

# 5. Cross-reference linked entities
python3 ~/.claude/skills/openplanter/scripts/cross_reference.py /path/to/investigation

# 6. Validate evidence chains
python3 ~/.claude/skills/openplanter/scripts/evidence_chain.py /path/to/investigation

# 7. Score confidence
python3 ~/.claude/skills/openplanter/scripts/confidence_scorer.py /path/to/investigation

Investigation Methodology

Epistemic Discipline

Assume nothing about the environment until confirmed firsthand. These principles prevent the most common investigation failures:

1. Ground truth comes from files, not memory. Read actual data before modifying it, and read actual error messages before diagnosing. Model memory of data structure is unreliable—reading the file takes seconds, recovering from a wrong assumption takes minutes.
2. Empty output is ambiguous. If a command returns empty, cross-check with ls -la and wc -c before concluding a file is actually empty, because output capture mechanisms can silently lose data.
3. Success does not mean correctness. A command that "succeeds" may have done nothing. Check actual outcomes, not just exit codes. After downloading, verify with ls and wc -c. After extraction, verify expected files exist.
4. Verify round-trip correctness. After any data transformation (parsing, linking, aggregation), check the result from the consumer's perspective—load the output, spot-check records, verify row counts. Transformations that silently drop records are the most common source of wrong conclusions.
5. Three failures = wrong approach. If a command fails 3 times, change strategy entirely. Repeating an identical command expecting different results wastes context window.
6. Produce artifacts early. Write a working first draft of findings as soon as the requirements are clear, then iterate. An imperfect deliverable beats a perfect analysis with no output. If 3+ steps have passed without writing any output, stop and write—even if incomplete.