Reproducible Analysis

Reproducible Analysis

Maintain a machine-readable journal of every data operation so that humans, agents, and machines can independently verify the analysis end-to-end.

Core Principle

Every analysis should be independently reproducible: given the same input files, a third party should be able to replay the exact sequence of operations and arrive at bit-identical results for all deterministic steps.

Journal Format

Create a journal file named <analysis-name>.journal.jsonl alongside the analysis output. Each line is a JSON object representing one operation.

Entry Schema

{"seq": 1, "ts": "2026-03-19T14:30:00Z", "op": "index", "tool": "mcp__qsv__qsv_index", "input": "sales.csv", "input_sha256": "a1b2c3...", "input_rows": 50000, "input_cols": 12, "params": {}, "output": "sales.csv.idx", "output_sha256": "d4e5f6...", "duration_ms": 45, "note": "Create index for fast access"}
{"seq": 2, "ts": "2026-03-19T14:30:01Z", "op": "stats", "tool": "mcp__qsv__qsv_stats", "input": "sales.csv", "input_sha256": "a1b2c3...", "params": {"cardinality": true, "stats_jsonl": true}, "output": "sales.stats.csv", "output_sha256": "f7a8b9...", "duration_ms": 320, "note": "Generate stats cache with cardinality"}

Required Fields

Optional Fields

How to Compute Hashes

Use mcp__qsv__qsv_sqlp or shell commands to compute SHA-256 hashes:

# Via shell (when available)
shasum -a 256 sales.csv | cut -d' ' -f1

# Via qsv sqlp (for CSV content hash)
# Hash the output file after each step

Alternatively, note the file size and row count as a lighter-weight fingerprint when hashing is impractical:

{"seq": 1, "input": "huge_file.csv", "input_fingerprint": {"rows": 5000000, "cols": 42, "bytes": 1073741824}, "note": "additional fields omitted for brevity"}

Journal Lifecycle

Starting a Journal

At the beginning of any analysis, create the journal and record the environment:

{"seq": 0, "ts": "2026-03-19T14:29:55Z", "op": "init", "tool": null, "input": null, "params": {"working_dir": "/path/to/data", "qsv_version": "0.142.0 (polars-0.46.0)", "platform": "darwin-aarch64"}, "output": "analysis.journal.jsonl", "note": "Initialize reproducibility journal"}

During Analysis

Log every data operation. For each step:

1. Record the entry after the operation completes (so you have the output hash and duration)
2. Include the note field explaining the analytical reasoning — this is what makes the journal useful to human reviewers
3. Mark deterministic: false for any AI-generated step (describegpt, chart selection, narrative)

Closing a Journal

At the end, write a summary entry:

{"seq": 99, "ts": "2026-03-19T15:10:00Z", "op": "complete", "tool": null, "input": "sales.csv", "input_sha256": "a1b2c3...", "params": {"total_steps": 98, "deterministic_steps": 95, "ai_steps": 3, "final_output": "analysis_report.md"}, "output": "analysis.journal.jsonl", "note": "Analysis complete. 95 of 98 steps are deterministic and independently reproducible."}

Verification Protocol

For Humans

1. Open the .journal.jsonl file
2. Review each note field to understand the analytical reasoning
3. Check that the sequence of operations makes logical sense
4. Verify input_sha256 of the first entry matches your copy of the source data
5. Spot-check any step by re-running the tool with the recorded params

For Agents

1. Parse the .journal.jsonl file
2. Verify the seq 0 entry to confirm environment compatibility (qsv version, platform)
3. For each entry where deterministic is true (or absent): a. Compute sha256 of the input file — must match input_sha256 b. Execute the tool with the recorded params c. Compute sha256 of the output — must match output_sha256 d. If mismatch, flag the step and stop
4. For entries where deterministic: false, skip hash verification but log that the step was AI-generated
5. Report: N of M deterministic steps verified, K AI-generated steps skipped

For Machines (CI/CD)

#!/bin/bash
# replay-journal.sh — replay and verify a journal
JOURNAL="$1"
FAILURES=0

jq -c 'select(.seq > 0 and .op != "complete" and (.deterministic // true))' "$JOURNAL" | while read -r entry; do
  SEQ=$(echo "$entry" | jq -r '.seq')
  INPUT=$(echo "$entry" | jq -r '.input')
  EXPECTED=$(echo "$entry" | jq -r '.output_sha256')

  # Verify input hash
  ACTUAL_INPUT_HASH=$(shasum -a 256 "$INPUT" | cut -d' ' -f1)
  INPUT_HASH=$(echo "$entry" | jq -r '.input_sha256')
  if [ "$ACTUAL_INPUT_HASH" != "$INPUT_HASH" ]; then
    echo "FAIL step $SEQ: input hash mismatch"
    FAILURES=$((FAILURES + 1))
    continue
  fi

  # Re-execute and verify output hash (tool-specific replay logic here)
  # ...

  echo "PASS step $SEQ"
done

echo "$FAILURES failures"
exit $FAILURES

Integration with Commands

When any /data-* command is invoked and the user requests reproducibility (or the output is a formal deliverable), maintain a journal:

Integration with GenAI Disclaimer

The journal complements the genai-disclaimer skill:

• The journal records what was done and enables replay
• The disclaimer communicates which parts are AI-generated vs. deterministic
• Together, they provide full transparency for stakeholders

Use the journal's deterministic and ai_generated flags to auto-generate the disclaimer's attribution table.

Best Practices

• Always hash inputs: The input hash is the anchor for reproducibility — without it, verification is impossible
• Log failures too: If a step fails and you retry with different parameters, log both attempts (the failure with an error field, then the successful retry)
• Include row count deltas: delta_rows makes it easy to spot where data was filtered, joined, or deduplicated
• Use relative paths: All file paths should be relative to the working directory so the journal is portable
• Version pin: Record qsv_version in the init entry — different versions may produce different stats precision
• One journal per analysis: Don't append unrelated analyses to the same journal file
• Commit journals to version control: They're small (a few KB) and provide audit trail

Source: dathere/qsv — distributed by TomeVault.