dr-report

name	dr-report
description	Synthesize research findings into the final deliverable. Reads all data files, generates executive summary, key findings, gaps, and recommendations. Use when all phases are complete.

You are synthesizing research findings into the final deliverable.

Accepts optional arguments: /dr-report, /dr-report --provenance.

Steps

1. Load Context

Read .research/PROJECT.md for:

• The original research question / mission
• The decision context (what the user will do with this)
• The desired artifact type (strategic brief, landscape map, scorecard, etc.)

2. Check Completeness

Read .research/ROADMAP.md. For each phase:

• If COMPLETE, proceed
• If IN_PROGRESS or NOT_STARTED, warn the user: "Phase {N} is not complete. The report will have gaps. Continue anyway? Or run /dr-run first."

3. Load All Data

Read every file in data/. For each:

• Parse the entries
• Note total count, completeness rate, CONFLICTING/UNVERIFIED values
• Group by category or phase as appropriate

Merge adversary sidecar files: Also read any data/*.adversary.json sidecar files. Merge adversary verdicts into the data entries by matching task_id and field_name to the provenance envelopes.

• Error handling: If a sidecar file contains malformed JSON, skip it, log "[WARNING] Skipped malformed sidecar: {filename}", and continue processing other files. Do not crash.
• Update adversary_verdict, adversary_evidence, confidence_delta, verified_at, and survival_score in each matching provenance envelope.

4. Generate Report

Write output/final-report.md with this structure:

# {Research Title}

Generated: {ISO date}
Source: deepresearch autonomous research project

## Executive Summary

{2-3 paragraphs directly answering the research question from PROJECT.md.
This is the most important section. Be specific and actionable.
Reference data points and counts.}

## Methodology

- Research approach: {from ARCHITECTURE.md ADRs}
- Total entities researched: {count}
- Data sources: {count unique source URLs}
- Phases completed: {N of M}
- Overall data completeness: {percentage}

## Key Findings

### {Phase 1 Title}

{Findings from Phase 1 data. Include specific numbers, top entries, patterns.
Reference the data files for details.}

### {Phase 2 Title}

{Findings from Phase 2 data.}

{...repeat for each phase...}

**Adversary highlights in findings:** When presenting claims in Key Findings sections:
- Claims with `adversary_verdict: "refuted"` → mark with `[REFUTED]` and strikethrough: `~~claim text~~ [REFUTED]`
- Claims with `adversary_verdict: "weakened"` → mark with `[WEAKENED]` and show adversary counter-evidence inline: `claim text [WEAKENED — adversary found: "counter-evidence quote"]`
- Claims with `adversary_verdict: "confirmed"` → present normally (no special marking)
- Claims with `adversary_verdict: "pending"` or `"unverifiable"` → present normally

At the end of each Key Findings section, add a **Contested Claims** subsection listing all refuted/weakened claims with their adversary evidence:

```markdown
#### Contested Claims

| Claim | Verdict | Adversary Evidence | Survival Score |
|-------|---------|-------------------|----------------|
| Artisan AI raised $25M | REFUTED | "Crunchbase shows $12M total funding" | 0.00 |
| Regie.ai has 80 employees | WEAKENED | "LinkedIn shows ~120 employees" | 0.42 |

If no adversary sidecar files exist, skip all adversary highlights.

Patterns and Insights

{Cross-cutting observations that span multiple phases. What trends emerged? What was surprising? What does the data suggest that wasn't in the original question?}

Gaps and Limitations

• {N} entries marked INCOMPLETE — could not find full data
• {N} entries marked INACCESSIBLE — sources were unavailable
• Fields with high NOT_FOUND rates: {list}
• {N} CONFLICTING values unresolved
• {Any phases not completed}

Recommendations

{Based on the decision context from PROJECT.md. What should the user do with this information? Prioritized, specific, actionable.}

Appendix

Data Sources

{Count of unique URLs. Top domains referenced.}

Completeness by Field

Field	Complete	NOT_FOUND	UNVERIFIED	CONFLICTING
...	...	...	...	...

Research Quality

• Eval pass rate: {from latest eval-history file, if exists}
• Researcher iterations: {count of versions in eval-history}

### 5. Adapt to Artifact Type

Based on the done condition from PROJECT.md:
- **Strategic brief:** Focus on executive summary and recommendations
- **Landscape map:** Include a category breakdown table with key metrics per entity
- **Opportunity scorecard:** Add a scoring matrix with weighted criteria
- **Spreadsheet:** Additionally generate `output/data-export.json` with all entries merged and normalized

### 6. Provenance Export (--provenance flag)

If the user passed `--provenance`:

Write `output/provenance-chain.json` alongside the markdown report. This is the full epistemic audit trail.

Structure:
```json
{
  "generated_at": "2026-04-10T14:30:00Z",
  "trust_score": 0.74,
  "survival_rate": 0.87,
  "claims": {
    "P1.01:funding_total": {
      "claim_text": "Artisan AI raised $12M",
      "source_url": "https://crunchbase.com/organization/artisan-ai",
      "extraction_method": "direct_quote",
      "cross_ref_count": 2,
      "cross_ref_urls": ["https://techcrunch.com/artisan-ai-funding"],
      "confidence_score": 0.9,
      "volatility_class": "medium",
      "adversary_verdict": "confirmed",
      "adversary_evidence": null,
      "verified_at": "2026-04-10T14:32:00Z",
      "survival_score": 0.9
    }
  }
}

• Key format: {task_id}:{field_name} for easy lookup
• Flatten all provenance envelopes from all data files + sidecar verdicts into one exportable artifact
• trust_score: mean survival_score across all adversary-reviewed claims
• survival_rate: (confirmed + weakened) / (confirmed + weakened + refuted)
• If no adversary data exists, set trust_score and survival_rate to null

7. Print Summary

After writing the report:

Report written to output/final-report.md

{word count} words, {section count} sections
{entity count} entities across {category count} categories
{source count} unique sources referenced
Trust: {survival_rate}% survived | Score: {trust_score} (or "N/A" if no adversary data)

The report directly answers: "{original research question}"

If --provenance was passed, also print: Provenance chain written to output/provenance-chain.json ({claim count} claims)

Error Handling

• If no data files exist, tell the user to run /dr-run first.
• If PROJECT.md is missing, tell the user to run /dr-new first.
• If data is sparse (<10 entries total), warn that findings may not be representative.