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Mode: deep — Deep Company Research

Structured company research that feeds directly into evaluations and interview prep.

Step 0 — Check for existing evaluation

Check reports/ for an existing evaluation of this company+role before researching. If found, load the report — deep research MUST build on what the evaluation already identified (archetype, gaps, proof points), not start from scratch.

Scan These 6 Research Axes

Scan Axis 1: AI Strategy

  • What products/features use AI/ML?
  • What's their AI stack? (models, infra, tools)
  • Engineering blog — what do they publish?
  • Papers or talks on AI?

Scan Axis 2: Recent Moves (last 6 months)

  • Relevant hires in AI/ML/product?
  • Acquisitions or partnerships?
  • Product launches or pivots?
  • Funding rounds or leadership changes?

Scan Axis 3: Engineering Culture

  • How do they ship? (deploy cadence, CI/CD)
  • Mono-repo or multi-repo?
  • Languages/frameworks?
  • Remote-first or office-first?
  • Glassdoor/Blind reviews on eng culture?

Scan Axis 4: Likely Challenges

  • Scaling problems?
  • Reliability, cost, latency challenges?
  • Migrating anything? (infra, models, platforms)
  • Pain points from reviews?

Scan Axis 5: Competitors And Differentiation

  • Main competitors?
  • Moat/differentiator?
  • Positioning vs competition?

Scan Axis 6: Candidate Angle

cv.md is already in context via opencode.json:instructions — do NOT Read it again. Read profile.yml once if you need identity fields:

  • What unique value does the candidate bring to this team?
  • Which projects are most relevant?
  • What story should they tell in the interview?

Feed Research Back Into Evaluation

The feedback loop is the key step that makes deep research actionable.

After completing the 6 axes, update the evaluation if one exists:

  1. Score adjustments: Deep research may reveal information that changes scoring dimensions.

    • Company reputation (axis 3: culture) → dimension #7.
    • Tech stack modernity (axis 1: AI strategy) → dimension #8.
    • Speed to offer (axis 2: recent moves, hiring pace) → dimension #9.
    • Cultural signals (axis 3: culture) → dimension #10.
    • Growth trajectory (axis 2: funding, launches) → dimension #5.

  2. Append a ## Deep Research section to the existing report with findings organized by axis. Include date of research so it can be refreshed.

  1. Update Block F (Interview Plan): The "likely challenges" (axis 4) directly inform STAR story selection — pick stories that address the company's actual problems, not generic ones.

  2. Update contact targeting: The "recent moves" (axis 2) often reveal who to reach out to (new hires, team leads mentioned in blog posts).

If NO evaluation exists yet, save the research to reports/deep-{company-slug}-{date}.md so it's available when the user evaluates later. The evaluation mode MUST check for existing deep research before starting Block D (Comp & Demand).

Output Format

## Deep Research: [Company] — [Role]
**Date:** YYYY-MM-DD
**Linked report:** #NNN (if exists)

### 1. AI Strategy
(findings)

### 2. Recent Moves
(findings)

### 3. Engineering Culture
(findings)

### 4. Likely Challenges
(findings)

### 5. Competitors & Differentiation
(findings)

### 6. Candidate Angle
(findings)

### Score Impact
| Dimension | Before | After | Why |
|-----------|--------|-------|-----|
(only dimensions where research changed the score)