score-vc-fit

name	score-vc-fit
description	Score and rank VC firms and target investors based on fit with the startup's fundraising brief. Uses a weighted 8-dimension rubric. Produces a prioritized outreach list with clear reasoning. Triggers on: "score vcs", "rank investors", "evaluate fit", "step 5", "next step", "continue to score", "prioritize investors".

Score VC Fit

Pipeline: clarify-thesis → find-vcs → map-investors → enrich-vc-contacts → score-vc-fit → draft-investor-outreach

You are step 5 of 6. Your job is to turn the candidate list into a prioritized outreach list using a structured scoring rubric.

Tools

None. This step is reasoning only — scoring based on data already collected in previous steps.

Input

Read all three files:

• data/normalized/company_profile.json — the startup's fundraising profile
• data/raw/fund_candidates.json — candidate firms with rationale
• data/normalized/investor_contacts.json — enriched contacts

If any file is missing, stop and tell the founder which step to run first.

Scoring rubric

Score each fund out of 100 using 8 weighted dimensions:

Dimension	Max points	What to assess
Sector fit	25	Does the fund actively invest in this exact sector/subsector?
Stage fit	20	Does the fund's sweet spot match the raise stage?
Geography fit	10	Is the fund active in the startup's HQ geography or customer geography?
Check size fit	10	Does the fund's typical check size match the target?
Lead behavior fit	10	Does the fund's lead/follow style match the founder's preference?
Portfolio adjacency	10	Has the fund invested in comparable companies or adjacent spaces?
Recency	10	Has the fund made investments in the last 24 months (not just deploying old fund)?
Partner relevance	5	Is the identified contact person well-matched to this deal?

Score total = sum of non-null sub-scores, rescaled to 100: score_total = (sum_of_non_null / score_max_possible) * 100

Missing evidence handling:

• If evidence for a dimension is unavailable, score it null — do not score it 0
• score_max_possible = sum of max points for all non-null dimensions
• Note missing dimensions in score_notes
• If more than 3 dimensions are null, flag the record as low-confidence

Score interpretation:

• 90–100: excellent fit — prioritize first
• 75–89: strong fit — high priority
• 60–74: possible fit — worth pursuing if list is short
• below 60: weak fit — deprioritize or remove

Scoring rules:

• Be conservative — do not over-score weak evidence
• Do not reward prestige; reward actual fit evidence
• Penalize clear mismatches even if other dimensions are strong
• Distinguish "famous fund" from "right fund"

Output

Write an array to data/normalized/scored_funds.json conforming to schemas/fund_match.schema.json.

Sort by score_total descending. Join investor_contact objects into each fund record using fund_id.

Review checkpoint

After writing data/normalized/scored_funds.json, present the ranked list to the founder:

Ranked [N] VC firms for [company_name]:

| Rank | Fund | Score | Top contact | Sector | Stage | Concerns |
|------|------|-------|------------|--------|-------|---------|
| 1 | ... | 87/100 | ... | ✓ | ✓ | none |
| 2 | ... | 74/100 | ... | ✓ | ~ | check size unclear |
...

Score breakdown for top 5:
  [Fund name] — [score]/[max_possible]: sector [N], stage [N], geography [N], ...
  Score notes: [score_notes]

[If any funds scored below 60]:
  ⚠ [N] firms scored below 60 and will be deprioritized. Remove them entirely? (y/n)

[If many null dimensions]:
  Note: [N] firms had incomplete evidence — scores may shift with more research.

Does this ranking look right? You can:
  - Confirm to proceed to step 6 (drafting outreach messages)
  - Move a firm up/down: "prioritize [fund]" or "deprioritize [fund]"
  - Remove a firm: "remove [fund]"
  - Ask for more detail on a score: "explain score for [fund]"

Do not proceed to draft-investor-outreach until the founder confirms or adjusts the ranking.