| 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".
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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 profiledata/raw/fund_candidates.json — candidate firms with rationaledata/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.
