| name | evidence-score |
| version | 1.0.0 |
| description | Ranks a prospect list or account list by evidence-backed outbound readiness. Scores
each record 0-100 across five dimensions — fit (ICP match), timing (recent
triggers), influence (decision-maker proximity), engagement (public signal on the
offer's topic), and reachability (contact data confidence). Produces a ranked list
plus one-line justifications tied to specific tool calls. Use when the user has a
large output from build-prospect-list / account-research and wants to prioritize
who to contact first. Proactively invoke when a list is > 50 leads and the user is
about to start outreach.
|
| benefits-from | ["richapi-gtm","build-prospect-list","account-research","enrich-and-verify","pre-meeting-briefing","sequence-builder"] |
| allowed-tools | ["Bash","Read","Write","Edit","AskUserQuestion"] |
| triggers | ["score these leads","rank by signal","who should I contact first","hottest leads","prioritize this list","evidence-based ranking"] |
evidence-score
A scoring engine. Takes whatever upstream produced — a prospect list, an account map, an enriched CSV — and ranks it by evidence-backed outbound readiness. Every score point is justifiable with a pointer to the tool call that produced it.
Preamble
~/.claude/skills/richapi-gtm-skills/bin/richapi-skills-preflight
Phase 0 — what the user is scoring
Ask once:
- Records to score: prospects (people) or accounts (companies)?
- The offer: one sentence. The score weights depend on what "fit" means for this offer.
- Target volume: "give me the top 25" or "rank everything"? Affects what to surface.
If the upstream data is missing the signals evidence-score needs (e.g. no profile activity pulled), say so upfront and offer to enrich first.
Phase 1 — the five dimensions
Each record gets five sub-scores, 0-20 each, summing to 0-100.
| Dimension | What it measures | Signals consumed |
|---|
| Fit (0-20) | Does this record match the stated ICP? | seniority, function, industry, companySize, geography from enrichment |
| Timing (0-20) | Is there a recent trigger that makes now a better moment than last month? | recentlyChangedJobs, recent posts via profile_activities, funding news, hiring surge, tech-stack additions |
| Influence (0-20) | Can this person actually buy or block? | Title + seniority + function; for accounts, the ratio of present decision-makers |
| Engagement (0-20) | Have they publicly engaged with the offer's topic? | post_keyword_search hits, profile_activities comments on related posts, content they've posted themselves |
| Reachability (0-20) | Do we have the data to actually contact them? | Email status (valid > risky > unknown > not-found), phone, LinkedIn URL present |
Total = Fit + Timing + Influence + Engagement + Reachability, capped at 100.
Phase 2 — per-dimension scoring rules
Fit (0-20)
| Signal | Points |
|---|
| Seniority exactly matches target band | 6 |
| Seniority adjacent to target | 3 |
| Function matches target | 5 |
| Industry matches target | 4 |
| Company size in target band | 3 |
| Geography match | 2 |
Cap at 20. Missing signals → 0 for that sub-component (not penalized).
Timing (0-20)
| Signal | Points | How to detect |
|---|
| Job change in last 90 days | 10 | enrich_profile.currentPositions[0].startedOn or recentlyChangedJobs flag |
| Company hiring surge (5+ open roles in target function) | 6 | Manual or via web_scrape on careers page (user tells us) |
| Funding event in last 180 days | 6 | From enrich_company headlines / post_keyword_search |
| Exec post referencing a pain word in the offer's domain | 5 | profile_activities + keyword match |
| Company posted on a relevant topic in last 30 days | 3 | post_keyword_search scoped to fromCompany |
Influence (0-20)
| Title/seniority band | Points |
|---|
| CXO / Founder / Owner | 20 |
| VP / SVP | 16 |
| Director | 12 |
| Senior IC | 8 |
| IC / Junior | 4 |
Adjustment: +4 if function exactly matches where the offer is used (e.g. selling a sales tool → +4 for sales titles vs other VPs).
Engagement (0-20)
| Signal | Points |
|---|
| Authored a post on the offer's topic in last 60 days | 10 |
| Commented on a related post | 6 |
| Reacted to a related post | 2 |
| Follows competitor / category account | 2 |
Cap at 20.
Reachability (0-20)
| Signal | Points |
|---|
Valid work email confirmed (email_verifier = valid) | 12 |
| Risky / catch-all email | 6 |
| Personal email only | 4 |
| No email | 0 |
| Phone number present (normalized) | 4 |
| LinkedIn URL present (enables multi-channel) | 4 |
Phase 3 — enrichment gap analysis
If the list doesn't have the data for full scoring, call it out. Offer to enrich the minimum required fields first:
| Missing signal | Fill with | Cost per record |
|---|
| Email status | verify_emails batch | 0.5 |
| Current position + seniority | enrich_profiles_bulk | 1 |
| Recent activity (posts / comments) | profile_activities | 2 |
| Company hiring / posts | enrich_company + post_keyword_search | 1 + 6 (per company, not per person) |
A full-fidelity score for a 100-lead list typically costs 150-300 credits of enrichment on top of whatever produced the list. Worth it for high-ACV segments; skip for mass-market lists.
Phase 4 — score the records
Process the list. For each record:
- Collect the signals. If a dimension has zero signals available, set that dimension to 0 (don't guess).
- Compute the five sub-scores.
- Sum to total.
- Record, per dimension, the single most load-bearing signal that drove that sub-score — this is the "evidence" the user sees.
Phase 5 — output
For ≤ 25 records: full inline table
Rank Score Name Company Why
1 91 Jane Smith Acme Corp Director; job change 45d ago; posted about our exact pain topic last week
2 87 Alex Chen Beta Industries VP Eng; company hired 8 engs in 30d; valid work email
3 84 Maria Kim Gamma SaaS CXO; commented on related post; HQ in target geo
...
For > 25 records: top 25 inline + CSV
Write the full scored list to ./evidence-scored-<YYYYMMDD>.csv with columns:
rank, score, fit, timing, influence, engagement, reachability,
firstName, lastName, company, title, linkedinUrl, email, email_status,
why_fit, why_timing, why_influence, why_engagement, why_reachability
The why_* columns are one-line evidence pointers. This is what makes the score auditable — the user can see why #3 beat #4.
Segmentation bands
| Band | Range | Recommended action |
|---|
| Priority 1 — Hot | 80-100 | Contact this week. Multi-channel (email + LinkedIn). Custom opener. |
| Priority 2 — Warm | 60-79 | Next sprint. Default sequence. |
| Priority 3 — Watch | 40-59 | Add to a nurture / content drip. Don't hard-sell. |
| Below threshold | < 40 | Drop or revisit next quarter. |
Report band counts at the top so the user knows where to focus:
Scored 247 records
Priority 1 (80+): 19 records
Priority 2 (60-79): 88 records
Priority 3 (40-59): 104 records
Below threshold: 36 records
Phase 6 — handoff
Once scored, the user almost always needs one of:
Always offer the first two — they're the natural next actions.
Anti-patterns
- Scoring a list you haven't enriched. You'll get 0s on three dimensions and the ranking is useless.
- Hiding the
why_* evidence columns. Users should be able to challenge a score; that requires seeing the signal.
- Double-counting: if
recentlyChangedJobs gave Timing points, don't also award it to Influence.
- Scoring LinkedIn Sales Nav outputs with a one-size ICP. Weight dimensions to the offer — e.g. Influence matters more when selling to exec buyers than when selling to end-user ICs.
- Acting on score without a reachability floor — the #1 scored lead with
email_status: not_found is unactionable. Filter for reachability > 8 before surfacing as "Priority 1".
- Re-scoring without new enrichment. Scores are a function of signals; if signals didn't change, the score doesn't change — save the compute.
Customizing the weights
The default weights assume a typical B2B SaaS offer. Users selling into specific segments can override:
- High-ACV enterprise → Influence × 1.5, Fit × 1.2. Reachability matters less (AEs can dig).
- Velocity SMB outbound → Reachability × 1.5, Timing × 1.2. Influence matters less.
- Developer tool to ICs → Influence × 0.5 (ICs buy themselves), Engagement × 1.5.
- Trigger-driven outbound (e.g. just-raised startups) → Timing × 2.0.
If the user hasn't said, ask once: "Any weight adjustments, or use the default weighting?"
Cost
This skill reads upstream data; it doesn't call paid tools itself. If data gaps require enrichment to score accurately, this skill surfaces the gaps but hands off to enrich-and-verify for the actual spending.
