Stage: Research and qualification

Score Accounts

Use this skill to rank accounts by the strength of their signals so you can focus outreach on the highest-intent targets first.

Works with signal data from any source — Saber subscriptions, Apollo exports, HubSpot properties, or manually researched signals. The scoring algorithm is the same regardless of where the data comes from.

Step 1 — Get the signal data

Ask the user how their signal data is available:

Path A — Saber CLI

Run saber --help to confirm the CLI is installed.

Find the account list and active subscriptions:

saber list company list
saber subscription list

Fetch results for each relevant subscription:

saber subscription get <subscriptionId>

If no subscriptions have run yet, offer to activate signals first using create-company-signals, then return here.

Path B — Any other source

Ask the user to provide signal results. Accepted formats:

• Paste a table or CSV with columns: Company, Domain, [Signal 1], [Signal 2], ...
• Describe what they know for each account (e.g. "Acme raised a round, Beta is hiring SDRs")
• Share a HubSpot export or Apollo enrichment output

Confirm which signals are present and what a positive result looks like for each.

Step 2 — Apply the scoring model

If signal metadata is available (from generate-signals)

Use the weighted algorithm:

1. For each signal, determine if the answer is "positive" per interpretation rules
2. earnedWeight = sum of weights for positive-answer signals
3. totalWeight  = sum of all signal weights
4. rawScore     = (earnedWeight / totalWeight) × 100

5. Apply penalties:
   — Any disqualifier fires wrong  → score = 0 immediately
   — Zero buying_signal hits       → score = 0 (no pain evidence)
   — Zero urgency hits             → score − 15 (right fit, wrong time)

6. Final score: 0–100

Thresholds:

• 70+ — High fit, prioritise outreach
• 50–69 — Moderate fit, worth pursuing
• 30–49 — Low fit, monitor for triggers
• < 30 — Poor fit, deprioritise

If no signal metadata is available

Ask the user if any signals are more important than others. Then apply a simple model:

• Base: +1 per positive signal
• Priority signals: 2× weight for signals the user flags as most important
• Recency bonus: +0.5 if the signal is from the last 7 days
• Normalise to 0–10 relative to the highest scorer

Step 3 — Present ranked results

## Account Scores — [List Name]

### High priority (70+ or top tier)
| Rank | Company | Domain | Score | Top signals |
|------|---------|--------|-------|-------------|
| 1 | Acme Corp | acme.com | 84 | New VP Sales hired, hiring SDRs |
| 2 | Beta Inc | beta.io | 76 | Series B 3 months ago, HubSpot migration |

### Watch list (moderate fit)
| Rank | Company | Domain | Score | Top signal |
|------|---------|--------|-------|------------|
| ...  | | | | |

### Deprioritise (low fit)
| Rank | Company | Domain | Score | Notes |
|------|---------|--------|-------|-------|
| ...  | | | | |

Step 4 — Suggest next steps

• High-priority accounts: use write-outreach to draft personalised messages referencing the top signal
• Watch list: re-run signals in 2–4 weeks to watch for urgency triggers
• Low-scoring accounts: pause or remove from active list; revisit if a trigger fires
• Use deal-coaching on any high-priority account that's already in the pipeline