qualify-inbound

name	qualify-inbound
description	Qualify an inbound lead by running signals against their company domain and reading native fit + urgency scores. Falls back to manual scoring when the Saber CLI or a scoring profile isn't available.

Qualify Inbound

Use this skill to qualify an inbound lead fast — run the signal questions against their company, then read calibrated fit and urgency scores.

For scoring concepts, see _shared/scoring.md.

Saber CLI check

Run saber --help. With the CLI you get native scoring — without it, the skill still works using a manual qualification fallback (Path B).

Step 1 — Get the lead

Ask for the inbound company's domain (e.g. acme.com). If only a name is provided, ask for the domain or attempt to infer it.

Step 2 — Choose the path

saber scoring profile list
saber scoring assignment list --type company --object <domain>

• At least one company scoring profile assigned to this domain → Path A (read native scores).
• A profile exists but isn't assigned to this domain → Path A, with one extra step: assign first.
• No company scoring profile at all → Path A is unavailable. Either route to configure-scoring (best one-time investment) or take Path B.
• No CLI → Path B.

---

Path A — Native scoring

A1. Check for fresh signal data

Before running new signals (which costs credits), check for recent results on this domain:

saber subscription list

If subscriptions covering the relevant signals have already run for the domain's list, scores already exist and you can skip straight to A3.

A2. Run signals if needed

Show the credit balance:

saber credits

Tell the user: "Running [N] signals against [domain] will use [N] credits." Confirm, then run:

saber signal --domain <domain> --question "<signal question>" --answer-type boolean --no-wait
saber signal get <signalId>

Auto-trigger will update assigned scores as each signal completes.

A3. Assign profile if needed

If the lead's domain isn't already assigned to the profile:

saber scoring assignment create --profile <profileId> --type company --object <domain>

Assignment triggers immediate compute.

A4. Read scores

saber scoring scores --type company --object <domain> --detailed

You get one row per dimension (fit, urgency) with the contributing rules.

A5. Map to inbound tiering

Fit	Urgency	Tier	Recommended action
≥ 70	≥ 50	Hot	Fast-track to AE; reach out within hours
≥ 70	< 50	Right fit, slow timing	Keep warm — light-touch nurture, re-check on next signal cycle
40–69	≥ 70	Reactive — wrong fit, hot moment	Discovery call to verify; could be a one-off rather than a long-term fit
40–69	< 70	Possible fit	Use research-account before reaching out
< 40	any	Not a fit	Polite nurture or disqualify

If a disqualifier rule contributed strongly negative points, surface it explicitly — that's usually a hard "no fit" regardless of the rest.

A6. Present qualification summary

## Qualification: [Company] ([domain])

**Tier:** Hot / Right fit, slow timing / Reactive / Possible fit / Not a fit

**Fit:** 84 (Δ +6)
- New VP Sales hired (+20)
- 51–200 employees (+15)
- HubSpot user (+10)

**Urgency:** 72 (Δ +12)
- Series B in last 6 months (+25)
- Hiring SDRs (+15)

**Recommendation:** [1–2 sentences explaining the tier and suggested next action]

---

Path B — Manual qualification (fallback)

Used when there's no CLI or no scoring profile.

B1. Load signal definitions

Check conversation context for approved signals (from signal-discovery or informal). If none, ask:

"Do you have defined signal questions for your ICP? If not, run signal-discovery first to set them up."

B2. Run the signals manually

Without the CLI, research each signal question against the domain — web search, LinkedIn, news, the company's own careers page.

B3. Score

Count positive signals; weight by the user's stated priorities. Apply disqualifiers as in generate-signals. Map to:

• Strong fit — majority positive, no disqualifiers
• Possible fit — mixed
• Weak fit — few positives or a disqualifier fired
• Not a fit — disqualifier or zero positive

B4. Present

## Qualification: [Company] ([domain])

**Score:** Strong fit / Possible fit / Weak fit / Not a fit

| Signal | Result | Notes |
|---|---|---|
| [question] | Yes / No | [context] |

**Recommendation:** [1–2 sentences]

---

Step 3 — Suggest next steps

• Hot / Strong fit: use write-outreach to draft a personalised first touch
• Right fit, slow timing: add to nurture; re-check when the next signal cycle runs
• Reactive / Possible fit: use research-account to validate before investing AE time
• Not a fit / Weak fit: route to a generic nurture sequence or disqualify

Related

• _shared/scoring.md — scoring concepts and CLI reference
• configure-scoring — set up scoring once, qualify many
• research-account — deeper context for borderline leads
• write-outreach — turn a hot qualification into a message