Stage: Signal foundations

Generate Signals

Use this skill to turn a structured ICP into a full signal set — 12–15 research questions that can be run against any list of prospect companies in Saber.

Run extract-icp first if you don't have a structured ICP yet.

Input

Provide the ICP output from extract-icp, including:

• Pain points
• Buying triggers
• Target company profile (size, industry, stage)
• Optional: targeting context (e.g. "focus on NetSuite users")

The core insight: inverse pain points

Pain points describe what customers LACKED before buying. Good prospects ALSO lack these things right now.

When generating signals, look for companies that currently have the problem — not ones that already solved it.

Signal categories

Generate signals across three categories:

icp_fit (4–6 signals)

Tests whether the company profile matches the ICP target.

• Employee count, industry, stage, geography, tech stack requirements
• These establish baseline fit — wrong profile = wrong prospect regardless of timing

urgency (3–5 signals)

Tests whether a recent event creates buying pressure.

• Must include explicit time bounds — e.g. "in the last 6 months", "in the last 9 months"
• Never phrase as "Has the company ever..." — that returns stale data
• Based on buying triggers from the ICP
• A missing urgency signal is not a disqualifier — it means "right fit, wrong time" (−15 points)

buying_signal (4–5 signals)

Tests whether the company shows evidence of the pain point.

• Derived from ICP pain points using the inverse framing above
• A missing buying signal means no evidence of pain → likely a poor fit
• Never set as a disqualifier — buying signals are bonuses, not hard requirements

Signal format

Output each signal in this format:

Signal {N} — {category}
  Question:    {the exact research question to ask about a prospect company}
  AnswerType:  {boolean | number | open_text}
  DerivedFrom: {pain_point | buying_trigger | icp_constraint}
  SourceText:  {the exact pain point or trigger this is based on}
  Rationale:   {why this signal identifies a good prospect}

  Interpretation:
    [For boolean]
      positiveAnswer:   true / false   (which answer indicates a fit?)

    [For number]
      numberRange:      { min: X, max: Y }
      higherIsBetter:   true / false   (if outside range, which direction is better?)

    [For open_text]
      positiveKeywords: ["word1", "word2", ...]
      negativeKeywords: ["word1", "word2", ...]

    [All signals]
      isDisqualifier:   true / false   (wrong answer → prospect score = 0?)
      weight:           1 / 2 / 3      (1 = nice-to-have, 2 = important, 3 = critical)

Disqualifier rules

When to set isDisqualifier: true:

• The product only works with a specific tech stack (requires NetSuite, Salesforce, AWS)
• The product is only relevant to a specific industry or geography
• The prospect would be a direct competitor

When to NEVER set isDisqualifier: true:

• buying_signal category — these are bonuses, never gatekeepers
• urgency category — timing signals never eliminate a prospect
• Any signal based on a "nice-to-have" criterion

Weight guide

Scoring algorithm

Once signals are run against a prospect:

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

Saber CLI — activating signals

Once the signal set is approved, use create-company-signals to activate them in Saber:

saber subscription create \
  --list <listId> \
  --name "<signal name>" \
  --question "<signal question>" \
  --answer-type boolean \
  --frequency weekly

Run one subscription create command per signal. The signal metadata (weight, category, interpretation rules) should be kept in conversation context for use by score-accounts.

Quality checks

Before finalising:

• Every urgency signal has an explicit time bound ("in the last 6 months" — not "recently" or "ever")
• No buying_signal has isDisqualifier: true
• No urgency signal has isDisqualifier: true
• At most 2–3 signals have weight: 3 (reserve for truly critical signals)
• Each signal maps back to a specific pain point, trigger, or ICP constraint
• open_text signals have both positiveKeywords and negativeKeywords defined
• The signal set covers all three categories: icp_fit, urgency, buying_signal
• At least one signal uses the inverse pain point framing (looking for absence of a capability)

Next steps

• Use create-company-signals to activate the signals against a Saber list
• After signals run, use score-accounts to rank accounts using the weighted scoring model above