tenacious-signal-brief

name

tenacious-signal-brief

description

Generate a hiring signal brief and competitor gap brief for a Tenacious Consulting prospect from public data. Use this skill whenever the user mentions building outbound for Tenacious, enriching a prospect, the "hiring signal brief," "competitor gap brief," scoring AI maturity (0-3), combining Crunchbase + layoffs.fyi + job-post + leadership signals, or any phrasing like "research this company before we email them." Always use this instead of ad-hoc enrichment — the honesty and confidence-language rules (no "aggressive hiring" below five open roles, confidence-weighted phrasing, no fabricated numbers) are Tenacious brand constraints, not style preferences, and getting them wrong damages the client's reputation.

Tenacious Hiring Signal Brief

A prospect-enrichment skill that produces two JSON artifacts before any outbound:

1. hiring_signal_brief.json — a per-prospect view of the five signals that define their buying window.
2. competitor_gap_brief.json — the prospect's AI-maturity position against 5–10 top-quartile peers in their sector.

Together these convert outbound from "you might need offshore engineering" into a grounded research finding the prospect cannot object to without contradicting their own public record.

Why the honesty rule is load-bearing

Tenacious's reputation is the moat. Over-claiming a signal ("you are scaling aggressively" when the prospect has three open roles) costs more than silence. Every field in the output carries a confidence score, and the agent that consumes this brief MUST shift its language when confidence is low — asking rather than asserting. If you cannot ground a claim in the brief, leave the field null. A null field is a signal to the downstream agent to omit that line, not to improvise.

Input

Either:

• A company name + domain, OR
• A Crunchbase ODM record ID

Seed data available (from the challenge seed repo):

• Crunchbase ODM sample (1,001 companies, Apache 2.0)
• layoffs.fyi CSV (CC-BY)
• Public job-post snapshot (early April 2026) or small live crawl (≤200 companies, no login, respect robots.txt)

The five signals

Signal	Source	Output field
Funding event (Series A/B in last 180 days)	Crunchbase ODM + press	funding.event, funding.amount_usd, funding.closed_at, funding.confidence
Job-post velocity (60-day delta)	BuiltIn, Wellfound, LinkedIn careers pages	hiring.open_eng_roles, hiring.delta_60d, hiring.confidence
Layoffs (last 120 days)	layoffs.fyi	layoffs.event, layoffs.headcount_pct, layoffs.closed_at, layoffs.confidence
Leadership change (CTO/VP Eng in last 90 days)	Crunchbase + press	leadership.change, leadership.role, leadership.appointed_at, leadership.confidence
AI maturity score (0–3)	Job posts + team page + GitHub org + exec talks + tech stack	ai_maturity.score, ai_maturity.inputs[], ai_maturity.confidence

AI maturity scoring — the part most agents get wrong

The score is a 0–3 integer with per-input justification. Weights:

• High: AI-adjacent open roles as a fraction of total eng openings; named AI/ML leadership on public team page.
• Medium: Public GitHub org commits on model/inference repos; CEO/CTO posts or keynotes in last 12 months naming AI as strategic.
• Low: Modern data/ML stack signal (dbt, Snowflake, Databricks, W&B, Ray, vLLM via BuiltWith/Wappalyzer); annual-report or fundraising-press AI positioning.

Rules:

• Absence is not proof of absence. Many companies keep AI work private. A score of 0 with low input count is "no public signal," not "no AI work."
• Score 2 from weak inputs ≠ score 2 from strong inputs. Emit a confidence field (low/medium/high) alongside the score.
• A readiness of 2 inferred from one medium-weight input must be flagged low-confidence so the downstream agent softens the language.

Competitor gap brief

Once AI maturity is scored:

1. Identify 5–10 top-quartile competitors in the prospect's sector and size band using Crunchbase industry + employee-count filters.
2. Run the same AI-maturity scoring on each.
3. Compute the prospect's position in the sector's distribution (percentile + rank).
4. Extract 2–3 specific public-signal practices the top quartile shows that the prospect does not (e.g., "three of five top-quartile peers have a Head of AI on the public team page; prospect does not").

The output is a research finding, not a pitch. The downstream agent uses it to lead with a gap observation, then pivots to Tenacious capability only if the prospect engages.

How AI maturity changes the pitch (downstream consumers care)

Emit a pitch_guidance field the email drafter reads:

• Segment 4 (capability gap / ML platform / agentic): Only viable at ai_maturity.score >= 2. Below that, flag segment_4_viable: false so the email drafter does not reach for it.
• Segment 1 (recently funded): Always viable, but language shifts. High readiness → "scale your AI team faster than in-house hiring supports." Low readiness → "stand up your first AI function with a dedicated squad."
• Segment 2 (mid-market restructuring): Same shift as Segment 1.
• Segment 3 (leadership transition): Unaffected — the new leader's stance is the variable, not prior state.

Output schema

Emit two files. Exact schema:

// hiring_signal_brief.json
{
  "prospect": {"company": "string", "domain": "string", "crunchbase_id": "string"},
  "enriched_at": "ISO-8601 timestamp",
  "funding": {
    "event": "Series A | Series B | null",
    "amount_usd": 14000000,
    "closed_at": "2026-02-14",
    "confidence": "high | medium | low",
    "sources": ["url1", "url2"]
  },
  "hiring": {
    "open_eng_roles": 27,
     
     
     
     
  
   
     
     
     
     
     
  
   
     
     
     
     
     
     
  
   
     
     
     
             
           
             
    
  
   
     
     
     
  

// competitor_gap_brief.json
{
  "prospect": {"company": "string", "sector": "string", "size_band": "50-200"},
  "cohort": [
    {"company": "string", "ai_maturity": 3, "source_urls": ["..."]}
  ],
  "prospect_position": {"percentile": 35, "rank": "7 of 10"},
  "gaps": [
    {
      "practice": "Named Head of AI on public team page",
       
       
       
    
  

Output discipline

• Null over guess. A field you cannot ground goes null with no fallback text.
• Every non-null field carries a confidence value and a sources array. No exceptions. The email drafter refuses to quote uncited claims.
• No fabricated numbers. Making up a funding amount or headcount is a disqualifying violation per the evidence-graph grading rule.
• Stamp enriched_at. HubSpot records require current enrichment timestamps; stale briefs are a tracked failure mode.

Probe-triggering failure modes to avoid

These are the exact adversarial probes the challenge will grade you on. Fail them here and you fail them downstream:

• ICP misclassification. A company with both a recent layoff AND recent funding goes into Segment 2 (restructuring), not Segment 1 (freshly funded). Flag both signals; let the ICP classifier decide.
• Signal over-claiming. Fewer than 5 open roles → do NOT emit hiring.delta_60d as "aggressive." Emit the raw number; let the downstream agent decide the language.
• AI-maturity false positive. A "loud but shallow" company (one exec keynote, no team, no roles) must not score 3. Require at least one HIGH-weight input for any score ≥ 2.
• AI-maturity false negative. A "quiet but sophisticated" company (no public signal but real AI work) will score 0. Note this in ai_maturity.language_notes so the email drafter asks rather than asserts absence.
• Gap over-claiming. Do not emit a gap unless ≥ 3 of the cohort show the practice publicly. Sector-universal practices only.

When to hand off to a human

If any of these trigger, set requires_human_review: true and stop:

• Prospect appears in layoffs.fyi within last 30 days with ≥25% headcount cut (sensitive; tone risk).
• Leadership change is a founder departure or a publicly announced restructure (brand risk).
• Competitor gap brief would name a direct Tenacious client (from delivery_bench_summary.md → do not use client names in a gap brief under any circumstance).

Bench-to-brief match

Before closing the brief, cross-reference the prospect's implied need against bench_summary.md:

• If hiring signals point to "Python data engineers" and the bench has zero available, emit bench_match: {matched: false, gap: "python_data"}. The email drafter will then avoid specific capacity commitments.
• Never hallucinate bench capacity. The bench summary is ground truth.