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How opportunity density is scored

We don’t just list agents β€” it tries to surface where agents should exist but are under-built. This page documents exactly how those signals are computed, including the source data, the formula, and every per-niche score.

What we're measuring

For every niche in the directory we estimate expected agent density from three independent 0-100 dimensions, then compare against observed coverage from the live directory. A niche is interesting when expected density is high but observed coverage is low.

Scoring happens in two stages. Stage 1 is a nightly batch job that writes baseline scores into the database. Stage 2 is a live re-weighting layer the opportunity map runs at render time, applying an economic-weight multiplier plus any sector / per-niche nudges the viewer has dialled in.

1. agentability
Can an agent do it?

Repetitive, digital, document-heavy work that fits LLM + tool-use cleanly scores high. Anything needing physical dexterity, deep judgement, or trust an agent can't deliver scores low.

2. economic activity
Is there money / work there?

Rough US-market spend and willingness to pay. Large buyer budgets, expensive errors, mature software categories score high. Tiny hobby workflows or low-budget areas score low.

3. geo readiness
Can it be deployed here?

Friction, not market size. Clear US buyers, accessible data, manageable regulation = high. Heavy regulation, restricted data, high liability, fragmented systems = low.

Stage 1 β€” baseline scoring (nightly)

Run by scripts/score-opportunities.js. WritesexpectedDensityScore, observedNodeCount,observedWeightedDensity, crowdingLevel, and a baselineopportunityScore back to each row in OpportunityNiche.

expectedDensityScore = round((agentabilityScore Γ— economicActivityScore Γ— geoReadinessScore) / 10,000)
penalty = min(60, round(observedWeightedDensity Γ— 8))
opportunityScore = max(0, expectedDensityScore βˆ’ penalty)

Three 0-100 inputs multiplied together, divided to renormalise to 0-100. The penalty for existing coverage is capped at 60 so a single strong incumbent can't flatten an otherwise high-expected niche to zero. observedWeightedDensity is the sum of per-agent weights from the deterministic taxonomy match (v0 weights every observed node 1.0; v1 will weight by agentLevel).

Stage 2 β€” live re-weighting (at render time)

Run by lib/opportunity-reweight.ts directly in the page. Each niche has aneconomicWeightScore on a 1-5 scale (ben + ChatGPT, 2026-05-21) that re-weights the stage-1 gap. The user can also nudge the ranking with per-sector and per-niche dials on the page; defaults are 1.0 (no change).

gapScore = max(0, (expectedDensityScore / 100) βˆ’ (observedWeightedDensity / 50))
econMult = ECON_MULTIPLIER[economicWeightScore] // 1β†’0.4, 2β†’0.7, 3β†’1.0, 4β†’1.4, 5β†’1.8
sectorMult = userSectorOverride // default 1.0
nicheMult = userNicheOverride // -1 β†’ 0.75, 0 β†’ 1.0, +1 β†’ 1.25
displayedScore = gapScore Γ— econMult Γ— sectorMult Γ— nicheMult

The ranking you see on /opportunities isdisplayedScore, not the stored opportunityScore. The stage-1opportunityScore is still used by some downstream batch jobs (the spider prioritiser, search budget allocator) where a single stable value is needed.

Where the input numbers came from

The three input scores per niche are not derived from a TAM database β€” they are heuristic. To avoid single-model bias we ran the same estimation worksheet through two independent LLMs and averaged the results:

  1. A shared worksheet was built containing every niche, the formula, the dimension definitions, sector baseline ranges (e.g. healthcare-ops: ag 70-90 / ec 75-95 / ge 60-82), and a list of special-judgement notes (e.g. β€œclinical decisioning gets lower geo than admin/RCM in the same sector”).
  2. Claude Sonnet 4.5 and ChatGPT independently filled in agentability / economic / geo for all 348 niches. No prior estimates were shown β€” each pass was anchor-free.
  3. The two estimates were averaged row-by-row. Of 348 niches, both models covered every row.
  4. Manual overrides were then applied to 8 niches across 5 modelling-edge cases where the LLMs systematically over- or under-corrected. See the override table below.
  5. The final values are the β€œFinal (live)” columns in the per-niche table at the bottom.

Override cases

These are sector groupings where one or both LLMs tended to give a number that didn't reflect the on-the-ground reality of deployment. The override clamps the averaged score into a defensible range.

groupruleexample niches
regulated-autonomygeo clamped to 55-70 β€” clinical decisioning and autonomous trading carry real liability/regulatory friction the LLMs underweighted.radiology-second-read, clinical-decision-support, algorithmic-trading-bot, portfolio-rebalancing-agent, financial-advisor-agent
healthcare-admingeo clamped to 68-78, economic floored at 80. These are admin/RCM workflows that ARE deployable with human oversight β€” don't over-penalise geo just because the sector is β€œhealthcare”.prior-authorization, claims-denial-management, medical-coding-cdi, hcc-risk-adjustment, benefits-verification
infra-docagentability clamped to 45-70. These are infrastructure / API / docs nodes β€” important to the ecosystem but they are NOT operating agents themselves.developer-portal-api-docs, web-search-api, financial-data-api-for-agents, retrieval-model-api
public-sectorgeo clamped to 65-80. Government procurement and permitting carry friction the LLMs underweighted.permit-application, permit-applications-citizen, grant-writing-nonprofit, grant-management-agent, public-rfp-response
consumer-lifestyleeconomic clamped to 45-70. Broad consumer demand doesn't translate to willingness to pay for these niches.meal-planning-agent, ai-gardening-assistant, ai-cooking-assistant, ai-personal-stylist, wellness-coaching-ai

Observed coverage & crowding

Each niche's observedNodeCount is how many agents from the live directory matched it (via the deterministic taxonomy scorer). Coverage is binned for display:

  • empty: 0 weighted nodes
  • sparse: 1-2 weighted nodes
  • emerging: 3-6 weighted nodes
  • crowded: 7+ weighted nodes

v0 weights every observed node equally. v1 will weight by agentLevel (L4/L5 = 1.0, L3 = 0.8, ..., L0 infra = 0.2-0.4) so a generic API endpoint doesn't mark a whole niche as covered. See queue for the v1 spec.

Limitations (read this part)

  • Scores are directional, not precise. This is not investment advice.
  • The input scores are heuristic β€” defensible but not derived from external market data.
  • Directory coverage is incomplete; niches can look empty because we haven't found their agents yet.
  • Geography is inferred from English-language web sources; non-US dominant niches may be under-represented.
  • Private/internal agents (Frank, Hermes, Openclaw, our curators) are excluded from observed coverage.
  • The stage-1 multiplicative formula penalises any niche with one weak dimension. Stage 2 partially fixes this by lifting niches with high economicWeightScore, but a v1 stage-1 formula using a weighted average is still queued.

Per-niche scores (348 niches)

Columns: Sonnet (ag/ec/ge), ChatGPT (ag/ec/ge), Final live (ag/ec/ge after averaging + overrides), expectedDensity, observed agents, opportunityScore. Niches with manual overrides are flagged in the right-hand column. Sorted by expectedDensity descending.

nicheparentSonnetChatGPTFinal (live)expobsoppoverride
agecgeagecgeagecge
voice-contact-centre-agentcustomer-success-supportβ€”β€”β€”β€”β€”β€”888890896429
personal-ai-assistantconsumer-personalβ€”β€”β€”β€”β€”β€”908585872427
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ai-soc-platformsecurity888592888690881009169169
rfp-responsesales-marketing-revops95759290849093809168352
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Amber-tinted rows had at least one input clamped by a manual override. Click any niche slug to see its detail page (matched agents, suggested queries, productisation copy).