Use whenever the user wants to find, shortlist, vet, or enrich US AI/ML/data consulting firms (consultancies) — AI/ML development, MLOps, generative AI / LLM apps (RAG, chatbots, agents), computer vision, NLP, recommendation systems, data engineering, BI/analytics. Triggers on "find an AI/ML consulting firm to build our recommendation engine", "shortlist three RAG/LLM consultancies for an enterprise chatbot", "compare three AI/ML consulting firms with strong ratings", or "pull contact info for these 8 AI consultancy domains", even when described indirectly (we want to use AI for X, deploy ML to production). Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog filterable by industry, services, location, size, ratings. Defer to find-software-developer for general app/backend work where AI is just a feature. Skip in-house ML/data hires, LLM/AI-tool comparisons (ChatGPT vs Claude), "how do I fine-tune X" DIY questions, AI courses for individuals, non-US firms, individual freelancers.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
Use whenever the user wants to find, shortlist, vet, or enrich US AI/ML/data consulting firms (consultancies) — AI/ML development, MLOps, generative AI / LLM apps (RAG, chatbots, agents), computer vision, NLP, recommendation systems, data engineering, BI/analytics. Triggers on "find an AI/ML consulting firm to build our recommendation engine", "shortlist three RAG/LLM consultancies for an enterprise chatbot", "compare three AI/ML consulting firms with strong ratings", or "pull contact info for these 8 AI consultancy domains", even when described indirectly (we want to use AI for X, deploy ML to production). Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog filterable by industry, services, location, size, ratings. Defer to find-software-developer for general app/backend work where AI is just a feature. Skip in-house ML/data hires, LLM/AI-tool comparisons (ChatGPT vs Claude), "how do I fine-tune X" DIY questions, AI courses for individuals, non-US firms, individual freelancers.
Drive the ServiceGraph API (https://api.servicegraph.co) to find,
shortlist, and enrich US AI/ML and data consultancies via the
pro_services dataset. The catalog tags firms with
industry:data_ai_consulting and a 4-tag service sub-taxonomy:
ai-ml-development (the largest at ~12k firms), data-analytics,
cloud-services, and api-integration. There is no
data-engineering or business-intelligence sub-tag —
data-analytics covers both. Confirm exact tag names via
/v1/datasets/pro_services/fields?include_values=1.
Always pin industry:data_ai_consulting. This skill exists to
do that automatically — the user shouldn't have to think about catalog
taxonomy.
Any HTTP client works (curl, fetch, requests). Examples below use curl.
Sibling skills — defer when scope is different
General application or backend dev that uses AI as a feature
(e.g. "build us a SaaS with an AI chatbot tab") → find-software-developer.
Web/site projects that include some AI → find-web-developer.
AI-related marketing or content → find-marketing-agency.
This skill is for engagements where the AI/ML/data work IS the
deliverable.
When NOT to use this skill
Consumer AI courses or learning ("find me an online course to learn ML").
AI/LLM product comparisons ("ChatGPT vs Claude vs Gemini", "Cursor vs Copilot").
DIY/code tasks ("how do I fine-tune Llama", "review this PyTorch loop").
In-house ML/data hires (Machine Learning Engineer, Data Scientist).
Generic AI knowledge questions.
Non-US firms / individual freelance ML engineers.
Prerequisites
ServiceGraph access, either:
the ServiceGraph MCP server (https://mcp.servicegraph.co) loaded in
your harness — this plugin's .mcp.json wires it up; OAuth 2.1 + PKCE
keeps credentials in the harness sandbox — or
a ServiceGraph API key (vk_…, minted at
https://servicegraph.co/profile/api-keys) available as
SERVICEGRAPH_API_KEY in the environment or .env.local for the REST
path (setup steps under Auth below).
An HTTP client for the REST path — the examples use curl.
Instructions
The loop is free-first: discovery, validation, search, and brief reads cost
nothing; only unlock after the user confirms the spend.
Pick the call path — the ServiceGraph MCP tools if loaded, otherwise the
REST flow (MCP server and Auth sections below).
GET /v1/datasets/pro_services/fields?include_values=1 — confirm the
fields and values you plan to filter on exist.
Build the filter (Filter DSL below) and validate it with
GET /v1/datasets/pro_services/check — or draft it from plain English via
POST /v1/datasets/pro_services/translate-intent.
GET /v1/datasets/pro_services/search — present the free brief cards and
let the user pick.
Quote the unlock cost (10 credits per row, 30-day TTL) and get an explicit
go-ahead.
POST /v1/datasets/pro_services/unlocks with the chosen apexes; report the
revealed detail.
GET /v1/me/credits to report the remaining balance when asked.
MCP server (preferred for authed calls)
If your harness has the ServiceGraph MCP server loaded (tools
containing servicegraph), prefer those — OAuth 2.1 + PKCE keeps the
token in the harness sandbox. Otherwise use the REST flow below.
API surface (dataset id: pro_services)
Every endpoint requires the bearer (Authorization: Bearer vk_…).
No anonymous tier.
Endpoint
Cost
Use it for
GET /v1/datasets/pro_services/fields[?include_values=1]
free
Confirm data_ai_consulting industry value and sub-tag names.
GET /v1/datasets/pro_services/check?filter=…
free
Validate filter.
POST /v1/datasets/pro_services/translate-intent
free
{intent} → DSL filter + sanity count.
GET /v1/datasets/pro_services/search?filter=…&limit=
free
Brief firm cards + per-row unlock hint + total.
GET /v1/datasets/pro_services/:apex
free
One row brief; detail only if unlocked.
POST /v1/datasets/pro_services/unlocks
10 credits / firm
{apexes:[...]} ≤100; atomic; 30-day TTL on detail.
GET /v1/me/credits
free
Balance.
Cost model. Discovery / validation / search / brief reads are
free. Detail (url, phone, email, social, address, full platforms
map) costs 10 credits per firm and lasts 30 days.
Auth
vk_* API keys minted in the dashboard. Keep the token out of the
LLM context — never read .env* into your context; dispatch via
shell.
Try the call first through a shell wrapper that sources
.env.local:
On 401 prompt the user (don't accept the key in chat):
"Open https://servicegraph.co/profile/api-keys, create a
key, and add SERVICEGRAPH_API_KEY=vk_… to .env.local here
(or export it). Tell me when done. Please don't paste the key
into chat."
Retry after the user signals ready.
Filter DSL
GitHub-search-style.
filter := orExpr
orExpr := andExpr ("OR" andExpr)*
andExpr := notExpr (("AND")? notExpr)* # whitespace = implicit AND
notExpr := ("NOT" | "-") notExpr | atom
atom := "(" filter ")" | predicate
predicate:= IDENT op valueOrList | bareword
op := ":" | "=" | ">=" | "<=" | ">" | "<"
valueOrList := value ("," value)*
value := IDENT | NUMBER | tagAtEvidence
tagAtEvidence := IDENT "@" ("low"|"medium"|"high")
bareword := IDENT | NUMBER # → keyword:<bareword>
Four rules that bite: AND binds tighter than OR (use parens);
comma list = OR within one predicate; negation is -x or NOT x;
bareword = keyword search (quote multi-word phrases).
AI-flavored examples (validate yours with /check):
service_provided:data-analytics + keywords pipelines / engineering (no data-engineering tag)
BI / analytics
service_provided:data-analytics (covers BI too — no separate business-intelligence tag)
Cloud architecture for data/ML
service_provided:cloud-services
API / data integration
service_provided:api-integration
LLM apps / RAG / agents
llm, rag, agent (keywords)
Generative AI
"generative ai", genai
Computer vision
"computer vision", cv
NLP / IDP / document understanding
nlp, idp, "document understanding"
MLOps / model deployment
mlops, deployment
Recommendation systems
recommendation, recsys
Predictive analytics / churn / forecasting
predictive, forecasting, churn
Identifying firms — apex
Firms are identified by their apex domain (scaleai.com, not
www.scaleai.com/about).
Output
All responses are JSON.
Search returns free brief firm cards — apex, name, location, and
rating signals — plus a per-row unlock hint and the match total. Briefs
never include url, phone_primary, email_primary, legal_name,
address_full, or the full platforms map.
Unlock (POST …/unlocks) returns each unlocked firm's detail block —
contact fields, address, socials, the platforms map — plus per-item
billing; detail stays readable for 30 days.
Errors arrive as a JSON envelope
{"error": {"code": "…", "message": "…"}} — see Errors below.
Examples
A. AI/ML consultancy for a recommendation engine
User: "AI/ML consultancy to build our recommendation engine for an ecommerce site."
GET /v1/datasets/pro_services/search?filter=industry:data_ai_consulting+service_provided:ai-ml-development+recommendation+ecommerce&limit=10
# Present, get pick of 3. "Unlocking 3 = 30 credits, 30-day TTL."
POST /v1/datasets/pro_services/unlocks
{ "apexes": ["firm-a.com", "firm-b.com", "firm-c.com"] }
B. RAG / LLM consultancies for a chatbot
User: "Three RAG/LLM consultancies for an enterprise chatbot."
GET /v1/datasets/pro_services/search?filter=industry:data_ai_consulting+(rag OR llm)+chatbot+enterprise&limit=10
If thin, drop enterprise and surface client-tier signals from the
unlocked detail later.
C. Data engineering partner
User: "Data-engineering partner to build our analytics pipelines."
No data-engineering tag — data-analytics is the closest and
covers both BI and engineering. Pin the tag plus keyword:
GET /v1/datasets/pro_services/search?filter=industry:data_ai_consulting+service_provided:data-analytics+(pipelines OR engineering)&limit=10
D. MLOps for model deployment
GET /v1/datasets/pro_services/search?filter=industry:data_ai_consulting+mlops&limit=10
E. Indirect intent — "use AI to predict customer churn"
User: "We want to use AI to predict customer churn — who can help us build that?"
GET /v1/datasets/pro_services/search?filter=industry:data_ai_consulting+service_provided:ai-ml-development+(churn OR predictive)&limit=10
Or let the translator do the mapping:
POST /v1/datasets/pro_services/translate-intent
{ "intent": "AI consultancy to build customer churn prediction" }
F. Computer vision + healthcare vertical
GET /v1/datasets/pro_services/search?filter=industry:data_ai_consulting+"computer vision"+healthcare&limit=10
G. Quality threshold + Fortune 500 clients
GET /v1/datasets/pro_services/search?filter=industry:data_ai_consulting+service_provided:ai-ml-development@high+rating>=4+fortune&limit=10
"Fortune 500" as a structured filter isn't a thing — surface from
briefs or treat it as a keyword.
H. Custom LLM agent for customer service
GET /v1/datasets/pro_services/search?filter=industry:data_ai_consulting+(llm OR agent)+("customer service" OR support)&limit=10
I. BYO apex list — enrich domains
User pastes 8–20 AI consultancy domains:
GET /v1/datasets/pro_services/:apex per domain — free brief
(404 = not in catalog, no charge).
User picks N to fully enrich. POST /unlocks = 10×N credits,
atomic, detail returned.
Re-runs within 30-day TTL are free.
A 404 here often means the firm is actually a SaaS product company
(many AI vendors brand as "AI services" but operate as a product) —
filtered out of the catalog.
Gotchas
Always pin industry:data_ai_consulting. Without it, ai-ml-development as a service tag surfaces IT firms that list AI as a sub-service.
Defer to find-software-developer for general dev that uses AI as a feature. When the deliverable is a SaaS product or app and AI is one of several features, that's software-dev work; this skill is for engagements where AI/ML/data work IS the deliverable.
Catalog audit notes: AI/ML-tagged firms have a higher historical rate of misclassification (some are SaaS products, some are B2C ed-tech). If an unlock returns a SaaS product, flag and skip rather than recommend.
Many sub-niches are keyword-only. Multi-word sub-niches split into ANDed barewords unless quoted (computer vision → computer AND vision; "computer vision" → one phrase).
LLM-product comparisons (ChatGPT vs Claude vs Gemini) are NOT procurement — refuse.
AI courses for individuals (Coursera, fast.ai) are NOT in the catalog — refuse.
Briefs DO include apex, name, industry, service_provided, location, ratings. They DON'T include url, phone_primary, email_primary, legal_name, address_full, full platforms — those require an unlock.
not_found / not_in_dataset 404 = not in pro_services. Not charged. Skip.
Unlock is atomic. N apexes either all charge (up to 10×N credits) or none on 402.
