Every 15 minutes
Screen emailed CVs into a Supabase candidates table
CVs sent to your hiring inbox are read, summarised and scored against the role, and each candidate is added to your Supabase table.
- Runs
- Every 15 minutes
- Apps
- Gmail, Supabase
- AI
- Yes, uses the built-in AI allowance
- Access
- Gmail readonly; Supabase write
Send this to your assistant
Create a ClickRuntime automation: every 15 minutes, take new emails with CV attachments, extract the candidate's details and score them against the role with AI, and insert them into my Supabase candidates table.
First time? Adding ClickRuntime to your assistant takes about two minutes.
Why use it
Why teams use this automation
A small company posts a job and gets a hundred CVs by email in a week. Opening each attachment, noting the details and deciding who to call takes days, and strong candidates accept other offers in the meantime.
Reading each CV as it arrives and storing a summary and score in a table lets you sort candidates in minutes and call the best ones while they're still available.
What you get out of it
Shortlist in minutes
Sort the table by score and start with the strongest matches.
Respond to good candidates fast
Top candidates hear from you within days, not weeks.
Consistent first look
Every CV is assessed against the same role description.
A proper record
Name, contact details, summary and the original email are kept together.
How it works
What happens, step by step
Every 15 minutes, ClickRuntime checks your hiring inbox for new emails with a CV attached.
It reads the text of each PDF.
AI picks out the candidate's name, email and experience, writes a short summary and scores them against the role.
Each candidate is added to your Supabase candidates table with a link to the email.
Emails already handled are remembered, so nobody is added twice.
Before it runs live
- Connect Google through your own Google app (a one-time, 10-minute setup), so it works with your own Gmail
- Connect Supabase
What you get
Build your own
Need it to work differently?
Your own rules
Conditions, thresholds, filters and exceptions, written the way you'd explain them to a colleague.
Any number of apps
One automation can read from one app and update several others. Over 248 apps are ready, and anything with an API works too.
AI where it helps
Summaries, classification, extraction and drafts use ClickRuntime's built-in AI, with no extra key.
On a schedule or on each event
Runs as often as every 5 minutes, or the moment an app sends an event, and keeps track of what it already handled.
Changes you can ask for
- Act only on the items that match your own rules, and skip the rest
- Change what the AI looks for, or how it writes the result
- Add another app, for example also send it to Slack
- Combine it with other Gmail and Supabase steps into one automation
Under the hood
The code that runs this automation
import base64, io
from pypdf import PdfReader
from google.oauth2.credentials import Credentials
from googleapiclient.discovery import build
from clickruntime import on_trigger, credentials, http, state, ai, report_fetched
ROLE = "Customer success manager: 3+ years SaaS, B2B accounts, English and Spanish"
SUPABASE = "https://your-project.supabase.co/rest/v1"
def sb(method, path, **kw):
key = credentials.secret("SUPABASE_KEY")
r = http.request(method, f"{SUPABASE}/{path}", **kw, headers={"apikey": key, "Authorization": f"Bearer {key}", "Prefer": "return=representation,resolution=merge-duplicates"})
return r.raise_for_status() or (r.json() if r.content else None)
SCHEMA = {"type": "object", "properties": {"name": {"type": "string"}, "email": {"type": "string"}, "summary": {"type": "string"}, "score": {"type": "integer"}}}
@on_trigger
def run(event):
gmail, done = build("gmail", "v1", credentials=Credentials(credentials.connection("google", "work")), cache_discovery=False), set(state.get("done") or [])
msgs = [m for m in gmail.users().messages().list(userId="me", q="to:jobs@northwind.com has:attachment filename:pdf newer_than:2d").execute().get("messages", []) if m["id"] not in done]
report_fetched(len(msgs))
if not msgs:
return {"summary": "Nothing new"}
for m in msgs:
full = gmail.users().messages().get(userId="me", id=m["id"]).execute()
part = next(p for p in full["payload"]["parts"] if p.get("filename", "").lower().endswith(".pdf"))
data = gmail.users().messages().attachments().get(userId="me", messageId=m["id"], id=part["body"]["attachmentId"]).execute()["data"]
text = " ".join(p.extract_text() or "" for p in PdfReader(io.BytesIO(base64.urlsafe_b64decode(data))).pages)[:8000]
c = ai.generate(f"Role: {ROLE}\nExtract name, email, a 2-sentence summary and a 1-10 fit score from this CV:\n{text}", json_schema=SCHEMA, max_output_tokens=250).json
sb("POST", "candidates", json={**c, "message_id": m["id"], "file": part["filename"]})
done.add(m["id"])
state.set("done", list(done)[-2000:])
return {"candidates": len(msgs)}FAQ
Common questions
Does the score decide who gets hired?
Which file types work?
Is candidate data kept private?
What permissions does it need?
Does it need AI?
How many runs does it use?
Can I change how it works?
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