No technical skills needed. Connect DINQ's talent data to Codex, write one clear instruction, and get a client-ready candidate mapping — complete with profiles, contact info, and tiering.
Create an API Key and the matching Remote MCP Server URL to copy will appear below.
DINQ Search under Name, paste the Remote MCP Server URL into the URL field, then select Save;Use DINQ to find quantitative researchers, prioritizing candidates with systematic trading or alpha research experience.Results mostly come down to whether you clearly stated the role, scope, fields, and standard. Just copy the template below and fill in the placeholders.
Fill in the placeholders in the brackets and send it to Codex. Start with a 10–20 person sample the first time, and scale up once the format looks right.
Using the connected DINQ tool, help me build a headhunter-grade talent mapping that meets the delivery standard of the top-5 international executive search firms. [Role background] - Target role: (e.g. Quant Researcher / Autonomous Driving Algorithm Engineer / IR Director) - Location: (e.g. Hong Kong / Beijing & Shanghai / US, bilingual Chinese background preferred) - Target companies: (list 5–15 target companies; search them one at a time) [Required fields] Each candidate must include: 1. Name (Chinese and English, if applicable) 2. Current company + title + city 3. Full LinkedIn URL (must be a real, clickable personal profile link in the format https://www.linkedin.com/in/xxx/ — mark "-" if not found, never fabricate) 4. Email (state status: verified / inferred from company format, unverified / none — suggest InMail) 5. Personal site / GitHub / Google Scholar (exhaustively search these for technical or academic backgrounds) 6. Education and years of experience 7. Tier: Tier A (strong match, write a rationale for each person) / Tier B / Tier C [Data quality requirements] - Cross-check DINQ's results with a web search: verify whether the candidate has switched jobs, whether the role is stale, and whether the location matches; - Flag any name confusion, outdated info, or candidates clearly outside scope; - Mark any missing data as "-" — never leave it blank, and never fabricate. [Delivery format] (e.g. a bilingual Chinese/English PowerPoint deck with a dark, premium color scheme; or start with a table I can review first)
If you've already shortlisted a batch of candidates in DINQ, we strongly recommend this workflow:
Export your shortlisted candidates from DINQ as a CSV / spreadsheet file (with a LinkedIn link column).
Drag the exported file straight into Codex. This file becomes the "source of truth" for the roster and LinkedIn links.
In your prompt, tell Codex to "treat the uploaded file as the source of truth — use DINQ and web search to correct errors, fill in contact info, update roles, and tier the candidates."
Check against these before delivery — if it falls short, have Codex keep revising.
Complete structure: overview, talent distribution, tiering framework, individual profiles, outreach suggestions. Professional visuals, ready for the client as-is.
LinkedIn, email, GitHub, and Google Scholar searched one by one; anything not found is honestly marked — never fabricated.
Use Codex to cross-check whether roles are stale or people have moved on, fill in background info, and turn the raw data into a finished, tiered, judgment-informed product.
Break "search 10 companies for candidates" into one company at a time — DINQ's results are noticeably better that way.
DINQ's deep search starts a job and polls for results; it's normal for Codex to wait and query a few times, usually resolving within a minute or two.
Email coverage is probabilistic. Addresses marked "inferred, unverified" must be verified before sending, or you'll hurt deliverability and brand reputation.
Spot-check a few links actually open before delivery. Requiring Codex to use only real links and mark missing ones with "-" is a hard rule to put in the prompt.
Every search consumes account Credits, from the same pool as web-app searches. Before batch-running mappings for multiple roles, check your remaining usage first so you don't run out halfway through.
Codex is a product by OpenAI. DINQ connects to it through MCP; candidate data comes from your own DINQ account. See DINQ's Privacy Policy and Terms of Service. Questions? Email [email protected].