recruit

Recruit

A job post in, a ranked shortlist out: people who fit, one line on why, and a way to reach them. Every stage is counted and every contact lookup leaves a vendor trail, written to a run file.

Runs on treg. If it isn't set up (treg --version fails), read https://treg.to/llms.txt and follow it to install and sign in, then come back here.

The run

Read the job. treg catalog search "linkedin job details", then call the best row with the post URL. No post URL: use the text the user pasted.
Extract criteria from the post, yourself: title variants (the title as posted plus two to four names the same job goes by), must-have skills, seniority, locations (and whether remote is fine), and the hiring company, which is always excluded. Show the criteria to the user in five lines before spending; adjust if they object.
Search with the routed people search, one call per title variant (and per location when there are several):

treg call treg.people.search --data '{"title":"Staff Backend Engineer","location":"Berlin, Germany","country":"DE","keywords":["go","kafka"],"limit":25}'

limit is the price dial: most providers bill per row. Start at 25 per query; raise it only when the funnel comes out thin. Read _treg.ignored_filters: a provider that could not apply a filter answered a looser question, so those rows get the hard filters below with no benefit of the doubt. 4. Dedupe by LinkedIn URL (lowercase, no query string, no trailing slash); rows without one by name + company. 5. Hard filters. Drop, and record a reason for each drop: works at the hiring company, wrong location, seniority too far off, title from another job family, no profile URL to contact from. 6. Rank, yourself: read each survivor against the criteria and write one line, in the user's words, on why they fit ("8 yrs Go at a payments scale-up, led a Kafka migration"). No line you can write, no rank. Keep the top N (default 20). 7. Evidence (optional, cheap), for the top E of them (default 10). Gather cited facts, then re-score: a claim the evidence backs moves a candidate up, one it contradicts moves them down. Cap each candidate's evidence spend (default $0.05) and stop at the cap. Skip a source when the person has nothing for it (no GitHub handle, no posts); never guess a handle.

Evidence	Call	Use it for
Recent LinkedIn posts	`anyapi.linkedin.profile_posts_full` with `{"url": "<profile>", "limit": 5}` (or `_thin`, whichever `treg catalog get` prices lower)	what they work on now, in their words
GitHub	`treg catalog search "github user profile"` for the user, then `crawl4ai.github.repo.profile` on their top repos	languages, stars, repos that match the job's skills
Talks and writing	`anyapi.youtube.search` and `anyapi.google.scholar`, query `"<name>" <topic>`	talks and papers on the job's topic; keep only results that name them
Open to move	`datagma.people.job-change.detect` (`fullName`, `companyName`, `jobTitle`); tenure from the search row; `akta.companies.news` once per current company, shared by everyone there	a recent move (less likely to move again), long tenure, layoffs or a funding gap at their company

Read each answer before you cite it: a search result that does not name the person is not evidence. The posts feed mixes in other people's posts; count only items whose author.handle is the profile's own, from the last six months. The job-change check answers a status (NO_NEW_COMPANY means still there). When a source refuses (a provider out of capacity), skip it for the run; its refusal names alternatives, which you may use if they fit under the cap. treg catalog get <id> first if a call is new to you: the catalog changes, and the price and parameters on the row win over this table. 8. Contact, for the top N only:

treg call treg.people.email.find.personal --data '{"linkedin_url":"…"}' --header 'X-Treg-Route-Verify: true' --header 'X-Treg-Route-Max-Cost: 0.30'
treg call treg.people.phone.find --data '{"linkedin_url":"…"}' --header 'X-Treg-Route-Verify: true' --header 'X-Treg-Route-Max-Cost: 0.30'

Both are routed: treg tries providers cheapest per hit first, most misses are free, and the verify header checks a hit in the same call (the mailbox; for a phone, that the line is live). Send only the LinkedIn URL: it is what every provider takes. Never re-send a lookup for the same person; every hit bills, repeats included. 9. Hand back the shortlist and the run file, and state coverage plainly: "214 found, 61 kept, top 20: 14 emails, 9 live mobiles, $4.12".

The run file

Write recruit-run-<YYYYMMDD-HHMM>.json next to the user's work, and update it after every stage, so a stopped run still says what it did. Money comes from the answers, never from a price list: _treg.charged_micro on each call (micro-USD), _treg.tried for the vendors asked, and _treg.verification.cost_micro for the check.

{
  "job": {"url": "…", "title": "…", "company": "…", "company_domain": "…", "location": "…"},
  "criteria": {"titles": ["…"], "skills": ["…"], "seniority": "senior", "locations": ["…"],
               "remote_ok": false, "exclude_companies": ["…"]},
  "stages": [
    {"stage": "job",     "calls": 1, "charged_usd": 0.0},
    {"stage": "search",  "calls": 6, "rows": 214, "charged_usd": 0.0},
    {"stage": "dedupe",  "in": 214, "out": 163},
    {"stage": "filter",  "in": 163, "out": 61,
     "reject_reasons": {"hiring company": 9, "location": 52, "seniority": 27, "job family": 14},
     "reject_samples": [{"name": "…", "linkedin_url": "…", "reason": "location: Munich"}]},
    {"stage": "rank",    "in": 61, "out": 20},
    {"stage": "evidence", "people": 10, "cap_usd": 0.05, "charged_usd": 0.0,
     "by_source": {"linkedin_posts": {"tried": 10, "found": 7, "charged_usd": 0.0}}},
    {"stage": "contact", "people": 20, "emails": 14, "phones": 9, "charged_usd": 0.0}
  ],
  "candidates": [
    {"rank": 1, "name": "…", "title": "…", "company": "…", "location": "…", "linkedin_url": "…",
     "why_fit": "…", "score_before": 7, "score_after": 8,
     "evidence": [
       {"type": "linkedin_posts", "claim": "…", "source": "anyapi.linkedin.profile_posts_full",
        "url": "…", "charged_usd": 0.0}
     ],
     "evidence_usd": 0.0,
     "email": {"value": "…", "verdict": "valid"},
     "phone": {"value": "…", "verdict": "live"},
     "reach": [
       {"lookup": "email", "endpoint_id": "…", "vendor": "…", "outcome": "miss", "charged_usd": 0.0},
       {"lookup": "email", "endpoint_id": "…", "vendor": "…", "outcome": "hit", "charged_usd": 0.0},
       {"lookup": "email_check", "endpoint_id": "…", "verdict": "valid", "charged_usd": 0.0},
       {"lookup": "phone", "endpoint_id": "…", "vendor": "…", "outcome": "hit", "charged_usd": 0.0},
       {"lookup": "phone_check", "endpoint_id": "…", "verdict": "live", "charged_usd": 0.0}
     ]}
  ],
  "totals": {"charged_usd": 0.0, "emails": 14, "phones": 9}
}

evidence holds only what a call returned: type (linkedin_posts, github, talks, papers, job_change, tenure, company_news), a one-line claim, the source endpoint id (search_row for tenure), the url when there is one, and that call's charged_usd. A shared company-news call is charged once, to the first candidate who used it.
reach is one entry per row of _treg.tried (endpoint_id, provider as vendor, outcome, charged_micro / 1e6 as charged_usd), then one for _treg.verification when a check ran (served_by, verdict, cost_micro). A skipped attempt keeps its outcome: skipped.
email / phone are null on a miss. verdict is the check's own word: an email valid, invalid, catch_all, risky or unknown; a phone live, dead or unknown. A live line is not proof it is this person's number; never call a phone "verified".
reject_samples: up to five per reason, so the reasons can be checked by eye.
totals.charged_usd is the sum of every charged_usd above: job, search, evidence and contact.

Before the first list

Say once: contacting people in the EU and UK needs a lawful basis, and anyone who asks to be left alone is dropped for good. Then hand over the list; the user decides how to use it.

About this skill

The run

The run file

Before the first list