review-candidates-auto

name

review-candidates-auto

description

Triage the Superposition candidate review backlog unattended ("full auto"). Use when the user asks to auto-review, clear, or work through the awaiting-review queue/backlog, review all candidates for a job without stopping, or batch-process candidates. For decisions the user wants to make themselves one-by-one, use review-candidates-human-loop instead. Requires the company soul to be set up first (bootstrap-company-soul) — assessments are weak without it.

Review candidates — full auto

Work through the awaiting-review backlog without pausing between candidates. For each one you gather the FitCheck context with read tools, then let the Master Agent record the decision through invoke_agent. "Full auto" means you don't stop to ask the human between candidates — it does not mean skipping Superposition's own approval gates. The Master Agent still enforces them (e.g. it won't schedule before client approval); when it escalates or needs approval, surface that in your summary.

Prerequisites

• Connected with the right active org (connect-superposition).
• Company soul is populated (bootstrap-company-soul). If get_company looks thin, stop and recommend bootstrapping first — auto-reviewing against a weak profile produces low-quality calls.

Steps

1. Scope the org. get_superposition_context to confirm the active org.
2. List active roles. list_jobs and keep the ACTIVE ones.
3. Size the backlog. get_pipeline_metrics to see how many candidates are awaiting review per job. Report the total up front and, if it's large, confirm a batch cap (default: 25 per run) before proceeding so you don't run unbounded.
4. Pull the queue per job. search_candidates({ jobId, stages: ["AWAITING_CLIENT_REVIEW"] }) to list the awaiting-review candidates.
5. Load assessment context. get_candidates({ candidateIds: [...] }) (batched, up to 20 per call) for the FitCheck rationale, calibrated traits, and source.category (INBOUND vs OUTBOUND).
6. Record each decision via the Master Agent. For each candidate call invoke_agent, threading the returned sessionId:

   "Review candidate <candidateId> for job <jobId>. Assess fit against the role using their FitCheck assessment and record the decision with brief reasoning. Follow the standard pipeline rules; if anything needs client approval or human input, say so instead of forcing it."

7. Summarize. Produce one table: candidate (linked), the decision the agent recorded, a one-line reason, and a flag for anything escalated or left pending. Note how many were processed vs. skipped.

Output

Auto-review — Acme · Senior Backend Engineer (awaiting review: 18, processed: 18)

| Candidate                                              | Decision        | Why                                  |
|--------------------------------------------------------|-----------------|--------------------------------------|
| [Diego Prats](https://app.superposition.ai/c/abc-123)  | Advanced        | Strong distributed-systems match     |
| [Mei Tan](https://app.superposition.ai/c/def-456)      | Passed          | Below the experience floor for L5    |
| [Sam Okoro](https://app.superposition.ai/c/ghi-789)    | Escalated       | Comp expectation > range — needs client |

Processed 18/18. 1 escalated to a human. None skipped.

Guardrails

• Never fabricate decisions. Report exactly what the Master Agent recorded. If a call fails or is ambiguous, mark it pending rather than inventing an outcome.
• Respect server gates. If the agent says it needs client approval, can't email an inbound candidate, or escalates — relay it; don't try to route around it.
• Stay bounded. Honor the batch cap; if more remain, report the remaining count and offer to run the next batch.
• Use review-candidates-human-loop when the user wants to make the calls themselves.