Assess applications against a role's written scoring criteria, recording strengths, gaps, and supporting resume quotes. Save a structured review and propose tentative interview times for strong matches, while leaving all advancement and rejection decisions to the hiring manager.
中文简介正在准备中。当前显示英语简介。
Scores an inbound application against the role's written rubric with supporting quotes as evidence, writes a structured screen back to {{applications_source}}, and proposes interview slots on {{hiring_manager_calendar}} for strong matches. Never advances or rejects a candidate — every decision stays with the hiring manager.
安装前可以查看交给 AI 代理的指令内容。
Read {{role_rubric}} in full before looking at any application: the required criteria, the preferred criteria, what strong evidence looks like for each, and how they're weighed into an overall score. This is the only standard you score against — not general resume-reading judgment.
Check {{applications_source}} for applications that don't already carry a screen (no screen note, no "screened" tag/status). Pick one and treat it as a fully independent pass — nothing from another candidate's screen carries over, and nothing here should carry into the next one.
Read the entire resume and any attached materials for that one candidate — not a skim. Note concrete, quotable claims (roles, scope, outcomes, tools) you'll need for evidence in Step 4.
For every rubric criterion:
| Judgment | Meaning |
|---|---|
| Met | Clear evidence in the resume that the criterion is satisfied |
| Partially met | Related but incomplete or ambiguous evidence |
| Not met | No evidence, or evidence contradicts the criterion |
For each criterion, pull the exact quote from the resume that supports the judgment — no judgment without a quote. Roll the per-criterion judgments up into one overall score per the rubric's weighting, and separate required criteria (a "not met" here caps the overall score) from preferred ones (a tiebreaker, not a gate).
Write back to {{applications_source}} a screen containing: overall score, strengths (criteria met, with quotes), gaps (criteria not/partially met, with quotes or the absence noted), and the rubric weighting used. This is the artifact the hiring manager reviews — it must stand on its own without you in the room.
If the overall score clears the rubric's "strong match" bar, check {{hiring_manager_calendar}} for open slots in the next few business days and propose 2–3 candidate times as tentative, unconfirmed holds — never a sent invite, never a booked event. Below the bar, write the screen and stop; no slots are proposed.
Tag or annotate the application in {{applications_source}} as screened so the next sweep skips it. This is what makes the sweep idempotent across runs with no shared memory.
</workflow> <guardrails> - **Never auto-reject or auto-advance.** The agent has no "reject" or "advance" action. It writes a screen and, for strong matches, proposes slots — the hiring manager makes every decision. - **Rubric-only scoring.** Score strictly against {{role_rubric}}. When the rubric changes, screen against the new version — never fall back to general judgment when the rubric is silent on something. - **Evidence or it didn't happen.** Every strength, gap, and score traces to a quote from the actual application. No unsupported judgments. - **Isolation per candidate.** Each application is its own independent pass, seeded with just that resume and the rubric. Only the screen and any proposed slots leave the sandbox. - **Proposed, never confirmed.** Interview slots are tentative holds on {{hiring_manager_calendar}}, offered for a person to confirm — the agent never sends the invite. - **Scoped secrets.** Access to {{applications_source}} and {{hiring_manager_calendar}} is brokered through connectors; no raw credential is ever pasted into chat. </guardrails> </skill>还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问，或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
Review Slack access requests against Okta role and team information, then prepare narrowly scoped GitHub or AWS IAM permissions. Apply changes only after an eligible person's approval and record the grant, reasoning, approver, and timestamp.
中文简介正在准备中。当前显示英语简介。
Research companies and people before outreach or meetings. Combines business profiles, recent news, hiring signals, and key contacts into a sourced brief with talking points and suggested questions, adding relationship history when CRM data is connected.
中文简介正在准备中。当前显示英语简介。
Review daily Google Ads and Meta Ads results for budget pacing, declining performance, and unusual activity. Get prioritized findings with supporting numbers and concrete recommendations for a marketing team to assess and carry out.
中文简介正在准备中。当前显示英语简介。
Automate website tasks such as navigating pages, filling forms, clicking buttons, taking screenshots, and extracting data. The skill also supports web app testing, with specialized guides for Electron desktop apps, Slack, and cloud browsers.
中文简介正在准备中。当前显示英语简介。
Process emailed vendor invoices by extracting details, comparing them with purchase orders and invoice history, and recording duplicates, overcharges, or missing orders. Summarize results in Slack for human review; payment approval and execution remain with people.
中文简介正在准备中。当前显示英语简介。
Guide changes to Kortix billing code, admin controls, and pricing screens. Explain how plans, trials, enterprise contracts, and access exceptions should work, where pricing is defined, and which audited routes must handle account changes.
中文简介正在准备中。当前显示英语简介。