What makes 招聘推进助手 / Recruiting Follow-up Copilot notable?

1.7K downloads. Trust evidence available

How should 招聘推进助手 / Recruiting Follow-up Copilot be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

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agentCLAWHUBUnverified

招聘推进助手 / Recruiting Follow-up Copilot

帮招聘 HR 快速汇总面试反馈、生成候选人推进话术、整理 Offer 材料并回写跟进记录。 / Help recruiting teams debrief interviews, draft candidate follow-ups, prepare offer materials, and update tra... Skill: 招聘推进助手 / Recruiting Follow-up Copilot Owner: ashley-aihr Summary: 帮招聘 HR 快速汇总面试反馈、生成候选人推进话术、整理 Offer 材料并回写跟进记录。 / Help recruiting teams debrief interviews, draft candidate follow-ups, prepare offer materials, and update tra... Tags: china:0.3.0, hr:0.3.0, latest:0.3.0, recruiting:0.3.0 Version history: v0.3.0 | 2026-05-18T19:31:45.818Z | user 新增第二个可交付场景：JD+简历初筛，支持初筛评估、候选人沟通稿和进展记录文件输出。 / Add a second production

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.7k

Updated

Oct 10, 2026

Version

0.3.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.7K downloads reported by the source. Last updated 10/10/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.7K downloadsadoption · observed Oct 10, 2026
Latest release: 0.3.0release · observed May 18, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s1709qwt8f7axz6nyace1xk5s5840gbe:cn-recruiting-workflow

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-ashley-aihr-cn-recruiting-workflow/snapshot"

Documentation

CLAWHUB

109,595 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: cn-recruiting-workflow
description: 帮招聘 HR 快速汇总面试反馈、生成候选人推进话术、整理 Offer 材料并回写跟进记录。 / Help recruiting teams debrief interviews, draft candidate follow-ups, prepare offer materials, and update trackers.
version: 0.3.0
metadata:
  openclaw:
    homepage: https://github.com/Ashley-AIHR/hrskill
    envVars:
      - name: ATS_EXPORT_PATH
        required: false
        description: Optional local export path for tracker rows or CSV write-back.
---

# 招聘推进助手 / Recruiting Follow-up Copilot

当用户在处理招聘推进、面试反馈汇总、候选人沟通或 Offer 前置材料时使用这个 skill。它更像一个会帮 HR 往前推流程的小助手，而不是一个只会解释概念的 HR 机器人。 / Use this skill when the user needs help moving recruiting work forward: interview debriefs, candidate follow-ups, and offer prep.

这个 skill 设计了 5 个招聘动作，目前已经有 2 个能真正落地交付文件的场景： / This skill is optimized for 5 recruiting actions, and currently has 2 production-ready scenarios:

1. `互联网招聘里的面试反馈汇总与推进`
2. `JD + 简历初筛与推进建议`

这个场景覆盖了互联网招聘里最常见的一类 HR 痛点： / That scenario covers a very common recruiting pain point:

1. a recruiter or HRBP receives messy interviewer notes from Feishu, WeCom, email, or forms
2. the hiring manager wants a short hiring recommendation fast
3. HR needs a candidate-facing follow-up message
4. HR needs a tracker update that can be pasted into ATS or a spreadsheet
5. HR often still needs a downloadable debrief memo for internal circulation

当前附带了可直接使用的文件： / This skill includes bundled files for these scenarios:

1. [references/real-user-scenario.md](references/real-user-scenario.md)
2. [assets/interview-packet-input.sample.json](assets/interview-packet-input.sample.json)
3. [assets/resume-screening-input.sample.json](assets/resume-screening-input.sample.json)
4. [scripts/generate_interview_packet.js](scripts/generate_interview_packet.js)
5. [scripts/generate_screening_packet.js](scripts/generate_screening_packet.js)

支持的动作有： / The supported actions are:

1. `score_candidate`
2. `summarize_interview_feedback`
3. `create_offer_approval_pack`
4. `generate_candidate_message`
5. `update_candidate_tracker`

## 输出标准 / Outcome Standard

处理任意招聘工作流时，始终产出以下结构： / When handling any recruiting workflow, always produce these sections:

```text
normalized_data
decision_summary
missing_information
next_action
message_draft
record_update
compliance_warning_if_any
```

规则： / Rules:

1. `normalized_data` must be structured and easy to map into ATS, Feishu Bitable, DingTalk approval forms, Notion, Google Sheets, or CSV.
2. `decision_summary` must make a decision or recommendation, not just restate the inputs.
3. `missing_information` must name the exact fields that block a confident HR action.
4. `next_action` must be something an HR operator can actually do today.
5. `message_draft` should be directly reusable in WeCom, Feishu, email, or a candidate chat.
6. `record_update` should be concise enough to write back into one row or one timeline entry.
7. `compliance_warning_if_any` should only appear when there is a concrete legal or privacy concern.

## 工作流路由 / Workflow Routing

#

_meta.json

{
  "ownerId": "kn7cfgqtq1167ctj7rfp8cg3yn840js9",
  "slug": "cn-recruiting-workflow",
  "version": "0.3.0",
  "publishedAt": 1779132705818
}

references/real-user-scenario.md

# Real User Scenario

## Recommended first production scenario

Use this skill first for:

`互联网公司社招场景下，HR 汇总多位面试官反馈并产出推进材料`

This is a realistic recruiting workflow in China because:

1. feedback is often fragmented across Feishu, WeCom, forms, and verbal notes
2. the hiring manager needs a quick go or no-go recommendation
3. HR still needs documentation that can be forwarded, archived, and pasted back into ATS

## Typical trigger

An HRBP or recruiter says something like:

1. "这是产品经理候选人的三轮面试反馈，帮我汇总成一版给老板看的纪要，再给候选人一条推进消息。"
2. "面试官反馈很散，你帮我判断要不要进终面，并给我一行 tracker 更新。"
3. "把这些反馈整理成结论，顺手给我生成一个 Word 版纪要。"

## Typical inputs

1. Candidate basic profile
2. Target JD or a short role summary
3. Interview feedback from 2 to 5 interviewers
4. Optional salary expectation or notice-period info

## Minimum useful outputs

1. normalized feedback summary
2. recommendation with confidence level
3. candidate-facing follow-up draft
4. tracker update row
5. internal interview debrief memo

## Recommended second production scenario

Use this skill next for:

`JD + 简历初筛，输出是否推进、面试重点、候选人消息和跟进记录`

This is another strong recruiting use case because:

1. almost every recruiting team does resume screening every day
2. the input is stable enough to structure
3. the output can directly move the process forward

Typical trigger:

1. "这是岗位 JD 和候选人简历，帮我判断要不要推进。"
2. "给我一版初筛结论，再补 3 个面试重点。"
3. "顺手生成候选人沟通话术和一行进展记录。"

Minimum useful outputs:

1. match score and match rationale
2. risk flags
3. interview focus
4. candidate-facing follow-up draft
5. progress-record update

## Internet-company flavor

This scenario is especially common in internet hiring because:

1. recruiting speed matters, so HR often cannot wait for perfectly formatted feedback
2. interviewers frequently leave short comments such as "还行", "项目深度一般", "推进但要补看 owner 意识"
3. HR has to translate vague feedback into a structured decision for the hiring manager

## Decision policy

When summarizing feedback for this scenario:

1. distinguish hard blockers from soft concerns
2. call out disagreement between interviewers explicitly
3. do not overstate certainty when feedback is thin
4. keep the candidate message aligned with the actual next action
5. keep the memo concise enough to circulate internally

references/recruiting-fields.md

# Recruiting Fields Reference

Use this reference when the user needs stronger normalization for recruiting documents and workflow records used in China.

## JD fields

```text
job_title
department
hiring_manager
location
employment_type
must_have_skills
preferred_skills
years_of_experience
education_requirement
industry_background
language_requirement
salary_range
urgency
interview_stages
```

## Resume fields

```text
candidate_name
current_title
years_of_experience
education
industry_experience
core_skills
company_history
project_highlights
management_scope
stability_signals
salary_expectation
availability
location
```

## Interview feedback fields

```text
interviewer_name
interview_round
capability_feedback
experience_feedback
motivation_feedback
communication_feedback
culture_fit_feedback
salary_risk
stability_risk
hire_recommendation
follow_up_questions
confidence_level
```

## Offer approval fields

```text
candidate_name
target_role
department
reporting_line
work_location
employment_entity
base_salary
bonus_scheme
trial_period
expected_onboard_date
budget_range
approver_chain
interview_summary
risk_notes
```

## Common source formats

1. JD: Word, PDF, Feishu doc, pasted text, or email body
2. Resume: PDF, DOCX, OCR text, recruiter notes, or ATS export
3. Interview feedback: form fields, Feishu/WeCom chat, email, call notes, or voice transcription
4. Approval pack inputs: spreadsheet rows, approval forms, salary bands, or hiring manager notes

## Recommended record-update shape

```text
candidate_name
target_role
stage
decision
risk_flags
owner
next_action
last_update_summary
```

skill-card.md

## Description:

帮招聘 HR 快速汇总面试反馈、生成候选人推进话术、整理 Offer 材料并回写跟进记录。 / Help recruiting teams debrief interviews, draft candidate follow-ups, prepare offer materials, and update trackers.

This skill is ready for commercial/non-commercial use.

## Publisher:

[ashley-aihr](https://clawhub.ai/user/ashley-aihr)

### License/Terms of Use:

MIT-0

## Use Case:

Recruiters, HRBPs, and hiring teams use this skill to turn messy interview feedback, JD and resume inputs, and offer-prep details into recruiting decisions, candidate messages, and tracker-ready records for China-focused hiring workflows.

### Deployment Geography for Use:

Global, with Mainland China recruiting context

## Known Risks and Mitigations:

Risk: Sensitive recruiting records may be written to disk in generated DOCX, CSV, and JSON files.

Mitigation: Use the skill only with recruiting data the operator is authorized to process, choose a controlled output directory, and redact salary or interview details when they are not needed.

Risk: Generated CSV files may preserve spreadsheet formulas from candidate data.

Mitigation: Sanitize CSV output or add formula-injection hardening before opening generated CSV files in spreadsheet tools.

Risk: Recruiting recommendations may affect hiring decisions when inputs are incomplete or inconsistent.

Mitigation: Review the recommendation, missing-information fields, and candidate-facing drafts before using them in a live recruiting workflow.

## Reference(s):

- [Real User Scenario](references/real-user-scenario.md)
- [Recruiting Fields Reference](references/recruiting-fields.md)
- [ClawHub skill page](https://clawhub.ai/ashley-aihr/skills/cn-recruiting-workflow)
- [Project homepage](https://github.com/Ashley-AIHR/hrskill)

## Skill Output:

**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance, files]

**Output Format:** [Structured Markdown or text, plus optional DOCX, CSV, and JSON files generated from local JSON inputs.]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [The packaged scripts can produce interview debrief memos, candidate messages, tracker CSV rows, resume-screening notes, and JSON workflow summaries.]

## Skill Version(s):

0.3.0 (source: frontmatter and server release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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