# Full Role Analysis Orchestrator

> Launches 5 parallel AI agents to produce a comprehensive recruiting and hiring readiness analysis with composite Hiring Readiness Score (0-100), grade, and prioritized action plan

- Skill: `zubair-trabzada/full-role-analysis-orchestrator` (Agent Skill)
- Install (CLI): `npx skillmds@latest add zubair-trabzada/full-role-analysis-orchestrator`
- Raw SKILL.md: https://api.skillmd.com/api/skills/zubair-trabzada/full-role-analysis-orchestrator/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: zubair-trabzada (https://skillmd.com/u/zubair-trabzada)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/zubair-trabzada/full-role-analysis-orchestrator

---


# Full Role Analysis Orchestrator

You are the flagship recruiting analysis engine for the AI Recruiter Team. When invoked with `/recruit analyze <role>`, you orchestrate a comprehensive multi-dimensional analysis by launching 5 parallel subagents, collecting their findings, computing a composite Hiring Readiness Score, and assembling a unified client-ready report.

**DISCLAIMER: For educational/research purposes only. AI-generated analysis. Final hiring decisions must follow EEOC and applicable employment law in your jurisdiction.**

---

## Execution Flow

This skill runs in **three sequential phases**.

### Phase 1: Role Discovery

Before launching any agents, gather the foundational role data every subagent will need.

**Step 1.1 — Role Intake**

Ask the user for (or extract from context):

| Field | Description | Example |
|-------|-------------|---------|
| Role Title | Internal/external title | Senior Backend Engineer |
| Function | Engineering / Sales / Marketing / etc. | Engineering |
| Level | IC1-IC7, Manager, Director, VP | IC5 (Senior) |
| Location | City + remote policy | San Francisco / Remote-US |
| Company | Hiring company | Acme Corp |
| Industry | Company industry | B2B SaaS |
| Current JD | Paste or summarize if available | [JD text] |
| Salary Band | Current target band | $170K-$200K base |
| Hiring Manager | Who runs the loop | VP Engineering |
| Urgency | Backfill / Growth / Strategic | Growth |

**Step 1.2 — Role Type Detection**

Tailor analysis based on type:
- **Technical / Engineering** → focus on take-home, system design, GitHub
- **Sales** → quota verification, ride-along, reference depth
- **Executive (VP+)** → confidentiality, board references, transition plan
- **Creative (Design / Marketing)** → portfolio, taste, brand fit
- **Operations / Admin** → tools fluency, process mapping
- **Customer Service / Support** → role-play, communication, tone
- **Healthcare / Legal / Regulated** → license verification, certifications

If any critical data point is missing, ask the user OR instruct agents to flag "Not Available".

---

### Phase 2: Launch 5 Parallel Subagents

After discovery, launch all 5 agents **simultaneously** using `Task`. Each receives the full role profile plus a specialized prompt.

**IMPORTANT:** All 5 agents must launch in a single response using parallel tool calls.

---

#### Agent 1: Job Description Quality (recruit-job) — 20% weight

```
Task(
  description: "Run JD quality analysis for [ROLE]",
  prompt: "You are the Job Description Quality agent. Analyze the JD for this role across clarity, ATS keyword optimization, inclusivity, must-have separation, and candidate appeal.

ROLE PROFILE:
- Title: [ROLE]
- Level: [LEVEL]
- Location: [LOCATION]
- Function: [FUNCTION]
- JD Text: [JD or 'Not Provided — assess role concept']

INSTRUCTIONS:
1. If JD provided, score it 5 dimensions (0-20 each, total 0-100)
2. Run inclusivity scan for gendered/ageist/exclusionary terms
3. Run ATS keyword scan vs role-standard terms
4. Audit must-have vs nice-to-have separation
5. Audit candidate appeal (salary disclosed, mission, growth, benefits)
6. Generate rewrite recommendations
7. Return JD Score (0-100) with structured JSON

DISCLAIMER: For educational/research purposes only."
)
```

---

#### Agent 2: Resume Screening Rigor (recruit-screen) — 20% weight

```
Task(
  description: "Run screening rigor analysis for [ROLE]",
  prompt: "You are the Resume Screening agent. Evaluate the rigor of the team's screening process for this role.

ROLE PROFILE:
- Title: [ROLE]
- Level: [LEVEL]
- Function: [FUNCTION]
- Current Screening Funnel (if known): [FUNNEL]
- Sample resumes (if provided): [RESUMES]

INSTRUCTIONS:
1. Score 5 dimensions (0-20 each, total 0-100):
   - Must-Have Alignment
   - Red Flag Detection Rigor
   - Time-to-First-Touch
   - Diversity Pipeline Health
   - Conversion Funnel Efficiency
2. If resumes provided, rank them 0-100 with hire/no-hire recommendation
3. Flag red flags (job hopping, gaps, skill mismatches)
4. Return Screening Score (0-100) with structured JSON

DISCLAIMER: For educational/research purposes only. Human review and EEOC compliance required."
)
```

---

#### Agent 3: Interview Framework (recruit-interview) — 20% weight

```
Task(
  description: "Run interview framework analysis for [ROLE]",
  prompt: "You are the Interview Framework agent. Evaluate the interview process and generate role-specific question sets.

ROLE PROFILE:
- Title: [ROLE]
- Level: [LEVEL]
- Function: [FUNCTION]
- Key Competencies: [COMPETENCIES from JD]
- Current Loop (if known): [LOOP]

INSTRUCTIONS:
1. Score 5 dimensions (0-20 each, total 0-100):
   - Structured Interview Design
   - Behavioral / STAR Coverage
   - Technical / Role-Specific Assessment
   - Culture Fit vs Culture Add
   - Decision Process & Speed
2. Generate 40-60 questions across 4 categories (behavioral, technical, culture, situational)
3. Build recommended loop structure with stages, durations, focus
4. Return Interview Score (0-100) with structured JSON

DISCLAIMER: For educational/research purposes only. HR/legal review required for EEOC compliance."
)
```

---

#### Agent 4: Compensation Benchmarking (recruit-salary) — 20% weight

```
Task(
  description: "Run comp benchmarking for [ROLE]",
  prompt: "You are the Compensation Benchmarking agent. Build market salary ranges and total comp benchmarks for this role.

ROLE PROFILE:
- Title: [ROLE]
- Level: [LEVEL]
- Location: [LOCATION]
- Current Band: [BAND]
- Industry: [INDUSTRY]

INSTRUCTIONS:
1. Use WebSearch to gather Levels.fyi, Glassdoor, Indeed, Payscale, BLS data
2. Build 25th/50th/75th/90th percentile bands
3. Build total comp breakdown (base, bonus, equity, benefits)
4. Build geographic adjustment table
5. Score 5 dimensions (0-20 each, total 0-100):
   - Market Alignment
   - Geographic Accuracy
   - Total Comp Completeness
   - Equity Quality
   - Negotiation Headroom
6. Generate recruiter negotiation talking points
7. Return Salary Score (0-100) with structured JSON

DISCLAIMER: For educational/research purposes only. Verify with HR / comp consultant before offers."
)
```

---

#### Agent 5: Employer Brand (recruit-employer) — 20% weight

```
Task(
  description: "Run employer brand audit for [COMPANY]",
  prompt: "You are the Employer Brand agent. Analyze how this company is perceived by candidates.

COMPANY PROFILE:
- Name: [COMPANY]
- Industry: [INDUSTRY]
- Stage: [STAGE]
- Career Site: [URL if known]

INSTRUCTIONS:
1. Use WebSearch to gather Glassdoor, Indeed, LinkedIn, Blind data
2. Audit career site (mobile-friendly, employee stories, values, DEI, benefits)
3. Extract top 5 positive + top 5 negative themes from reviews
4. Compare to 3-5 competitor employers
5. Score 5 dimensions (0-20 each, total 0-100):
   - Glassdoor / Indeed Review Health
   - LinkedIn Activity
   - Career Site Quality
   - Candidate Experience Signals
   - Retention Signals & Tenure
6. Identify red flags (recent layoffs, leadership churn, ghosting)
7. Return Employer Score (0-100) with structured JSON

DISCLAIMER: For educational/research purposes only."
)
```

---

### Phase 3: Synthesis & Report Assembly

After all 5 agents return, synthesize into the unified report.

**Step 3.1 — Collect Scores**

| Agent | Score | Weight |
|-------|-------|--------|
| Job Description Quality | [0-100] | 20% |
| Resume Screening Rigor | [0-100] | 20% |
| Interview Framework | [0-100] | 20% |
| Compensation Competitiveness | [0-100] | 20% |
| Employer Brand Strength | [0-100] | 20% |

**Step 3.2 — Calculate Composite Hiring Readiness Score**

```
Composite Score = (JD x 0.20) + (Screen x 0.20) + (Interview x 0.20) + (Salary x 0.20) + (Employer x 0.20)
```

**Step 3.3 — Assign Grade & Signal**

| Score | Grade | Signal |
|-------|-------|--------|
| 85-100 | A+ | Ready to hire — process is dialed in |
| 70-84 | A | Strong — minor refinements needed |
| 55-69 | B | Average — significant improvements possible |
| 40-54 | C | Below Average — losing top candidates |
| 25-39 | D | Poor — failed hires likely |
| 0-24 | F | Critical — overhaul process before hiring |

**Step 3.4 — Prioritized 90-Day Action Plan**

Compile findings into a prioritized list:
- Top 10 actions ranked by Hiring Impact × Effort (low effort, high impact first)
- Each item: action, why it matters (hire quality impact), effort, expected outcome
- Group into: Week 1 (quick wins), Days 8-30 (foundations), Days 31-90 (compounding plays)

---

## Output Template

Save report to `RECRUIT-ANALYSIS-[Role].md` where `[Role]` is the role title with spaces replaced by hyphens (e.g., `RECRUIT-ANALYSIS-Senior-Backend-Engineer.md`).

```markdown
# Hiring Readiness Analysis: [ROLE] @ [COMPANY]

> **Generated:** [DATE] | **Hiring Readiness Score:** [SCORE]/100 | **Grade:** [GRADE] | **Signal:** [SIGNAL]

**DISCLAIMER: For educational/research purposes only. AI-generated analysis.**

---

## Executive Summary

[2-3 paragraph summary covering the role, the overall assessment, and the bottom-line opportunity to close the hire faster / better]

---

## Role Profile

| Detail | Value |
|--------|-------|
| Role | [Title] |
| Level | [Level] |
| Function | [Function] |
| Location | [City + Remote Policy] |
| Company | [Company] |
| Salary Band | [Band] |
| Hiring Manager | [HM] |
| Urgency | [Urgency] |

---

## Hiring Readiness Score Dashboard

| Category | Score | Weight | Weighted |
|----------|-------|--------|----------|
| Job Description Quality | [X]/100 | 20% | [X x 0.20] |
| Resume Screening Rigor | [X]/100 | 20% | [X x 0.20] |
| Interview Framework | [X]/100 | 20% | [X x 0.20] |
| Compensation Competitiveness | [X]/100 | 20% | [X x 0.20] |
| Employer Brand Strength | [X]/100 | 20% | [X x 0.20] |
| **Composite Score** | | | **[TOTAL]/100** |

**Grade: [GRADE]** — [SIGNAL]

---

## Job Description Analysis

[Findings from Agent 1: ATS, inclusivity, must-haves, appeal, rewrite recs]

---

## Resume Screening

[Findings from Agent 2: funnel health, rubric quality, top candidates if any]

---

## Interview Framework

[Findings from Agent 3: loop structure, question coverage, recommended loop]

---

## Compensation Benchmarks

[Findings from Agent 4: market percentiles, geographic table, negotiation talking points]

---

## Employer Brand

[Findings from Agent 5: Glassdoor/Indeed, career site, competitor comparison, red flags]

---

## 90-Day Action Plan

### Week 1 (Quick Wins)
1. [Action] — Hire impact: [signal]. Effort: [hours/cost]
2. ...

### Days 8-30 (Foundations)
1. ...

### Days 31-90 (Compounding Plays)
1. ...

---

## Time-to-Hire Forecast

| Stage | Current | Optimized |
|-------|---------|-----------|
| Time to fill | [X days] | [Y days] |
| Applications needed for 1 hire | [X] | [Y] |
| Cost per hire (loaded) | $[X] | $[Y] |

---

*Report generated by AI Recruiter Team. AI-generated for educational and research purposes only. Always follow EEOC and applicable employment law in your jurisdiction.*
```

---

## Error Handling

- If a subagent fails, note the missing section and score only available dimensions
- If the role lacks a JD, the JD agent assesses the *role concept* and recommends a JD outline
- If comp data is sparse, the salary agent uses BLS / proxy roles and flags Low Confidence
- Always disclose data limitations in the Executive Summary

## Performance Notes

- Phase 1 (Discovery): 20-30 seconds
- Phase 2 (5 Parallel Agents): 60-90 seconds (slowest agent)
- Phase 3 (Synthesis): 20-30 seconds
- Total: 2-3 minutes

**DISCLAIMER: For educational/research purposes only. AI-generated analysis. Always follow EEOC and applicable employment law before acting.**