Purpose

Step 1: Get the Transcript

Option A — By candidate name (recommended):

User: "review my interview with Sarah"
→ AI searches Fathom for meetings with "Sarah" in title
→ If multiple matches, shows list to pick from
→ Fetches transcript automatically

Option B — Manual fetch:

User: "fetch meetings from fathom"
→ Lists recent meetings
→ User selects one
→ Then says "review my interview"

Fathom search by name:

# Fetch recent meetings, filter by title containing candidate name
curl -s --request GET \
  --url 'https://api.fathom.ai/external/v1/meetings?include_summary=true&created_after={30_DAYS_AGO}' \
  --header 'X-Api-Key: {FATHOM_API_KEY}' \
  | jq '.meetings[] | select(.title | ascii_downcase | contains("{candidate_name}"))'

Link to prerequisite skill: fathom-fetch-meetings fetches meetings filtered by attendee domain, returns transcripts.

Step 2: Get the Job Description

Once transcript is available, fetch the JD from Workable:

Ask: "What role was this interview for?"

Then run:

python3 03-skills/workable-master/scripts/workable_client.py --fetch-jd "[role title]"

If multiple matches, show options and ask for shortcode:

python3 03-skills/workable-master/scripts/workable_client.py --fetch-jd-code [shortcode]

Link to skill: workable-master handles all Workable operations.

Step 3: Interview Stage (REQUIRED)

Ask: "What type of interview was this?"

Triggers can specify type directly:

"hr round review with Sarah"
"technical round review with John"
"final round review with Ahmed"

Evaluation Frameworks

Each interview type has its own evaluation criteria. The framework ensures you're assessing what matters for that stage.

🟢 HR Round Framework

Focus: Cultural fit, soft skills, resume-personality alignment

Key Questions for HR Round:

• "What kind of work environment brings out your best?"
• "Tell me about a time you disagreed with a colleague"
• "What attracted you to this role specifically?"
• "Walk me through your career journey"
• "How do you handle feedback?"

Red Flags to Probe:

• Badmouthing previous employers
• Unexplained gaps or frequent job changes
• Vague answers about leaving previous roles
• Mismatches between resume claims and conversation

🔵 Technical/Case Study Round Framework

Focus: Deep skill assessment, problem-solving ability, technical communication

Key Questions for Technical Round:

• "Walk me through how you'd approach [specific problem]"
• "What's the trade-off between X and Y approach?"
• "Tell me about a technical challenge you solved recently"
• "How would you debug [scenario]?"
• "Explain [concept] to a non-technical stakeholder"

Case Study Best Practices:

• Give candidate time to think (don't rush)
• Probe reasoning, not just final answer
• Ask "what would you do differently?"
• Test both breadth and depth

🟣 Final Round Framework

Focus: Career aspirations, team fit, leadership potential, mutual evaluation

Key Questions for Final Round:

• "Where do you see yourself in 3 years?"
• "What would make you turn down this offer?"
• "How do you like to be managed?"
• "Tell me about a time you influenced without authority"
• "What questions do you have for me about the team?"

Final Round Focus:

• This is about mutual fit, not skills (already validated)
• Be honest about challenges and expectations
• Assess culture add, not just culture fit
• Leave time for their deep questions

General Evaluation Framework

For interviews not fitting the above categories, use the general 6-dimension framework:

Analyze across 6 dimensions, then calculate overall score out of 5:

1. Role Alignment

Do questions match the JD?

• Questions probe JD requirements
• Technical depth appropriate for role
• Scenarios match actual job challenges

2. Question Quality

Open-ended, behavioral, good follow-ups?

• Open vs closed questions ratio
• Behavioral questions (STAR-friendly)
• Follow-up probes ("Tell me more", "What happened next")
• Flag: Leading questions ("You're good with deadlines, right?")

3. Bias Signals

Fair and consistent?

Red flags:

• Assumptions based on background
• Interrupting candidate frequently
• Affinity bias ("Oh, you went to my school too!")
• Questions about protected characteristics

4. Structure & Flow

Clear progression?

• Opening: Set expectations, build rapport
• Middle: Logical topic flow
• Closing: Time for candidate questions, next steps explained
• Time management: Not rushed at end

5. Talk Ratio

Candidate should talk 70-80%

• Count interviewer vs candidate speaking time
• Flag if interviewer talks >40%
• Note long interviewer monologues

6. Candidate Experience

Would they recommend interviewing here?

• Warmth and rapport
• Active listening
• Selling the role appropriately
• Clear communication about process

Scoring

Overall Score: X/5

Calculate: Average across 6 dimensions, round to nearest 0.5

Output Format

Uses the standard review template from workable-master SKILL.md — plain-text format with:

1. Expert Panel Assessment — 3 experts, bullet points (Strong/Concern), score per expert
2. Barrel Scoring — 5 dimensions, classification
3. Candidate Scorecard — 7 dimensions scored /10, overall average
4. Interview Quality — short assessment of whether the questions were good, time was managed well, and gaps were probed
5. Recommendation — clear verdict with next steps

The Interview Quality section is unique to this skill (not in candidate-compare). It evaluates the interviewer's performance:

━━━ INTERVIEW QUALITY ━━━

• Questions matched JD: [Yes/Partially/No] — [brief note]
• Failure probing: [Strong/Adequate/Weak] — [brief note]
• Case question quality: [Strong/Adequate/Weak] — [brief note]
• Time management: [Good/Okay/Poor] — [brief note]

This gives the interviewer quick feedback on their own performance alongside the candidate assessment.

Best Practices Reference

Strong Questions

Behavioral (STAR):

• "Tell me about a time when..."
• "Describe a situation where you had to..."

Situational:

• "How would you handle..."
• "What would you do if..."

Follow-ups:

• "What was the result?"
• "What would you do differently?"

Avoid These

Ideal Structure

[0-5 min]   Welcome & rapport, explain format
[5-35 min]  Core questions with follow-ups
[35-45 min] Candidate questions
[45-50 min] Close — next steps, timeline, thank them

Tone

Be a coach, not a critic:

• "Here's an opportunity..." not "You did this wrong"
• "Consider trying..." not "You should have..."
• Always quote the transcript
• Provide concrete alternatives

Triggers

Post-Evaluation: Expert Assessment + Push to Workable

After generating the interview coaching feedback, always produce the candidate review using the standard template from workable-master:

Automatic Steps:

1. Select 3 experts based on the role (see workable-master SKILL.md for role→expert mapping)
2. Run expert assessments — each expert evaluates the candidate from their lens
3. Run barrel scoring — 5 dimensions, weighted total, classification
4. Compile strengths, gaps, and follow-up questions
5. Determine grade — map expert consensus + barrel score to Yes/Maybe/No

Then offer to push:

💡 Push this review to Sarah's Workable profile? (Grade: Yes ✅)

When user confirms:

python3 03-skills/workable-master/scripts/workable_client.py \
  --candidate "{candidate_name}" \
  --review --grade {0|1|2} \
  --assessment "{full_review_text}"

If user declines: Still save the review locally but don't push.

Integration

Connected skills:

Full Workflow (end-to-end):

Manager: "review my interview with Sarah"
→ AI searches Fathom for "Sarah" in meeting titles
→ AI: "What role was this interview for?"
Manager: "Product Manager"
→ AI fetches JD from Workable (workable-master)
→ AI selects 3 expert panel based on role
→ Expert assessments + barrel scoring
→ AI: "Push this review to Sarah's Workable profile? (Grade: Yes ✅)"
Manager: "yes"
→ Review posted to Workable with grade

Alternative (step-by-step):

Manager: "fetch meetings from fathom"
→ Lists recent meetings
Manager: picks meeting
Manager: "review my interview"
→ continues as above

No manual transcript or JD pasting required — everything pulls from your systems.

Version: 2.0 | Updated: 2026-02-16

Changelog:

• v1.6: Workable push integration — after evaluation, offers to push assessment to candidate's Workable profile via workable-push-assessment
• v1.5: Stage-specific evaluation — added HR Round, Technical Round, and Final Round frameworks with tailored dimensions and weights
• v1.4: Added candidate name search — "review my interview with [name]" fetches transcript automatically
• v1.3: Integrated with workable-fetch-jd for automatic JD fetching
• v1.2: Added triggers "assess my interview", "how's the candidate"
• v1.1: Integrated with fathom-fetch-meetings, simplified to 5-point scale, removed letter grades
• v1.0: Initial creation