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name: debrief description: Process an interview transcript and extract actionable intel argument-hint: <company>

Interview Debrief Skill

Process an interview transcript and extract actionable intel — what went well, what to sharpen, what you learned about the role and process, and what needs to happen next.

Trigger

• /debrief [company]
• "debrief [company]"
• "process the [company] transcript"
• Share a Google Docs transcript link after an interview

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Instructions

Step 1: Get the Transcript

If a Google Docs URL is provided, fetch it via Drive API:

import sys, io, json
sys.stdout.reconfigure(encoding='utf-8')
from googleapiclient.discovery import build
from google.oauth2.credentials import Credentials
from googleapiclient.http import MediaIoBaseDownload

token_path = '<YOUR_TOKEN_PATH>'  # e.g. ~/job-search/token.json
with open(token_path) as f:
    token_data = json.load(f)

creds = Credentials.from_authorized_user_info(token_data)
service = build('drive', 'v3', credentials=creds)

# Extract doc ID from URL: the part between /d/ and /edit
file_id = '<DOC_ID>'

request = service.files().export_media(fileId=file_id, mimeType='text/plain')
fh = io.BytesIO()
downloader = MediaIoBaseDownload(fh, request)
done = False
while not done:
    status, done = downloader.next_chunk()
fh.seek(0)
print(fh.read().decode('utf-8-sig'))

If no URL provided, ask for it or check if a transcript is already in the conversation.

Step 2: Analyze the Transcript

Extract and organize:

Call Summary

• Who was on the call (name, title, tenure)
• Interview type (phone screen, hiring manager, panel, final round)
• Estimated duration

What Went Well

• Questions where answers landed strongly
• Moments of rapport or genuine connection
• Stories or experiences that resonated

Key Intel

• Interview process details (stages, who's involved, timeline)
• Candidate pool info (how many, where you stand)
• Role details revealed during the call (scope, team size, reporting structure, budget)
• Culture signals — what the team is like, how they work
• Red flags or concerns surfaced (be honest)
• Hiring urgency and timeline

What the Next Interviewer Will Probe

• Explicit hints about what the next round will focus on
• Inferred probing areas based on what was and wasn't covered

Action Items

• Follow-up deadlines (when to expect to hear back)
• Items to prepare for next round
• People to research (next interviewers)
• Anything you committed to sending or doing

Step 3: Present the Debrief

Present a clean summary using the structure above. Use bullets, not paragraphs. Be direct about both positives and areas to sharpen.

Step 4: Update Systems

After presenting the debrief, update all of the following:

a. Update job tracker:

python sheets_tracker.py status "[Company]" "Interviewing" "[Stage completed]"
python sheets_tracker.py action "[Company]" "[Next action + deadline]"

b. Save interview memory: Write or update a memory file (e.g. [company]-interview-status.md) with structured intel from the debrief. Store in your Claude Code memory directory.

c. Update todos: Add follow-up items (e.g. "Follow up with [recruiter] if no word by [date]").

d. Update weekly plan: Add a mid-week adjustment note to weekly-plan.md if this changes the week's priorities.

Step 5: Prep Recommendations

Based on what was learned, recommend:

• Should the prep doc for the next round be updated? What specifically needs to change?
• Are there new stories that should be mapped to likely next-round questions?
• Any new research needed (interviewer profiles, product deep-dive, competitive landscape)?

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Constraints

• Use Drive API export (not Docs API) for fetching transcripts — Docs API requires separate enablement
• Be honest about weak moments in the transcript — real feedback only
• Don't fabricate intel that isn't in the transcript — if something is ambiguous, flag it
• Update ALL systems (tracker, memory, todos, weekly plan) — don't skip steps
• Fact-check any new company intel (revenue, headcount, product details, org changes) extracted from the transcript against public sources before writing to memory. Interviewers sometimes share approximate or outdated numbers. Cross-reference with the company website, press releases, or revenue databases before saving as fact. If unverifiable, label as "per [interviewer name], unverified."

Anti-patterns

• Don't just summarize the transcript — extract actionable intel
• Don't skip system updates — that's half the value of running this
• Don't sugarcoat weak answers — flag them so they can be sharpened for next round
• Don't present a wall of text — use structured format with clear headers
• Don't write unverified interviewer claims to memory as fact — always cross-reference first