Profile

Profile

Pre-flight

• [ ] Load active preferences (see memory skill):

``bash node scripts/db.js "SELECT category, key, value, confidence, source FROM preferences WHERE user_id = 1 AND status = 'active' ORDER BY category, key" ``

Phase 1: CV (online or PDF)

Ask user for CV (URL or PDF). Extract:

• [ ] Full name and title/profession
• [ ] Professional summary (elevator pitch)
• [ ] Work experience (company, role, period, achievements, team size, reporting line)
• [ ] Technical skills (tech stack, tools)
• [ ] Soft skills (leadership, communication, etc.)
• [ ] Certifications and courses
• [ ] Education (degrees, institutions)
• [ ] Languages and proficiency level
• [ ] Quantifiable achievements (metrics, impact)
• [ ] Notable projects
• [ ] Open source contributions

Save to users.data.profile as JSONB.

Phase 2: Questionnaire (CV gaps)

Questions in blocks of 4, multi-select where applicable. Each answer must have a weight: Must (non-negotiable), Strong (strong preference), Nice (would be a plus).

Block 1: Role and level

• [ ] Role type (IC, manager, mixed, architect, director/CTO)
• [ ] Target seniority (senior, staff, principal, manager, director, VP, C-level)
• [ ] Expected reporting line (CEO, CTO, VP Eng, other director)
• [ ] Technical involvement (% of time coding vs management)

Block 2: Work mode and geography

• [ ] Work mode (remote, hybrid, on-site)
• [ ] Current location and willingness to relocate
• [ ] Accepted timezones (Americas, Europe, Asia, global)
• [ ] Contract type (employee, contractor, B2B)

Block 3: Compensation

• [ ] Salary range (min, expected, currency)
• [ ] Equity expectations (% , realistic stage)
• [ ] Important benefits (health, education budget, equipment, etc.)
• [ ] Flexibility on must-haves (e.g: 100% remote absolute or accepts 1 quarterly trip)

Block 4: Team and autonomy

• [ ] Current vs desired team size
• [ ] Expected hiring/firing authority
• [ ] Budget authority (own budget decisions)
• [ ] Multiple squads / org scope

Block 5: Company type and stack

• [ ] Company size (startup, scale-up, corporate)
• [ ] Stage (pre-seed, seed, Series A-C, public)
• [ ] Preferred tech stack or open to others
• [ ] Product type (own product, internal platform, consulting)

Block 6: Industry and mission

• [ ] Preferred industries
• [ ] Industries to avoid (with nuance: absolute or accepts partial exposure?)
• [ ] Mission/values of interest (education, health, climate, fintech, dev tools, etc.)
• [ ] Graduated deal-breakers (crypto, gambling, research, freelance, junior)

Block 7: AI and culture

• [ ] AI focus (mandatory, preferred, indifferent)
• [ ] Type of AI role (strategy, adoption, platform, agents, RAG, evals)
• [ ] Work culture (async, sync, documentation-first, meetings)
• [ ] Expected impact in first 6 months

Block 8: Availability and language

• [ ] Availability (immediate, 2 weeks, 1 month)
• [ ] Current situation (employed active change, passive, unemployed)
• [ ] Working languages
• [ ] Travel (0%, occasional, up to 25%, indifferent)

Save to users.data.job_preferences as JSONB with weights.

Phase 3: Voice and style

3a: Automatic inference

• [ ] Open headless browser with persistent profile
• [ ] Read last 20-50 sent messages on LinkedIn (filter "You:")
• [ ] Read relevant sent emails in Gmail (to recruiters, HR, companies)
• [ ] Infer: tone, default language, writing characteristics, average length
• [ ] Extract 3-5 representative samples (1-2 recruiter, 2-3 personal)

3b: Confirmation with options

Ask the user:

• [ ] Tone (formal, casual-professional, casual, direct/no-nonsense)
• [ ] Default language (Spanish, English, depends on context)
• [ ] Preferred length (short 1-3 lines, medium 4-6, long 7+)
• [ ] Preferred greeting (Hi [name], Dear, no greeting, other)
• [ ] Preferred closing (Regards, Cheers, no closing, other)
• [ ] Use bullet lists in messages? (yes, no)
• [ ] Emojis in professional messages? (yes, no, only in personal)

3c: Validation

• [ ] Save inference + preferences to users.data.style_profile as JSONB
• [ ] Show 3 messages drafted with the style to the user for validation
• [ ] If user corrects → update style_profile

Phase 4: Platforms (output, not input)

• [ ] Consult PLATFORMS.md
• [ ] Cross-reference user profile vs role types/industries/seniority of each platform
• [ ] Assign Tier 1/2/3 to platforms based on fit
• [ ] Save to users.data.platforms as JSONB
• [ ] Don't ask the user. This is the output of analysis

Rules

• CV is source of truth. Questionnaire covers what the CV doesn't say
• All DB access via scripts/db.js (see db skill). Read-only by default, --write for saves
• Persist everything to users.data as JSONB via jsonb_set:

``bash node scripts/db.js "UPDATE users SET data = jsonb_set(data, '{profile}', ''::jsonb) WHERE id = 1" --write node scripts/db.js "SELECT data->'profile' AS profile FROM users WHERE id = 1" ``

• If user already has a profile in DB, validate changes before overwriting
• Profile is updated when user changes CV or answers new questions
• Questions in blocks of 4, multi-select where applicable
• Each preference with weight: Must / Strong / Nice
• Platforms = output of analysis, never user input
• Single user (repo owner)

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: galiprandi
• Source: galiprandi/job-seeker
• License: MIT
• Homepage: https://galiprandi.github.io/job-seeker/

Install and usage instructions live in the source repository linked above.