菜单
Manage your career graph — people, collaborators, projects, and potential new positions — plus the opportunity pipeline, skill gaps, and learning plan
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Use when the operator asks for a cross-cutting view of everything in flight — daily agenda, weekly review, "what needs me today", or orchestration across the analyst, advisor, scribe, ops, and career skills.
Use when the operator wants a morning brief, meeting prep, stakeholder context before a meeting, commitment tracking, a new recurring report/monitor designed, or status synthesis across channels — the operational powerhouse you never had headcount for.
Use when researching a question that serves a decision, running market/competitive/internal research, verifying AI-generated research, or aggregating multi-model consensus on contested claims
Use when facing a strategic or sensitive decision you can't yet take to the board or the team, when you need a no-ego sounding board, when you want a board-of-advisors debate on a question, when preparing for a real board conversation, or when keeping a decision journal and reviewing past calls.
Use when drafting board updates, emails, LinkedIn posts, or talks in the operator's voice, building or evolving a voice profile from their best writing, audience-testing a draft against reader personas, or iterating on a piece with dimension-scored feedback
Personal health & fitness monitoring — ingest HealthKit metrics, track goals, surface regressions, manage appointments
| name | career |
| description | Manage your career graph — people, collaborators, projects, and potential new positions — plus the opportunity pipeline, skill gaps, and learning plan |
| read_strategy | On first use: read Quick Start + Career Agent Profile. When working with people/projects: read People & Collaborators + Projects sections. When searching: read Discovery section (agent-driven search missions). When ingesting: read Ingestion + Sensemaking sections. When analyzing: read Analysis section. Full command reference: read Commands section on demand. |
| triggers | ["add job / ingest job / new position","add person / add collaborator / add project","who do I know at / show my network / career graph","search for jobs / run a search mission","analyze this job posting / make sense of","show my pipeline / skill gaps / learning plan","update status / career move / job search","create career profile / set up job search"] |
| prerequisites | ["TypeDB running (make db-start)","make build-skills"] |
Use this skill to manage your career as a knowledge graph — not just a job search. The graph holds people (contacts, mentors, sponsors, references), collaborators (who works with you on what), projects (the bodies of work that build career capital), and potential new positions, all layered on top of the opportunity pipeline (position / engagement / venture / lead). Claude acts as the career agent — browsing job boards, evaluating postings against the operator's profile, mapping relationships, and building structured understanding of positions, companies, people, and fit over time.
Key principle: The agent does the sensemaking. Scripts are tools that search APIs, store data, and run queries. The agent decides what's relevant, writes the rationale, and manages the pipeline.
Primer principle (see docs/operating-principles.md): every new opportunity or project gets the operator's messy initial brain dump stored as a primer note (add-note --type primer). Never require the operator to pre-structure it — capture first, organize later.
Role model: The career agent is a BFO/UFO role (career-agent-role) that inheres in a person — it is the person's active career role, and job-seeking is just one mode of it. One person can have multiple roles (career agent, author, evaluator). The role holds career-campaign data; the person holds identity data.
make db-startuv sync --all-extras (from project root)make build-db after make build-skillsPath note: Replace
.claude/skills/careerbelow with your installation directory:
- Claude plugin install:
${CLAUDE_PLUGIN_ROOT}/skills/career(self-contained bundle atplugins/career/)- skillful-alhazen project:
.claude/skills/careerWhen installed as a plugin, TypeDB starts automatically on session start (no manual init required).
TYPEDB_HOST: TypeDB server (default: localhost)TYPEDB_PORT: TypeDB port (default: 1729)TYPEDB_DATABASE: Database name (default: alhazen_notebook)# Ingest a job posting from URL
uv run python .claude/skills/career/career.py ingest-job \
--url "https://boards.greenhouse.io/anthropic/jobs/123456" \
--priority high
# List your pipeline
uv run python .claude/skills/career/career.py list-pipeline
# Show position details
uv run python .claude/skills/career/career.py show-position --id "position-abc123"
# Add a note to any entity
uv run python .claude/skills/career/career.py add-note \
--about "position-abc123" --type research \
--content "Strong AI safety focus, good culture fit"
uv run emits a VIRTUAL_ENV warning to stderr. Always use 2>/dev/null when piping output to a JSON parser -- never 2>&1, which merges the warning into stdout and breaks JSON parsing.
The career agent is modeled as a role (career-agent-role, BFO/UFO pattern) that inheres in a person — the person's active career role, with job-seeking being one mode of it. The role holds career-campaign data (target role, industries, salary expectations) while the person entity holds identity data (name, email, linkedin).
uv run python .claude/skills/career/career.py create-seeker-profile \
--person op-f25ab4b15b0f \
--target-role "Principal AI Architect" \
--industries "AI, Biotech, Scientific Computing" \
--salary "200k-250k" \
--location "Remote / SF Bay Area" \
--focus "TypeDB-powered knowledge systems, AI for science"
This creates a career-agent-role entity linked to the person via alh-role-bearing. All pipeline data (opportunities, skills, candidates) scopes to this role.
Skills are owned by the seeker role, derived from real evidence, and use a simplified Bloom's taxonomy for proficiency levels.
Proficiency levels:
| Level | Meaning | Evidence type | Interview readiness |
|---|---|---|---|
expert | Can design/teach | Publications, architected systems, led teams | Can whiteboard, critique alternatives |
practiced | Hands-on experience | Projects, production use, coursework | Can walk through how you used it |
aware | Conceptual knowledge | Read papers/docs, attended talks | Can answer "what is X" questions |
none | Not known | No evidence | Cannot discuss |
The agent ingests the seeker's professional artifacts as sources of evidence. Each artifact is stored and linked to the seeker via alh-aboutness so skills can trace back to their proof.
# Ingest LinkedIn profile (save page HTML or use Playwright MCP to capture)
uv run python .claude/skills/career/career.py ingest-job \
--url "https://linkedin.com/in/username"
# Ingest resume (PDF or text)
# Upload or paste resume content, agent stores as career-resume artifact
# Ingest ORCID profile
# Agent fetches via WebFetch and stores key publications/experience
# Ingest Google Scholar page
# Agent fetches and extracts publication list, h-index, citation metrics
The agent should fetch each source, extract the content, and store it as an artifact linked to the seeker role via alh-aboutness. This creates the evidence chain: artifact → seeker → skill.
The agent reads the ingested artifacts and conducts a structured interview with the seeker:
For each skill identified, the agent assesses the proficiency level based on the evidence and discussion.
uv run python .claude/skills/career/career.py add-skill \
--name "Knowledge Graphs" --level "expert" \
--evidence "Built 5000+ entity TypeDB production KG, BFO/UFO ontology design" \
--recency "daily 2026" \
--description "TypeDB 3.x, TypeQL, schema evolution, GLAV mapping"
uv run python .claude/skills/career/career.py add-skill \
--name "RAG Pipelines" --level "expert" \
--evidence "Voyage AI + Qdrant in production, hybrid ontology-filtered retrieval" \
--recency "daily 2026"
uv run python .claude/skills/career/career.py add-skill \
--name "Pharma Experience" --level "none" \
--description "No direct pharma industry experience. Biomedical informatics at CZI."
Each skill is linked to the seeker via career-seeker-has-skill. The --evidence field should reference the ingested artifacts where possible.
Once skills are populated, gap analysis joins seeker skills against position requirements:
uv run python .claude/skills/career/career.py show-gaps
uv run python .claude/skills/career/career.py list-skills
People are alh-person entities — deliberately shared with the ops skill's stakeholder dossiers (same alh_personal database, deliberate synergy). The career skill layers career-specific structure on top:
career-contact-for-opportunity relation, career-contact-role attribute).career-collaboration relation) with a career-collab-role (collaborator | mentor | sponsor | reference | co-author), career-collab-since date, and career-collab-strength (weak | working | strong).career-relationship-note), attached to the person via add-note --type relationship.# Add a person to the career graph
uv run python .claude/skills/career/career.py add-person \
--name "Jane Smith" \
--email "jane@example.com" \
--linkedin "https://linkedin.com/in/janesmith" \
--description "Met at ML Summit 2025; leads knowledge-graph team at BigCo"
# List everyone (with collaboration/contact counts)
uv run python .claude/skills/career/career.py list-people
# Full person view: contact roles, collaborations, relationship notes, borne roles
uv run python .claude/skills/career/career.py show-person --id "person-abc123"
# Link a person to a project or any opportunity
uv run python .claude/skills/career/career.py link-collaborator \
--person "person-abc123" \
--target "project-def456" \
--role co-author \
--since 2024-06-01 \
--strength strong
# Who collaborates on this project?
uv run python .claude/skills/career/career.py list-collaborators --target "project-def456"
# What does this person collaborate on?
uv run python .claude/skills/career/career.py list-collaborators --person "person-abc123"
After any meaningful interaction, capture the messy context — it is the most valuable input the graph can get:
uv run python .claude/skills/career/career.py add-note \
--about "person-abc123" --type relationship \
--content "Jane is skeptical of pure-LLM approaches; owes me an intro to her VP. Prefers async email over calls."
A career-project (sub career-opportunity) is a body of work that builds career capital — an open-source project, a paper, a product, a community effort. Projects carry your role (lead | contributor | advisor), a lifecycle status (exploring | active | paused | shipped | sunset), and a URL, and share everything opportunities have (notes, priority, company link, collaborators).
# Create a project
uv run python .claude/skills/career/career.py add-project \
--name "Alhazen Knowledge Notebook" \
--role lead \
--status active \
--url "https://github.com/example/alhazen" \
--priority high \
--description "TypeDB-backed knowledge notebook framework"
# ALWAYS capture the operator's primer right after creation (operating principle #5)
uv run python .claude/skills/career/career.py add-note \
--about "project-abc123" --type primer \
--content "Raw brain dump: why this project matters, who cares about it, what winning looks like..."
# List projects (board data: status, role, collaborators)
uv run python .claude/skills/career/career.py list-projects
uv run python .claude/skills/career/career.py list-projects --status active
# Update lifecycle
uv run python .claude/skills/career/career.py update-project \
--id "project-abc123" --status shipped
Primer rule: every new project and every new opportunity gets a career-primer-note via add-note --type primer capturing the operator's unfiltered initial thinking (voice-dictated is ideal). Do this at creation time, before any analysis.
Discovery is agent-driven: Claude browses job boards, reads postings, evaluates fit against the seeker profile, and adds good matches as candidates with a written rationale. No automated scoring or LLM triage — the agent IS the sensemaker.
search-source) for bulk listing from Greenhouse/Lever boardssearch-source on website sources) for browsing JS-rendered job sites like hiring.cafeweb-search skill) for ad-hoc browsing of careers pages or niche boardsuv run python .claude/skills/career/career_forager.py add-candidate \
--title "Research Scientist, AI for Science" \
--url "https://boards.greenhouse.io/anthropic/jobs/123" \
--location "San Francisco" \
--relevance 0.85 \
--reason "Strong fit: combines AI evaluation with scientific methodology"
Three types of sources:
# Company recruiting pages (Greenhouse/Lever/Ashby) — API-based, returns all jobs
uv run python .claude/skills/career/career_forager.py add-source \
--name "Anthropic" --platform greenhouse --token anthropic
# Job board aggregators (LinkedIn/Remotive/Adzuna) — API-based, keyword search
uv run python .claude/skills/career/career_forager.py add-source \
--name "ML Jobs" --platform linkedin --query "machine learning" --location "San Francisco"
# Job websites (hiring.cafe, etc.) — browser-based via Playwright MCP
uv run python .claude/skills/career/career_forager.py add-source \
--name "hiring.cafe" --platform website \
--url "https://hiring.cafe" \
--query "AI scientist knowledge graph biotech drug discovery"
| Platform | Type | Auth | Args |
|---|---|---|---|
greenhouse | Company board | None | --token (slug) |
lever | Company board | None | --token (slug) |
ashby | Company board | None | --token (slug) |
linkedin | Aggregator | None | --query, --location |
remotive | Aggregator | None | --query, --location |
adzuna | Aggregator | API key | --query, --location |
website | Job website | Playwright MCP | --url, --query |
Returns all job listings from a source for the agent to evaluate:
uv run python .claude/skills/career/career_forager.py search-source --source "Anthropic"
# Returns: {jobs: [{title, url, location, external_id}, ...]}
The agent reads through these, fetches interesting postings via web-fetch, and adds good matches as candidates.
For website platform sources, search-source returns instructions instead of job listings.
The agent uses Playwright MCP tools to browse the site interactively:
search-source --source "hiring.cafe" returns the URL and querybrowser_navigate to the source URLbrowser_type the query into the search box, submitbrowser_snapshot or browser_take_screenshot to see rendered resultsbrowser_run_code_unsafe to extract job URLs from the DOM, or read the snapshotWebFetch each interesting job URL (individual pages usually render server-side)add-candidate with rationale for each good matchExample: hiring.cafe
hiring.cafe is a 41M+ job aggregator with AI-powered search. It uses client-side React rendering, so API/curl cannot access search results — Playwright MCP is required.
The site auto-generates filters from natural language queries (Department, Industry, Tech Keywords). Sometimes the auto-filters are too narrow and return zero results. If this happens, click "Reject All" on the suggested filters and try a broader query, or remove specific filter chips.
Individual job pages (/viewjob/{id}) render server-side and can be fetched with WebFetch for full details.
Candidates are embedded in Qdrant on creation. Search by meaning:
uv run python .claude/skills/career/career_forager.py search-candidates \
--query "knowledge graph engineering for science" --limit 10
# List all candidates
uv run python .claude/skills/career/career_forager.py list-candidates
# List by status
uv run python .claude/skills/career/career_forager.py list-candidates --status reviewed
# Promote a candidate to a full position (creates career-position + application note)
uv run python .claude/skills/career/career_forager.py promote --id candidate-abc123
The agent does the sensemaking. Scripts are tools — they search APIs, store data, and run queries. The agent decides what's relevant, writes the rationale, and manages the pipeline.
Triggers: "add job", "ingest job", "new position", "found a job posting", "here's a job"
uv run python .claude/skills/career/career.py ingest-job \
--url "https://boards.greenhouse.io/anthropic/jobs/123456" \
--priority high \
--tags "ai" "ml" "safety"
Options:
--url (required): Job posting URL--priority: Set priority (high/medium/low)--tags: Space-separated tagsReturns:
{
"success": true,
"position_id": "position-abc123",
"artifact_id": "artifact-xyz789",
"status": "raw",
"message": "Artifact stored - ask Claude to 'analyze this job posting' for sensemaking."
}
What ingestion produces:
career-position entity with status researchingcareer-job-description artifact containing the raw scraped page contentcareer-application-note tracking the application statusuv run python .claude/skills/career/career.py add-position \
--title "Senior ML Engineer" \
--company "Anthropic" \
--priority high
uv run python .claude/skills/career/career.py add-company \
--name "Anthropic" \
--url "https://anthropic.com" \
--description "AI safety research company"
uv run python .claude/skills/career/career.py add-engagement \
--name "Acme Corp Data Consulting" \
--company-id "company-abc123" \
--type project \
--rate "$200/hr" \
--status active \
--priority high
Engagement types: hourly | project | retainer | advisory
uv run python .claude/skills/career/career.py add-venture \
--name "Augura Health Advisory" \
--stage proposal-sent \
--equity-type advisor \
--priority high
Venture stages: exploring | proposal-sent | negotiating | active | paused | closed
Equity types: none | advisor | cofounder | investor
uv run python .claude/skills/career/career.py add-lead \
--name "Jane Smith - BigCo" \
--status warm \
--priority medium \
--description "Met at ML Summit, interested in consulting"
Lead statuses: first-contact | active | inactive | closed
When user says "analyze this job posting" or "make sense of [position]":
Get the artifact content
uv run python .claude/skills/career/career.py show-artifact --id "artifact-xyz"
Read and comprehend the content
Research the company and leadership online (use the web-search skill)
Search for: company mission, funding, recent news, and LinkedIn activity of key leaders. Focus on:
Save findings as a research note attached to both the company and the position:
# Company-level research (background, funding, mission)
uv run python .claude/skills/career/career.py add-note \
--about "company-xyz" \
--type research \
--content "Series C, $4B raised. Mission: X. CEO recently spoke at Y conference..."
# Position-level research (leadership context, role fit signals, hiring manager)
uv run python .claude/skills/career/career.py add-note \
--about "position-abc123" \
--type research \
--content "Bo Wang (SVP Biomedical AI) is likely hiring manager. Very active on LinkedIn..."
Create/update the company
uv run python .claude/skills/career/career.py add-company \
--name "Anthropic" \
--url "https://anthropic.com" \
--description "AI safety research company"
Extract requirements using the skill vocabulary
Before extracting requirements, load the skill concept vocabulary:
uv run python .claude/skills/career/career.py list-concepts
This returns the full vocabulary with your proficiency levels, alt-labels, and hierarchy. Use canonical concept names when creating requirements so they match your skill profile.
For each skill in the posting:
# Create a new concept if needed
uv run python .claude/skills/career/career.py add-concept \
--name "Molecular Simulation" \
--alt-labels "MD,Molecular Dynamics,Force Fields"
# Add the user's self-assessed skill
uv run python .claude/skills/career/career.py add-skill \
--name "Molecular Simulation" --level "aware" \
--evidence "Read papers, understand concepts, no hands-on" \
--recency "reading 2026"
# Then add the requirement (using the canonical concept name)
uv run python .claude/skills/career/career.py add-requirement \
--position "position-abc123" \
--skill "Molecular Simulation" \
--level "required"
Key principle: Never assume the user's skill level. If a requirement mentions a skill not in their profile, ask them. Their self-assessment + evidence is the ground truth, not the agent's inference.
Create analysis notes
Fit Analysis Note:
uv run python .claude/skills/career/career.py add-note \
--about "position-abc123" \
--type fit-analysis \
--content "Strong fit for core requirements. Gap in distributed systems." \
--fit-score 0.82 \
--fit-summary "Strong technical fit, one gap to address"
Skill Gap Note:
uv run python .claude/skills/career/career.py add-note \
--about "position-abc123" \
--type skill-gap \
--content "Distributed systems is required. Recommend: DDIA book, MIT 6.824 course."
Flag uncertainties
uv run python .claude/skills/career/career.py tag \
--entity "requirement-xyz" \
--tag "uncertain"
Report findings to user: company overview, leadership signals, fit score breakdown, key gaps, suggested next steps (including who to follow on LinkedIn)
uv run python .claude/skills/career/career.py list-artifacts --status raw
uv run python .claude/skills/career/career.py list-artifacts --status all
uv run python .claude/skills/career/career.py show-artifact --id "artifact-xyz789"
Every opportunity MUST have after sensemaking:
add-note --type primer at creation time (operating principle #5: always capture primers)--company flag on add-position, auto-matched to existing career-company entities (do not create duplicates)salary-range attributecareer-requirement entities via add-requirementfit-score (0.0-1.0) and fit-summarycareer-opp-summary-note synthesizing all notes. Regenerated after any note is added or updated.embedding_map.py embed-and-map after saving the summary so the opportunity appears on Mission Control.Every opportunity has exactly one career-opp-summary-note — a living markdown dossier that is overwritten each time notes change. This is the primary embedding text for the Mission Control map and the quick-read view for understanding any opportunity.
Workflow — regenerate after any note update:
# 1. Fetch all notes + metadata
uv run python .claude/skills/career/career.py regenerate-summary --about <opp-id>
# 2. Read the JSON output, write a markdown summary following the template below
# 3. Save the summary (creates or overwrites)
uv run python .claude/skills/career/career.py upsert-summary --about <opp-id> --content "@/tmp/summary.md"
# 4. Re-embed to update the Mission Control map
uv run python local_skills/career/embedding_map.py embed-and-map
IMPORTANT: Step 4 (re-embed) MUST be run after saving the summary. Without it, new or updated opportunities will not appear on the Mission Control map. This step re-computes embeddings for ALL opportunities and regenerates the 2D layout.
Summary templates by type:
Position:
## Role
- Title, company, location/remote policy
- Key responsibilities (2-3 bullets)
- Salary range if known
## Fit
- Overall fit score and one-line assessment
- Top strengths (2-3 bullets with specifics)
- Key gaps (1-2 bullets)
## Company
- What they do, stage/size, why interesting
- Key people (hiring manager, contacts)
## Status
- Current application status
- Key dates, next steps, or outcome
Engagement:
## Engagement
- Client, scope, type (consulting/contract/advisory)
- Rate/compensation if known
## Fit
- Why this is a good match
- Key deliverables
## Status
- Current stage (proposal/active/paused/closed)
Venture:
## Overview
- What the venture is, stage
- Your role/involvement
## Opportunity
- Why it's interesting
- Key milestones or next steps
## Status
- Current stage (seed/series-a/series-b/growth/closed)
Lead:
## Contact
- Who, title, organization
- How you met, when
## Context
- What the connection is about
- Potential opportunity or value
## Status
- Relationship state (first-contact/active/inactive/closed)
uv run python .claude/skills/career/career.py update-status \
--position "position-abc123" \
--status "applied" \
--date "2025-02-05"
Status values: researching | applied | phone-screen | interviewing | offer | accepted | rejected | withdrawn
(accepted marks an offer you have accepted — the terminal "won" state for a position.)
# Interaction note
uv run python .claude/skills/career/career.py add-note \
--about "position-abc123" \
--type interaction \
--content "Phone screen went well, moving to technical round." \
--interaction-type "call" \
--interaction-date "2025-02-05"
# Strategy note
uv run python .claude/skills/career/career.py add-note \
--about "position-abc123" \
--type strategy \
--content "Lead with distributed systems experience from caching project."
# Claude Code brief (CC agent writes a brief for the next session)
uv run python .claude/skills/career/career.py add-note \
--about "position-abc123" \
--type cc-brief \
--content "Next session: prep for technical interview on 2025-02-10. Focus on system design questions."
# Claude Code feedback (CC agent records feedback from completed interaction)
uv run python .claude/skills/career/career.py add-note \
--about "position-abc123" \
--type cc-feedback \
--content "User reported phone screen went well. Interviewer asked about distributed caching. Move to next round."
Note types: research | strategy | interview | interaction | skill-gap | fit-analysis | primer | relationship | general | cc-brief | cc-feedback
uv run python .claude/skills/career/career.py show-gaps
uv run python .claude/skills/career/career.py learning-plan
uv run python .claude/skills/career/career.py add-resource \
--name "Designing Data-Intensive Applications" \
--type "book" \
--url "https://dataintensive.net" \
--hours 30 \
--skills "distributed-systems" "system-design"
uv run python .claude/skills/career/career.py link-resource \
--resource "<resource-id>" \
--requirement "<requirement-id>"
# Search for papers on a skill gap topic
uv run python .claude/skills/scientific-literature/scientific_literature.py search \
--query "machine learning systems design" \
--collection "ML Systems Reading List"
# Link collection to skill gap
uv run python .claude/skills/career/career.py link-collection \
--collection "<collection-id>" \
--skill "machine-learning"
# Link a specific paper to a learning resource
uv run python .claude/skills/career/career.py link-paper \
--resource "<resource-id>" \
--paper "<paper-id>"
# View updated plan
uv run python .claude/skills/career/career.py learning-plan
# All positions
uv run python .claude/skills/career/career.py list-pipeline
# Filter by status or priority
uv run python .claude/skills/career/career.py list-pipeline --status "interviewing"
uv run python .claude/skills/career/career.py list-pipeline --priority "high"
# All opportunity types
uv run python .claude/skills/career/career.py list-opportunities --type all
uv run python .claude/skills/career/career.py list-opportunities --type venture
uv run python .claude/skills/career/career.py list-opportunities --type engagement --status active
# Detail views
uv run python .claude/skills/career/career.py show-position --id "position-abc123"
uv run python .claude/skills/career/career.py show-opportunity --id "venture-abc123"
uv run python .claude/skills/career/career.py show-company --id "company-xyz"
uv run python .claude/skills/career/career.py show-gaps
uv run python .claude/skills/career/career.py learning-plan
uv run python .claude/skills/career/career.py report-pipeline # Pipeline overview
uv run python .claude/skills/career/career.py report-stats # Stats summary
uv run python .claude/skills/career/career.py report-gaps # Skill gaps
uv run python .claude/skills/career/career.py report-position --id "position-xyz" # Position detail
Use reports (Markdown) for displaying to users in chat. Use JSON commands (list-pipeline, show-position) for programmatic processing.
uv run python .claude/skills/career/career.py tag --entity "position-abc123" --tag "remote"
uv run python .claude/skills/career/career.py search-tag --tag "remote"
The career skill tracks multiple types of career opportunities via the career-opportunity hierarchy:
career-opportunity (base)
+-- career-position -- formal employment application (has application-status pipeline)
+-- career-engagement -- paid consulting/service work for a client
+-- career-venture -- startup/advisory/equity opportunity
+-- career-lead -- early-stage networking contact, role undefined
+-- career-project -- body of work building career capital (open-source, paper, product)
All opportunity types share: opportunity-status, priority-level, deadline, and can be linked to a career-company via opportunity-at-organization.
All opportunity types work with add-note --about <ID> -- notes attach to any identifiable-entity.
uv run python .claude/skills/career/career.py update-opportunity \
--id "venture-abc123" \
--status active \
--stage negotiating \
--priority high
| Situation | Entity type | Key attributes |
|---|---|---|
| Own consulting business | career-company | name, description |
| Startup advisory role | career-venture | venture-stage, equity-type |
| Consulting engagement | career-engagement | engagement-type, rate-info |
| Networking contact (no role yet) | career-lead | opportunity-status, description |
| Formal job application | career-position | job-url, application-status pipeline |
| Open-source/paper/product work | career-project | project-role, project-status, project-url |
Phylo strategy pattern: Use an existing career-position as the anchor for all interactions. Add career-interaction-note entries for each meeting. Only create a separate career-lead if a genuinely new thread emerges from those conversations.
| Type | Description |
|---|---|
your-skill | Your skills for gap analysis |
career-company | An employer/client organization |
career-opportunity | Base type for all opportunities |
career-position | Formal job posting (sub opportunity) |
career-engagement | Consulting engagement (sub opportunity) |
career-venture | Startup/advisory venture (sub opportunity) |
career-lead | Networking lead (sub opportunity) |
career-project | Body of work building career capital (sub opportunity) |
alh-person | A person in the career graph (shared with ops dossiers) |
career-agent-role | The person's active career role (job-seeking is one mode) |
career-learning-resource | Course, book, tutorial |
career-search-source | Company board for forager |
career-candidate | Discovered posting (forager) |
| Type | Description |
|---|---|
career-job-description | Raw HTML/text from job posting URL |
career-resume | Resume document |
career-cover-letter | Cover letter |
career-company-page | Company website snapshot |
career-proposal | Proposal or pitch deck for engagement/venture |
| Type | Purpose |
|---|---|
career-application-note | Status tracking (positions) |
career-research-note | Company/opportunity research |
career-interview-note | Interview prep/feedback |
career-strategy-note | Talking points, approach |
career-skill-gap-note | Learning needs |
career-fit-analysis-note | Fit assessment |
career-interaction-note | Contact logs |
career-primer-note | Operator's messy initial brain dump (every new opportunity/project) |
career-relationship-note | Narrative about a person relationship in career context |
career-cc-brief-note | Claude Code brief for next session |
career-cc-feedback-note | Claude Code feedback from completed interaction |
position-at-company -- links position to employeropportunity-at-organization -- links any opportunity type to a companycareer-collaboration -- links a person to a project/opportunity (owns collab-role, collab-since, collab-strength)career-contact-for-opportunity -- a person serving as contact for an opportunity (owns contact-role)aboutness -- links notes to any entity (position, company, opportunity, person)requirement-of -- links requirements to positionslocal_skills/career/schema.tqllocal_resources/typedb/alhazen_notebook.tql| Command | Description | Key Args |
|---|---|---|
ingest-job | Fetch job URL, store raw | --url |
add-skill | Add/update skill with Bloom's proficiency | --name, --level (expert/practiced/aware/none), --evidence, --recency |
list-skills | Show your skills | |
list-artifacts | List artifacts by status | --status |
show-artifact | Get artifact content | --id |
add-company | Add company | --name |
add-position | Add position manually | --title |
add-engagement | Add consulting engagement | --name, --type, --rate |
add-venture | Add startup/advisory venture | --name, --stage, --equity-type |
add-lead | Add networking lead | --name, --status |
update-opportunity | Update any opportunity status/stage/priority | --id |
show-opportunity | Show any opportunity details | --id |
list-opportunities | List by type/status | --type, --status, --priority |
add-person | Add a person to the career graph | --name, --email, --linkedin |
list-people | List people with collaboration/contact counts | |
show-person | Person detail: contact roles, collaborations, relationship notes | --id |
add-project | Add a career project | --name, --role, --status, --url |
list-projects | List projects with collaborators | --status, --role |
update-project | Update project role/status/url/priority | --id |
link-collaborator | Link person to project/opportunity | --person, --target, --role, --since, --strength |
list-collaborators | List collaborations | --person or --target |
migrate-from-jobhunt | One-shot copy of legacy jhunt-* data into career-* types | |
add-requirement | Add skill requirement | --position, --skill |
update-status | Change position application status | --position, --status |
add-note | Create any note type | --about, --type, --content |
add-resource | Add learning resource | --name, --type |
link-collection | Link paper collection to skill gap | --collection, --skill |
link-resource | Link resource to a skill requirement | --resource, --requirement |
link-paper | Link learning resource to a paper | --resource, --paper |
create-seeker-profile | Create the career-agent role for a person | --person, --target-role, --industries |
list-pipeline | Show position pipeline | --status, --priority, --person |
show-position | Position details | --id |
show-company | Company details | --id |
show-gaps | Skill gap analysis | |
learning-plan | Prioritized study list | |
tag | Tag an entity | --entity, --tag |
search-tag | Find by tag | --tag |
report-pipeline | Pipeline overview (Markdown) | |
report-stats | Stats summary (Markdown) | |
report-gaps | Skill gaps report (Markdown) | |
report-position | Position detail (Markdown) | --id |
| Command | Description | Key Args |
|---|---|---|
add-source | Add a search source (company board or aggregator) | --name, --platform, --token/--query |
list-sources | List configured search sources | |
remove-source | Remove a source | --id, --token, or --name |
search-source | Search one source (raw listings for agent review) | --source |
add-candidate | Add a candidate with agent evaluation | --title, --url, --relevance, --reason |
search-candidates | Semantic search across candidates (Qdrant) | --query, --limit |
list-candidates | List candidates | --status, --source |
promote | Promote candidate to full position | --id |
Declarative audit rules for data quality are defined in quality-checks.yaml.
# Check for violations
uv run python .claude/skills/career/career.py audit
# Auto-fix where possible
uv run python .claude/skills/career/career.py audit --fix
# Or use the generic audit runner
uv run python src/skillful_alhazen/utils/audit_runner.py run \
--checks local_skills/career/quality-checks.yaml
| Check | Severity | Description |
|---|---|---|
position-company-link | high | Positions should be linked to a company via position-at-company |
ugly-titles | medium | Position titles containing job-board boilerplate |
missing-short-name | medium | Positions without a short-name for compact display |
missing-salary | low | Positions without salary-range information |
positions-without-notes | low | Positions with no notes at all |
duplicate-companies | high | Multiple company entities with identical names |
orphaned-companies | medium | Companies with no positions or opportunities linked |
projects-without-collaborators | low | Projects with no linked collaborators |
people-without-context | low | People with no notes, collaborations, or contact roles |
A Next.js dashboard is available for visualizing your job search pipeline. It runs in Docker (not npm run dev).
docker compose build --no-cache dashboard
docker compose up -d dashboard
# Dashboard available at http://localhost:3001
/career) -- Kanban columns by application status/career, People tab) -- Table of the career graph's people with collaboration/contact counts/career, Projects tab) -- Board by project status with collaborator chips/career/position/{id}) -- Full position profile: requirements, notes, gap analysis, fit score/career/person/{id}) -- Contact roles, collaborations, relationship notes/career/project/{id}) -- Project profile with collaborators and notes/career/collection/{id}) -- Notes and resources grouped by collectiondashboard/src/app/(career)/career/dashboard/src/components/career/dashboard/src/app/api/career/dashboard/src/lib/career.tsThe career skill is the pivot of the former jobhunt skill. The schema rename (jhunt-* → career-*) orphans any legacy jhunt-* instance data in an existing alh_personal database. The migration is defined declaratively as GLAV mapping rules in mapping/rules/ (same framework and rule format as the dismech mapping in skillful-alhazen — source_match fetch → target_insert, with foreign keys resolved through the career-legacy-id natural key recorded on every migrated copy). See mapping/README.md for the rule catalog.
# Convenience wrapper (delegates to the bundled runner):
uv run python .claude/skills/career/career.py migrate-from-jobhunt --dry-run
uv run python .claude/skills/career/career.py migrate-from-jobhunt
# Or run the rules directly (schema_mapper-compatible CLI):
uv run python .claude/skills/career/mapping/glav_runner.py run \
--source-db alh_personal --target-db alh_personal \
--rules-dir .claude/skills/career/mapping/rules --dry-run
career-legacy-id) is loaded into the database — the SessionStart hook does this.career-agent-role) with fresh skolem ids + career-legacy-id. Phase 4 rules rebuild relations (company links, pipeline, requirements, skill hierarchy, forager links, aboutness, artifact representation) between the copies by matching legacy ids.career-legacy-id already exists in the target (target_check).career-* copies (compare counts per type against the rule report), then clean up manually. Tags on legacy entities are not copied.During sensemaking, if you encounter a concept, relationship, or entity type that has no place in the current TypeDB schema, that is a schema gap -- a signal for schema evolution, not a failure.
When you notice a schema gap:
uv run python local_resources/skilllog/skill_logger.py file-schema-gap \
--skill career \
--concept "<the concept you tried to represent>" \
--missing "<which TypeDB entity/relation/attribute is absent>" \
--suggested "<proposed TypeQL addition, or 'unknown' if unsure>"
Examples of schema gaps:
career-collab-role (collaborator/mentor/sponsor/reference/co-author) or career-contact-roleUse --dry-run first to review the issue before filing it.