Agent skill

when-tracking-dual-career-intelligence-use-career-intel

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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/when-tracking-dual-career-intelligence-use-career-intel

SKILL.md

/============================================================================/ /* SKILL SKILL :: VERILINGUA x VERIX EDITION / /============================================================================*/


name: skill version: 1.0.0 description: | [assert|neutral] Automated US/EU career opportunity tracking with policy monitoring, EV-based ranking, and tailored application materials [ground:given] [conf:0.95] [state:confirmed] category: career-orchestration tags:

  • general author: system cognitive_frame: primary: evidential goal_analysis: first_order: "Execute skill workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic career-orchestration processes"

/----------------------------------------------------------------------------/ /* S0 META-IDENTITY / /----------------------------------------------------------------------------*/

[define|neutral] SKILL := { name: "skill", category: "career-orchestration", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S1 COGNITIVE FRAME / /----------------------------------------------------------------------------*/

[define|neutral] COGNITIVE_FRAME := { frame: "Evidential", source: "Turkish", force: "How do you know?" } [ground:cognitive-science] [conf:0.92] [state:confirmed]

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

/----------------------------------------------------------------------------/ /* S2 TRIGGER CONDITIONS / /----------------------------------------------------------------------------*/

[define|neutral] TRIGGER_POSITIVE := { keywords: ["skill", "career-orchestration", "workflow"], context: "user needs skill capability" } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S3 CORE CONTENT / /----------------------------------------------------------------------------*/

Dual-Track Career Intelligence

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

Automated tracking of internal/external roles across US/EU markets with policy monitoring, visa leverage analysis, and tailored pitch generation.

Overview

This skill orchestrates 3 specialist agents to:

  1. Scout (researcher) - Crawl job boards and normalize opportunities
  2. RegWatch (researcher) - Diff EU policy/regulatory pages for visa changes
  3. Ranker (analyst) - Score opportunities by Fit, Option Value, Speed, Cred Stack
  4. PitchPrep (coder) - Generate tailored bullets, Q&A, and anecdotes

Critical differentiator: Tracks visa leverage and immigration policy changes alongside traditional job factors.

When to Use

  • Weekly/bi-weekly: Systematic career opportunity scanning
  • Before major applications: Generate tailored pitch materials
  • EU policy changes: Understand immigration implications
  • Strategic planning: Maintain optionality across geographies

Assigned Agents

Primary Agents

researcher (Scout role) - Phase 1: Web scraping, data normalization, source aggregation

  • Expertise: API integration, data extraction, YAML processing
  • Tools: curl, jq, yq, bash scripting
  • Output: Raw CSV of opportunities with metadata

researcher (RegWatch role) - Phase 2: Policy diff analysis, regulatory monitoring

  • Expertise: Document comparison, policy interpretation, change detection
  • Tools: diff, git, web scraping
  • Output: Policy change summary with action items

Secondary Agents

analyst (Ranker role) - Phase 3: Multi-factor scoring, EV calculation

  • Expertise: Scoring algorithms, decision analysis, prioritization
  • Tools: Python/Node scoring scripts, statistical analysis
  • Output: Ranked opportunities with justifications

coder (PitchPrep role) - Phase 4: Content generation, tailoring, formatting

  • Expertise: Natural language generation, resume optimization, storytelling
  • Tools: Template engines, GPT-assisted writing, markdown formatting
  • Output: Tailored cover letters, Q&A prep, cred stack mapping

Coordination Pattern

SKILL: dual-track-career-intelligence
  ↓
hierarchical-coordinator spawns 4 sequential phases
  ↓
Phase 1: Scout (researcher) → raw data
Phase 2: RegWatch (researcher) → policy deltas
Phase 3: Ranker (analyst) → scored opportunities
Phase 4: PitchPrep (coder) → tailored materials
  ↓
All phases coordinate via Memory MCP with WHO/WHEN/PROJECT/WHY tagging

Phase 1: Scout (Data Collection)

Agent: researcher (Scout role)

Inputs

  • data/sources/job_boards.yml - Job board APIs and search parameters
  • data/profiles/cv_core.md - Keywords and skills to match

Commands Executed

bash
#!/bin/bash
# Phase 1: Job Board Scanning

# PRE-TASK HOOK
npx claude-flow@alpha hooks pre-task \
  --description "Career intel: job board scanning" \
  --agent "researcher" \
  --role "Scout" \
  --skill "dual-track-career-intelligence"

# SESSION RESTORE (if resuming)
npx claude-flow@alpha hooks session-restore \
  --session-id "career-intel-$(date +%Y-%W)"

# SETUP
WEEK=$(date +%Y-%W)
mkdir -p outputs/reports
mkdir -p raw_data

# READ CONFIG
BOARDS=$(yq eval '.boards[].url' data/sources/job_boards.yml)
KEYWORDS=$(yq eval '.search_keywords | join(",")' data/sources/job_boards.yml)
GEO=$(yq eval '.geo_filters | join(",")' data/sources/job_boards.yml)

# SCRAPE JOB BOARDS
echo "title,company,location,url,posted_date,visa_support,remote_ok,comp_signal" > raw_data/jobs_${WEEK}.csv

for BOARD in $BOARDS; do
  echo "[Scout] Scanning: $BOARD"

  # Example API call (adapt to actual board APIs)
  curl -s "${BOARD}/api/jobs?keywords=${KEYWORDS}&geo=${GEO}" \
    | jq -r '.results[] | [
        .title,
        .company,
        .location,
        .apply_url,
        .posted_date,
        .visa_sponsorship,
        .remote_allowed,
        (.salary.min // 0)
      ] | @csv' \
    >> raw_data/jobs_${WEEK}.csv

  sleep 2  # Rate l

/*----------------------------------------------------------------------------*/
/* S4 SUCCESS CRITERIA                                                         */
/*----------------------------------------------------------------------------*/

[define|neutral] SUCCESS_CRITERIA := {
  primary: "Skill execution completes successfully",
  quality: "Output meets quality thresholds",
  verification: "Results validated against requirements"
} [ground:given] [conf:1.0] [state:confirmed]

/*----------------------------------------------------------------------------*/
/* S5 MCP INTEGRATION                                                          */
/*----------------------------------------------------------------------------*/

[define|neutral] MCP_INTEGRATION := {
  memory_mcp: "Store execution results and patterns",
  tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"]
} [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed]

/*----------------------------------------------------------------------------*/
/* S6 MEMORY NAMESPACE                                                         */
/*----------------------------------------------------------------------------*/

[define|neutral] MEMORY_NAMESPACE := {
  pattern: "skills/career-orchestration/skill/{project}/{timestamp}",
  store: ["executions", "decisions", "patterns"],
  retrieve: ["similar_tasks", "proven_patterns"]
} [ground:system-policy] [conf:1.0] [state:confirmed]

[define|neutral] MEMORY_TAGGING := {
  WHO: "skill-{session_id}",
  WHEN: "ISO8601_timestamp",
  PROJECT: "{project_name}",
  WHY: "skill-execution"
} [ground:system-policy] [conf:1.0] [state:confirmed]

/*----------------------------------------------------------------------------*/
/* S7 SKILL COMPLETION VERIFICATION                                            */
/*----------------------------------------------------------------------------*/

[direct|emphatic] COMPLETION_CHECKLIST := {
  agent_spawning: "Spawn agents via Task()",
  registry_validation: "Use registry agents only",
  todowrite_called: "Track progress with TodoWrite",
  work_delegation: "Delegate to specialized agents"
} [ground:system-policy] [conf:1.0] [state:confirmed]

/*----------------------------------------------------------------------------*/
/* S8 ABSOLUTE RULES                                                           */
/*----------------------------------------------------------------------------*/

[direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed]

[direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed]

[direct|emphatic] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed]

/*----------------------------------------------------------------------------*/
/* PROMISE                                                                     */
/*----------------------------------------------------------------------------*/

[commit|confident] <promise>SKILL_VERILINGUA_VERIX_COMPLIANT</promise> [ground:self-validation] [conf:0.99] [state:confirmed]

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