job-hunter

name	job-hunter
description	This skill should be used when the user asks to "find jobs", "search for jobs matching my expectations", "find the best job matching my expectation", "job hunt", "search job platforms", "match jobs to my profile", "find AI engineer jobs", "find ML engineer jobs", "search for senior software engineer roles", "find jobs with visa sponsorship", or mentions job hunting, job matching, career search, or job platform scraping.
version	1.2.0

Job Hunter

Search job platforms (UK and France), match listings to the candidate's profile, check visa sponsorship (UKVI for UK, Talent Passport for France), calculate financial viability including commute costs, and export ranked results to an Excel workbook (.xlsx).

Powered by DEB Cloud — scraping, reference data, and job caching are provided by the DEB Cloud MCP server (mcp__deb-jobhunter__* tools).

The country field in the expectations JSON drives platform selection, tax/social calculation, visa checking, and currency formatting. Supported countries: gb (default), fr.

Output language: All user-facing output (progress messages, reports, summaries, Excel headers) MUST be in the language of the country field. For fr: write in French. For gb: write in English. If the user writes in a specific language, respond in that language regardless of country.

CRITICAL — Follow ALL steps in order. DO NOT skip steps or improvise. The pipeline has 7 mandatory steps: Step 0 → 1 → 2 → 3 → 3.5 → 4 → 5 → 5.5 → 6. Each step depends on the previous step's output. Skipping enrichment (Step 4) degrades scoring. Skipping company checks (Step 5.5) loses ratings and visa data. Skipping commute (Step 6) loses financial viability. Do NOT write custom Python to replace process_jobs.py stages — always use the script as documented. Do NOT use scrape_jobs (legacy) — always use launch_scrape_jobs (async pipeline).

Context Management — Save Tool Responses to File

CRITICAL: MCP tool responses (especially scrape_jobs and scrape_url) return large payloads that saturate the context window. You MUST save them to disk immediately and reference the file path instead of keeping the content in context.

Temp directory: Create a mcp_jobhunter subdirectory under the system temp folder. Detect the OS temp path at runtime:

• Linux/macOS: /tmp/mcp_jobhunter/
• Windows: %TEMP%\mcp_jobhunter\ (e.g., C:\Users\{user}\AppData\Local\Temp\mcp_jobhunter\)

Use Bash to create the directory if it doesn't exist: mkdir -p /tmp/mcp_jobhunter (Unix) or equivalent.

Pattern for every MCP tool call that returns content:

1. Call the MCP tool (e.g., scrape_jobs, scrape_url, get_reputation, get_commute_results)
2. Immediately use the Write tool to save the full response to a JSON file in the temp directory
3. Only retain a brief summary in your working memory: status, count, file path
4. When you need the data later, use the Read tool to load it from the file

File naming convention (inside mcp_jobhunter/):

scrape_{platform}_{city}_{role_slug}_{YYYYMMDD_HHMMSS}.json
detail_batch{N}_{YYYYMMDD_HHMMSS}.json
reputation_batch{N}_{YYYYMMDD_HHMMSS}.json

Example flow:

# 1. Call tool
result = scrape_jobs(query="AI Engineer", platforms=["reed"], location="London")

# 2. Save full response to temp file immediately
Write("<temp_dir>/mcp_jobhunter/scrape_reed_london_ai_engineer_20260305_143022.json", json(result))

# 3. Only keep summary in context:
#    "reed-london: 12 jobs scraped, saved to <temp_dir>/mcp_jobhunter/scrape_reed_london_ai_engineer_20260305_143022.json"

# 4. Later, when parsing:
#    Read("<temp_dir>/mcp_jobhunter/scrape_reed_london_ai_engineer_20260305_143022.json")

This saves large payloads to disk immediately to avoid saturating the context window.

Data Persistence

CRITICAL: All intermediate data MUST be saved to disk at each step. This ensures that if the conversation runs out of context, the next invocation can resume from the last checkpoint without re-scraping.

Working Directory

Create a session working directory in the same folder as the expectations JSON:

{expectations_dir}/job-search-{YYYYMMDD_HHMMSS}/
├── state.json                          # Current progress tracker
├── checkpoint-raw-combined.json        # All raw results from pipeline (after Step 3)
├── checkpoint-dedup.json               # After deduplication (after Step 3.5)
├── checkpoint-filtered.json            # After first-layer filter (after Step 3.5)
├── checkpoint-enriched.json            # After JD enrichment (after Step 4)
├── checkpoint-scored.json              # After LLM agent scoring (after Step 5)
├── company-checks.json                 # Company reputation + UKVI + agency (Step 5.5)
├── commute-data.json                   # Commute costs from DEB Cloud (Step 6)
├── checkpoint-final.json               # After financial calc + visa check (after Step 6)
└── jobs-{YYYYMMDD_HHMMSS}.xlsx        # Final Excel output (after Step 6)

State File (state.json)

Track progress so interrupted sessions can resume:

{
  "session_id": "20260211_143022",
  "expectations_path": "path/to/expectations.json",
  "working_dir": "path/to/job-search-20260211_143022/",
  "deb_cloud_key_valid": true,
  "current_step": 3,
  "step_3_batch_id": null,
  "step_3_complete": false,
  "step_3_5_complete": false,
  "step_4_batch_id": null,
  "step_4_complete": false,
  "step_5_complete": false,
  "step_5_5_complete": false,
  "step_6_complete": false,
  "started_at": "2026-02-11T14:30:22",
  "updated_at": "2026-02-11T14:45:12"
}

Resume Logic

At the start of the workflow, check if a working directory exists with matching expectations file:

1. Look for job-search-* directories in the expectations folder
2. If found with a state.json, read it to determine where to resume
3. If Step 3 has a batch_id: poll for results, then continue from Step 3.5
4. If Step 3 was complete: load checkpoint-raw-combined.json, continue to Step 3.5
5. If Step 3.5+ complete: load the latest checkpoint, skip ahead
6. If no working directory found: start fresh from Step 1

---

Execution Mode

Proceed through all steps without asking for user confirmation between steps. Only pause to ask the user if: (1) DEB Cloud key is missing, (2) an error requires a decision, or (3) the search matrix exceeds 100 queries. All file writes, script executions, and MCP calls should proceed automatically.

Workflow

Execute these steps in order. Save intermediate data at each step. Log progress to the user after each major step. After completing each step, update state.json with the step's completion flag and updated_at timestamp.

Step 0: Validate DEB Cloud API Key

1. Read the DEB_CLOUD_API_KEY environment variable.
2. If set, call mcp__deb-jobhunter__ping to validate:
   • On success: store deb_cloud_key_valid: true in state. Note the plan and available platforms.
   • On failure: warn user, store deb_cloud_key_valid: false.
3. If not set: warn the user: "DEB Cloud API key not configured. Register at debytes.io/products/cloud to get a key. Running in degraded mode — scraping and data lookups are unavailable."
4. In degraded mode: the user can provide their own raw job data files. Dedup, scoring, financial calc, and Excel export still work locally.

Step 1: Load and Validate Inputs

1. Read the expectations JSON file from the path provided by the user.
2. Validate required fields: candidate, target_roles, locations, current_situation.
3. Determine country: read country from expectations JSON (default: "gb" if absent).
4. Determine sector: read sector from expectations JSON (default: "industry" if absent). Valid values: "industry", "academia".
5. Read the shared INI config from ${PLUGIN_ROOT}/config/job-hunter.ini.
6. Read the country-specific INI from ${PLUGIN_ROOT}/config/country-{country}.ini (overlays shared config).
7. If sector=academia: load [platforms_academia] and [platform_urls_academia] sections instead of [platforms]. Also load [academic_salary_grades] for salary parsing.
8. Resolve candidate document for scoring context: a. If candidate.resume_path is provided:
   • Detect format from extension: .tex, .pdf, .md, .txt, .docx, .doc
   • For .tex, .pdf, .md, .txt: read directly with the Read tool (plain text or native PDF support).
   • For .docx or .doc: run the extraction utility first:

     python "${PLUGIN_ROOT}/scripts/process_jobs.py" \
       --stage extract-resume \
       --resume "{resume_path}" \
       --output-dir "{working_dir}"
     

     Then read the resulting -extracted.txt file with the Read tool. b. If candidate.profile_path is also provided AND the file exists: read it as supplementary context (backward compatible). c. If neither path is provided or files don't exist: WARN the user and proceed with skills-only matching (from candidate.skills or INI [candidate_skills]). d. Store the resolved readable path in state.json as "candidate_document_path".
9. Load candidate skills from candidate.skills in the expectations JSON (fallback to INI [candidate_skills] for backward compat).
10. Create the working directory and initialize state.json.
11. Report to user: "Country: {country}. Sector: {sector}. Found N target roles, M locations. DEB Cloud: {connected/degraded}. Working directory: {path}. Proceeding."

Step 2: Build Search Matrix and Estimate Credits

1. For each target role, get the search_keywords array (use first keyword as primary).

2. Translate keywords if needed — search keywords MUST be in the language of the target country. If the user provided English keywords for a non-English country (e.g. "Data Engineer" for FR), translate them (e.g. "Ingénieur Data"). Include both the translated and original as separate keywords for broader coverage. Common FR translations: internship=Stage, engineer=Ingénieur, developer=Développeur, senior=Senior (same), manager=Responsable.

3. Combine all roles with all cities from both P1 and P2 groups.

4. Select platforms — read [platforms] from the country INI first, then fall back to shared INI:

   • default: priority-ordered platform list (used for paid plans).
   • free: reduced platform list (used for free plan).
   • If plan=free (from ping response): use the free list and only the first keyword per role (free_max_keywords=1). The user can request more platforms, but warn them about credit impact and show the estimate before proceeding.
   • All other plans: use the default list. The user can request additional platforms; show the estimate so they can decide.
   • GB (academia): use [platforms_academia] from country INI instead.

5. Only include platforms that are also enabled (=1) in the country INI [platforms] section.

6. Build the search matrix as a list of {query, platforms, location, country, min_salary} entries.

7. Read max_pages_per_platform from [general] in the shared INI (default 2). This value is passed as max_pages to both estimate_credits and launch_scrape_jobs. Users can increase this for broader results, but warn about the credit impact.

Credit estimation (Gate 1): Call mcp__deb-jobhunter__estimate_credits with the search matrix and max_pages (from INI). This returns a breakdown of estimated credits (scraping, enrichment, scoring, reputation, total). Show the user the estimate and confirm before proceeding.

Step 3: Scrape Job Listings (Pipeline)

Requires DEB Cloud key. If degraded mode, skip this step and instruct the user to provide raw job data.

Platform codes (match INI platform names — server auto-selects country-specific URLs):

• GB industry: linkedin, indeed, reed, totaljobs, cwjobs, cvlibrary, adzuna
• GB academia: use mcp__deb-jobhunter__scrape_url for jobs.ac.uk and EURAXESS with specific URLs from [platform_urls_academia] INI section
• FR: linkedin, indeed, apec, hellowork

Launch scrape workers:

IMPORTANT: Use launch_scrape_jobs (async batch tool), NOT scrape_jobs (legacy single-call tool). The scrape_jobs tool is for ad-hoc single queries only — it blocks, saturates context, and doesn't track progress. The pipeline MUST use the async launch/poll/get pattern.

1. Call mcp__deb-jobhunter__launch_scrape_jobs with the search matrix, target_roles list, and max_pages (from INI).

   • Returns {batch_id, worker_count, estimated_credits}.
   • Workers run in parallel on the server — scraping, parsing, deduplication, and caching happen automatically.

2. Wait for completion: Call mcp__deb-jobhunter__poll_jobs(batch_id=...) once.

   • The server polls internally and streams progress via MCP progress notifications.
   • Returns when status is COMPLETE, PARTIAL, FAILED, or after timeout.
   • Response: {status, progress: {total, done, failed}, credits_consumed, tasks: [...]}.
   • If timed_out=true in response, call poll_jobs again to continue waiting.

3. Get results: Call mcp__deb-jobhunter__get_scrape_results(batch_id=...).

   • If response contains jobs key: use the Write tool to save the jobs array as JSON to {working_dir}/checkpoint-raw-combined.json.
   • If response contains download_url key: download to disk via curl:

     curl -s --ssl-no-revoke -o "{working_dir}/checkpoint-raw-combined.json" "{download_url}"
     

   • Each job includes listing_score (0-100) from the parser, used for filtering.
   • IMPORTANT: Never try to pipe MCP tool responses through Python stdin. Use Write for inline results or curl for downloads.

Report progress: "Scraping complete. {N} total listings from {platforms}. Credits used: {credits}."

Step 3.5: Deduplicate and Filter

1. Deduplicate via process_jobs.py:

python "${PLUGIN_ROOT}/scripts/process_jobs.py" \
  --stage dedup \
  --raw "{working_dir}/checkpoint-raw-combined.json" \
  --output-dir "{working_dir}"

Saves checkpoint-dedup.json.

2. First-layer filter — drop low-relevance jobs before enrichment:

python "${PLUGIN_ROOT}/scripts/process_jobs.py" \
  --stage filter \
  --dedup "{working_dir}/checkpoint-dedup.json" \
  --expectations "{expectations_path}" \
  --filter-threshold 40 \
  --output-dir "{working_dir}"

Uses listing_score from the parser + salary floor heuristic. Free — no API calls. Saves checkpoint-filtered.json.

Report: "After dedup: {N} unique. After filter: {M} jobs (dropped {K} low-relevance)."

Step 4: JD Enrichment (Gate 2)

Requires DEB Cloud key. Read [enrichment] from INI config. If enabled=0, skip to Step 5.

1. Prepare UUIDs for enrichment via process_jobs.py:
   • If plan=free: use free_max_enrich from INI [enrichment] section (default 250).
   • All other plans: enrich all filtered jobs (omit --max-enrich).

python "${PLUGIN_ROOT}/scripts/process_jobs.py" \
  --stage enrich-prep \
  --filtered "{working_dir}/checkpoint-filtered.json" \
  --max-enrich {N} \
  --output-dir "{working_dir}"

Outputs enrich-payload.json — JSON array of UUIDs for top N jobs by listing_score.

2. If ≤50 UUIDs (inline mode): read the UUIDs and call directly:
   • mcp__deb-jobhunter__launch_enrich_jobs(uuids=[...])
3. If >50 UUIDs (batch mode): a. Get file size: wc -c < "{working_dir}/enrich-payload.json" b. Call mcp__deb-jobhunter__init_enrichment(uuid_count=N, file_size=BYTES) c. Upload the file:

   curl -s --ssl-no-revoke -X PUT -H "Content-Type: application/json" \
     -H "Content-Length: BYTES" \
     --data-binary @"{working_dir}/enrich-payload.json" "{upload_url}"
   

   d. Call mcp__deb-jobhunter__launch_enrich_jobs(batch_id="...")
4. Returns {batch_id, worker_count, jobs_to_enrich}.
5. Wait for completion using mcp__deb-jobhunter__poll_jobs(batch_id=...) — single call, server streams progress.
6. Get results via mcp__deb-jobhunter__get_enrich_results(batch_id=...).
   • If response contains descriptions key: JD data returned inline. Use Write to save to {working_dir}/checkpoint-enrich-raw.json.
   • If response contains download_url key: download via curl:

     curl -s --ssl-no-revoke -o "{working_dir}/checkpoint-enrich-raw.json" "{download_url}"
     

   • Each JD has: responsibilities, requirements_hard, requirements_soft, tech_stack, seniority_signals, yoe_required, education.
7. Merge JD data back into the filtered jobs. For each enriched job, set:
   • jd_fetched: true, jd_status: "enriched"
   • JD fields populated from the enrichment response
8. For jobs not enriched (unavailable, failed), set jd_fetched: false, jd_status: "unavailable" or "failed".
9. Save merged result as checkpoint-enriched.json.

Report: "Enriched {N} of {M} jobs. {unavailable} JDs unavailable. Credits used: {credits}."

Step 5: Job Scoring (8 Dimensions)

Read scoring_mode from [scoring] in the INI config.

If scoring_mode = remote (requires DEB Cloud key):

1. Read the candidate document (resume/profile from Step 1).
2. Build profile text: concatenate resume content + candidate skills from [candidate_skills] in INI (max 8000 chars — summarize if needed).
3. Build expectations context from the expectations JSON: {target_roles, p1_cities, p2_cities, requires_visa, country, sector}
4. Read jobs from checkpoint-enriched.json (or checkpoint-filtered.json if enrichment was skipped).
5. If ≤15 jobs (inline mode):
   • Call mcp__deb-jobhunter__launch_score_jobs(jobs=[...], expectations={...}, profile="...").
6. If >15 jobs (batch mode via GCS): a. Get file size: wc -c < checkpoint-enriched.json b. Call mcp__deb-jobhunter__init_scoring(job_count=N, file_size=BYTES, expectations={...}, profile="...") c. Upload the file:

   curl -s --ssl-no-revoke -X PUT -H "Content-Type: application/json" \
     -H "Content-Length: BYTES" \
     --data-binary @"{working_dir}/checkpoint-enriched.json" "{upload_url}"
   

   d. Call mcp__deb-jobhunter__launch_score_jobs(batch_id="...")
7. Wait for completion using mcp__deb-jobhunter__poll_jobs(batch_id=...).
8. Get results via mcp__deb-jobhunter__get_score_results(batch_id=...).
   • If response contains scored_jobs: use Write to save to {working_dir}/checkpoint-scored.json.
   • If response contains download_url: download via curl:

     curl -s --ssl-no-revoke -o "{working_dir}/checkpoint-scored.json" "{download_url}"
     

9. Scoring returns 8 dimensions: skill_match, requirements_match, role_match, experience_match, seniority_match, salary_match, location_priority, sponsor_match.
   • For enriched jobs: all 8 dimensions scored.
   • For non-enriched jobs: requirements_match and experience_match are null, skill_match defaults to 50.
10. Filter out jobs with match_score < 30.

If scoring_mode = local:

1. Run the full pipeline locally which includes scoring:

   python "${PLUGIN_ROOT}/scripts/process_jobs.py" \
     --stage all \
     --raw "{working_dir}/checkpoint-raw-combined.json" \
     --expectations "{expectations_path}" \
     --config "${PLUGIN_ROOT}/config/job-hunter.ini" \
     --output-dir "{working_dir}"
   

2. This runs dedup + local heuristic scoring + financial calc + Excel in one pass.
3. Output: checkpoint-scored.json and Excel workbook.
4. Skip Steps 5.5 and 6 (already included in --stage all).

Update state.json: Set step_5_complete: true, current_step: 5.5, updated_at: <now>.

Report: "Scored {N} jobs. {kept} with match_score >= 30, {removed} discarded."

Step 5.5: Company Checks (Reputation + UKVI)

Enrich scored jobs with employee review ratings AND visa sponsor status in one step.

Requires DEB Cloud key. If degraded mode, skip this step.

1. Launch company checks using the scoring batch ID:

   • Call mcp__deb-jobhunter__launch_company_checks(score_batch_id="...").
   • The server extracts unique companies from scored jobs (match_score >= 30), runs reputation lookup + UKVI sponsor check in batches of 15.
   • Returns {batch_id, worker_count, companies_count}.

2. Wait for completion using mcp__deb-jobhunter__poll_jobs(batch_id=...).

3. Get results via mcp__deb-jobhunter__get_company_check_results(batch_id=...).

   • If response contains companies: save to {working_dir}/company-checks.json.
   • If response contains download_url: download via curl.
   • Each entry has: company, rating, review_count, source, reputation_status, is_sponsor, sponsor_route.

4. Merge into scored jobs via process_jobs.py:

python "${PLUGIN_ROOT}/scripts/process_jobs.py" \
  --stage reputation-merge \
  --scored "{working_dir}/checkpoint-scored.json" \
  --reputation-data "{working_dir}/company-checks.json" \
  --output-dir "{working_dir}"

Adds company_rating, rating_reviews, rating_source, is_sponsor, sponsor_route to each job.

Update state.json: Set step_5_5_complete: true, current_step: 6, updated_at: <now>.

Report: "Company checks complete. {rated} companies rated, {sponsors} sponsors found."

Step 6: Financial Viability and Excel Export

Before running the processing script, fetch commute cost data:

1. Commute cost data (dynamic, replaces INI commute tables):

   • Extract unique city names from checkpoint-scored.json location field
   • Normalize cities: strip postcodes, "England, United Kingdom", "Area" suffixes. E.g. "London EC2A 2AP" → "London", "Manchester, England, United Kingdom" → "Manchester". Deduplicate after normalizing.
   • Skip the candidate's home city (commute = 0)
   • Read origin from expectations JSON: candidate.home_city
   • Call mcp__deb-jobhunter__launch_commute_jobs(origin, destinations, country) with the normalized list
   • Poll with mcp__deb-jobhunter__poll_jobs(batch_id) until complete
   • Call mcp__deb-jobhunter__get_commute_results(batch_id) to retrieve results
   • If results contain download_url, download and save; otherwise save routes array directly
   • Save to {working_dir}/commute-data.json
   • Pass --commute-data flag to process_jobs.py (overrides INI commute tables)

2. Run the excel stage of the processing script. The checkpoint-scored.json MUST be a flat JSON array of jobs (not wrapped in scored_jobs/stats). Each job must have ALL fields from dedup + score fields from scoring. If it's a dict with scored_jobs key, the merge step (Step 5f) was skipped — go back and fix it.

python "${PLUGIN_ROOT}/scripts/process_jobs.py" \
  --stage excel \
  --scored "{working_dir}/checkpoint-scored.json" \
  --expectations "{expectations_path}" \
  --config "${PLUGIN_ROOT}/config/job-hunter.ini" \
  --commute-data "{working_dir}/commute-data.json" \
  --output-dir "{working_dir}"

If DEB Cloud is unavailable (degraded mode), omit --commute-data flag. The script will fall back to INI commute tables.

The script reads the LLM-scored checkpoint and:

1. Filters out remaining jobs with match_score < 30
2. For jobs with listed salary: calculates tax, social contributions (NI for UK / cotisations for FR), net monthly, commute costs, financial viability — using country-specific formulas
3. Computes composite_score = match_score × 0.60 + financial_score × 0.40
4. Visa sponsor and agency status already set on jobs by company checks MCP (Step 5.5)
5. Generates formatted Excel workbook with role-based tabs (currency from expectations: GBP/EUR)

The Excel workbook contains:

• One tab per target role (sorted by priority): P1 jobs first, then P2, ranked by composite_score
• All Results: Every scored listing (uncapped)
• Summary: Statistics and metadata

Update state.json: Set step_6_complete: true, updated_at: <now>.

Report to user:

• Total scored / after filtering / viable counts
• Platforms used and any that failed
• Top 5 results per role (title, company, salary, composite_score, viable, is_sponsor)
• Excel file path

---

Terminal Output

Read terminal_mode from [output] in the INI config. If pipeline (default), follow the exact formatting below. If standard, skip this section entirely and use your normal output style.

When terminal_mode = pipeline: Follow this exact output format to give users a polished pipeline experience. Print each block as markdown text between tool calls. Use Unicode block characters for progress bars and emoji for status icons.

Progress bar helper — use this pattern for all bars:

filled = "█" * int(pct / 5)    # 20 chars = 100%
empty  = "░" * (20 - len(filled))
bar = f"[{filled}{empty}]"

After Step 2 (estimate_credits):

🔍 Search matrix: {roles} roles × {cities} cities × {platforms} platforms = {workers} parallel workers
🔥 Estimated cost: ~{total} credits  |  Quota: {remaining} remaining ✅

Where remaining = ping.monthly_credits - ping.credits_used. Show ✅ if remaining > total, ⚠️ if remaining < total * 1.2, ❌ if remaining < total.

Step 3 (scraping):

Print before calling poll_jobs(batch_id):

🔄 Scraping [{bar}] {pct}%  |  {done}/{total} workers
  {platform} / {location}  {icon}  {result_count} jobs    {credits} cr
  {platform} / {location}  {icon}  {result_count} jobs    {credits} cr
  Credits consumed: {credits_consumed}

The server streams progress via MCP notifications — the progress bar updates as workers complete. When poll_jobs returns, render the final state with all tasks showing ✅/❌.

Where:

• pct = int(done / total * 100)
• Per-task icon: ✅ if COMPLETE, ⏳ if RUNNING, ❌ if FAILED, ⬜ if QUEUED
• Per-task data comes from poll_jobs.tasks[]: result_count, credits fields

After Step 3 + 3.5 (scraping + dedup + filter complete):

🔍 Scraping complete in {elapsed}  →  {raw_total} jobs raw  |  dedup: {dedup_count}  |  filter: {filter_count}

Where elapsed = time since launch_scrape_jobs call (format: 42s, 1m 23s, 2m 05s).

Step 4 (enrichment):

Print before calling poll_jobs(batch_id):

🔬 Enriching [{bar}] {pct}%  |  {done}/{total} workers
  Worker 1  {icon}  {result_count} JDs    {credits} cr
  Worker 2  {icon}  {result_count} JDs    {credits} cr
  Worker 3  {icon}  {result_count} JDs    {credits} cr
  Credits consumed: {credits_consumed}

When poll_jobs returns, render the final state with all workers showing ✅/❌:

🔬 Enriching [████████████████████] 100%  |  {done}/{total} workers
  Worker 1  ✅  {result_count} JDs    {credits} cr
  Worker 2  ✅  {result_count} JDs    {credits} cr
  Worker 3  ✅  {result_count} JDs    {credits} cr
  Credits consumed: {credits_consumed}

🔬 Enrichment complete in {elapsed}  →  {enriched}/{total} JDs fetched  ({unavailable} unavailable)

Step 5 (scoring):

Print before calling poll_jobs(batch_id):

📊 Scoring [{bar}] {pct}%  |  {done}/{total} workers
  Batch 1  {icon}  {result_count} jobs    {credits} cr
  Batch 2  {icon}  {result_count} jobs    {credits} cr
  Credits consumed: {credits_consumed}

When poll_jobs returns, render final state:

📊 Scoring [████████████████████] 100%  |  {done}/{total} workers
  Batch 1  ✅  {result_count} jobs    {credits} cr
  Batch 2  ✅  {result_count} jobs    {credits} cr
  Credits consumed: {credits_consumed}

📊 Scoring complete in {elapsed}  →  {scored}/{total} jobs scored

Step 5.5 (company checks):

Print before calling poll_jobs(batch_id):

🏢 Company checks [{bar}] {pct}%  |  {done}/{total} workers
  Batch 1  {icon}  {result_count} companies    {credits} cr
  Batch 2  {icon}  {result_count} companies    {credits} cr
  Credits consumed: {credits_consumed}

When poll_jobs returns, render final state:

🏢 Company checks [████████████████████] 100%  |  {done}/{total} workers
  Batch 1  ✅  {result_count} companies    {credits} cr
  Batch 2  ✅  {result_count} companies    {credits} cr
  Credits consumed: {credits_consumed}

🏢 Company checks complete in {elapsed}  →  {rated} rated, {sponsors} sponsors, {agencies} agencies

Step 6 (commute + financial + Excel):

Print before calling poll_jobs(batch_id):

🚗 Commute [{bar}] {pct}%  |  {done}/{total} workers
  Batch 1  {icon}  {result_count} cities    {credits} cr
  Batch 2  {icon}  {result_count} cities    {credits} cr
  Credits consumed: {credits_consumed}

When poll_jobs returns, render final state:

🚗 Commute [████████████████████] 100%  |  {done}/{total} workers
  Batch 1  ✅  {result_count} cities    {credits} cr
  Batch 2  ✅  {result_count} cities    {credits} cr
  Credits consumed: {credits_consumed}

🚗 Commute complete in {elapsed}  →  {found} routes found

Final summary (after Step 6):

✅ Done in {total_elapsed}  |  Total: {total_credits} credits  |  Remaining: {remaining}

📊 {excel_full_path}
   P1: {p1_count} jobs  |  P2: {p2_count} jobs  |  All: {all_count} jobs

🏆 Top 5:
   1. {title} @ {company} — score {composite_score}  ({salary_text}, {location})
   2. {title} @ {company} — score {composite_score}  ({salary_text}, {location})
   3. {title} @ {company} — score {composite_score}  ({salary_text}, {location})
   4. {title} @ {company} — score {composite_score}  ({salary_text}, {location})
   5. {title} @ {company} — score {composite_score}  ({salary_text}, {location})

Where:

• total_elapsed: wall-clock time from session start
• total_credits: sum of all credits charged across all tools
• remaining: updated quota after all charges
• excel_full_path: full absolute path to the Excel file (from process_jobs.py output)
• P1/P2 counts from process_jobs.py output
• Top 5: highest composite_score jobs from results, with salary and location

---

Important Constraints

• Windows encoding: On Windows, always use encoding='utf-8' when opening JSON files with Python (open(path, encoding='utf-8')). The default cp1252 encoding will fail on special characters in job data. When chaining curl download with Python verification in Bash, use separate commands.
• Respect rate limits: wait request_delay_seconds between requests to the same platform.
• If a platform blocks or errors, skip it and note the failure — do NOT retry.
• If salary is unlisted, still include the listing but flag it and skip financial calculation.
• Commute costs are dynamically fetched via launch_commute_jobs / get_commute_results MCP tools. If unavailable, the script falls back to INI config values.
• UKVI sponsor and agency status are set by company checks MCP tool (Step 5.5).
• If DEB Cloud key is missing or invalid, scraping and data lookups are unavailable. The user can provide their own raw job data for local processing.
• FR: Talent Passport visa checking is salary-threshold based — no sponsor list needed.
• Always save intermediate data — never rely on conversation context for preserving scraped results.

Reference Files

• references/scraping-strategy.md — Platform scraping instructions and parsing rules
• references/matching-algorithm.md — Scoring overview (local vs remote modes)
• references/csv-output-spec.md — Output column definitions and Excel formatting
• ${PLUGIN_ROOT}/config/job-hunter.ini — Shared configuration (general settings)
• ${PLUGIN_ROOT}/config/country-gb.ini — UK-specific: tax, NI, platforms, visa, commute
• ${PLUGIN_ROOT}/config/country-fr.ini — FR-specific: tax, social, platforms, visa, commute
• ${PLUGIN_ROOT}/scripts/process_jobs.py — Processing script (dedup, filter, financial, Excel export)
• ${PLUGIN_ROOT}/examples/job-expectations-example-gb.json — UK example input schema
• ${PLUGIN_ROOT}/examples/job-expectations-example-fr.json — French example input schema