Job Search DE
Personal / Career Intelligence
Find roles in Germany, review the evidence for fit, and track applications in one workbench.
- Discover
- Verify
- Evaluate
- Track
Your shortlist.
Germany AI & engineering
- ML Infrastructure EngineerBerlin · Hybrid88Selected for review
- Senior Platform EngineerMunich · Hybrid82
- Applied AI EngineerGermany · Remote76
Why this role?
ML Infrastructure Engineer
- Direct match
- Python and PyTorch in a deployed ML service.
- Transferable
- Container deployment experience supports platform work.
- Confirm next
- Production Kubernetes ownership is not established.
Illustrative workflow · Sample roles and evidence · No live job requests
In brief
An agent skill that collects job listings, checks source availability, compares requirements with candidate evidence, and exports a workbench for reviewing roles and tracking applications.
An open-source skill for coding agents, with a standalone browser workbench.
Job discovery, fit assessment, and application tracking often happen in separate tools. This project brings them into one workflow that an existing coding agent can run.
The skill supports job searches across professions in Germany. The public workbench illustrates the workflow with technology roles and sample scores.
A useful shortlist needs more than matching keywords: it needs source links, freshness checks, explicit candidate evidence, and a way to track the next action. Missing information should remain visible rather than becoming an invented match.
What it includes
- 01Collects openings from company career feeds, with supplementary job-board sources for broader coverage.
- 02Checks URL responses and posting metadata to flag expired, inaccessible, or uncertain listings.
- 03Uses criteria-based triage followed by requirement-by-requirement profile evidence, including partial matches and unknowns.
- 04Exports a standalone workbench with search, sorting, application tracking, notes, and four visual themes.
- 05Stores profile and configuration files in the workspace; workbench status and notes persist in browser storage.
How it works
- 01Build the profile
extract candidate evidence and record search preferences and constraints.
- 02Discover roles
collect company career feeds and supplementary sources into a common format.
- 03Check listings
inspect URL responses and available posting dates; retain uncertainty when evidence is missing.
- 04Evaluate fit
triage against criteria, then compare individual requirements with profile evidence.
- 05Review and act
export the workbench and report, review the shortlist, and track applications.
Explore the pipeline illustration
Find roles. Review fit. Track what follows.
Collect openings, check their sources, and compare requirements with profile evidence before adding them to your application shortlist.
Company career feeds
Normalize company career feeds and supplementary discovery sources.
Check listing freshness
Inspect URL responses and posting dates; flag missing or uncertain evidence.
- URL respondsReachable page; hiring status still needs review
- Posting metadataRead JobPosting structured data when available
- Freshness windowCompare dates with the configured threshold
- Unavailable listingFlag broken or expired source links
Review fit evidence
Triage first, then record matches, transferable skills, gaps, and unknowns.
“Built production LLM pipelines using LangGraph and PyTorch.”
Application workbench
Filter the shortlist, inspect details, and record your next application step.
Illustrative roles, scores, and profile evidence. This diagram does not fetch live jobs.
Build notes
Keep listing provenance visible
source verification- Context
- Duplicate and outdated listings make a shortlist difficult to trust.
- Approach
- Prefer company career feeds, retain source links, and check available posting metadata. Supplementary sources broaden discovery.
- What it enables
- Listings can be traced back to their source. A successful URL check does not establish that an employer is actively hiring.
Separate triage from evidence review
evidence scoring- Context
- One overall match score can hide missing requirements and unsupported assumptions.
- Approach
- Use an initial criteria-based pass, then record direct matches, partial matches, gaps, and unknowns for the shortlist.
- What it enables
- The score has an explanation that a candidate can review. It is a rubric score, not a statistically calibrated probability.
Export a browser workbench
standalone workbench- Context
- A terminal report is awkward for comparing roles and maintaining application status.
- Approach
- Generate an HTML workbench with embedded data, four themes, and browser storage for status and notes.
- What it enables
- The exported shortlist can be reviewed without rerunning the agent. New discovery and scoring still require the agent workflow.
System capabilities
- 2
- Evaluation stages
DefinitionCriteria triage followed by detailed evidence scoring; scores are prioritization aids, not hiring probabilities.
- 4
- Workbench themes
DefinitionEditorial Craft, Dark Velocity, Industrial Precision, and Spatial Quartz.
Compatibility
- Coverage depends on configured sources, public endpoint availability, and changing response formats. Freshness checks cannot identify every ghost listing or repost.
- No separate LLM API key is needed by the skill, but the host agent may have usage costs and may process profile content through its model provider.
- Workbench notes and status are stored in the current browser and do not automatically sync across devices. Profile changes require saving or exporting the configuration files.
- The public demo contains illustrative roles, dates, and scores; its links lead to company career pages and do not verify the displayed openings. Web fonts may require a network connection.