# Shobr: Job seach CLI via browser automation, event-sourcing, LLM > Source: > Published: 2026-09-28 08:05:47+00:00 **The stealthiest, UNIX-iest, Job Search Automator, with a Hacker-in-the-Loop approach.** ## demo.mp4 In 2026's job market, there are many job application automation tools, some of them FOSS. This one's mine, and relies on: - [`beachpatrol`](https://github.com/sebastiancarlos/beachpatrol) for browser automation of your*own daily-driver browser* , and - [`roffume`](https://github.com/sebastiancarlos/roffume) for resume files management. - **Daily-Driver Stealth:** We don't use headless browsers. SHOBR uses[`beachpatrol`](https://github.com/sebastiancarlos/beachpatrol) to drive your existing, authenticated browser. To LinkedIn, you are just a normal user clicking around. - **Human-in-the-Loop:** SHOBR prepares, proposes, and verifies. It writes the drafts and builds the PDFs, but the final application submit is always done by you. This is*no "spray and pray"* , but you can still pray to any API-compatible deities. - **LLM-light By Design:** Automated, high-quality tuning of resume to job requires some LLM, there's not much leeway around it. But this project uses*as little LLM as possible* , and*doesn't demand an Agent driver* (like other projects in this space). If you want more, it should be trivial to ask an LLM to write a SKILL or an MCP server on top of SHOBR. - **LLM-provider-Agnostic:** Uses an LLM abstraction ([`any-llm`](https://github.com/mozilla-ai/any-llm) ). So, you can run SHOBR's LLM steps on OpenAI, Anthropic, a local model, or hijack a local LLM agent subscription via[`faaah`](https://github.com/sebastiancarlos/faaah) . - **Event-Sourced Data:** All data (discovered jobs, screenings, tracking) is saved in append-only JSONL event logs and projected into state files. You can interrupt the pipeline, or recompute lead approval with new rules, at any time without data loss. - **Markdown-Based CV Toolchain:** Resumes are tailored in Markdown and compiled to ATS-readable PDFs (via Groff and Pandoc). All deliverables are put in per-application folders. - **Full E2E Red-Green TDD:** Built with the stdlib's`unittest` , no extra framework. - **Not Vibecoded** : 3500 LOC (including comments) at time of writing. Somewhat atypical in this space. The **one hard requirement** of this project is [**`beachpatrol`**](https://github.com/sebastiancarlos/beachpatrol). You can think of it as a browser that you're meant to use as your daily driver, but which is *also* fully automatable (via a clever "Playwright wrapper" approach). Why `beachpatrol`? Well, job search requires scraping. Ideally scraping done using your *actual authenticated credentials*. So, what better way to avoid detection than using your *actual daily-driver browser to do the scraping*? (It should be virtually identical to regular use, provided you don't break any ToS). Other "job search automation tools" either use unauthenticated requests or headless browsers, or ask you to extract / copy your authenticated credentials into their automated browsers. Our `beachpatrol` approach aims to do them all one better by *using your actual daily-driver browser*. If you're interested, see [beachpatrol's README](https://github.com/sebastiancarlos/beachpatrol). With `beachpatrol` already setup, `shobr` requires Python >= 3.14 with [`uv`](https://docs.astral.sh/uv/): ``` git clone https://github.com/sebastiancarlos/shobr cd shobr uv sync # install the single runtime dependency, `any-llm-sdk[openai]` uv tool install . # Put the `shobr` CLI on `PATH` shobr --help ``` Because SHOBR leverages `roffume` to compile Markdown resumes into PDFs, you will need some standard Unix text-processing tools on your system: `groff` and `pandoc`. Then, in order: 1. Run `shobr setup` to scaffold the SHOBR config file under`$XDG_CONFIG_HOME/shobr/config.toml` , the profile templates, and to make`shobr` 's own`beachpatrol` commands available to`beachpatrol` (by symlinking them into the expected folder). Fill the config in. 2. Ensure you have one `beachpatrol` profile which is logged into LinkedIn. Put that*`beachpatrol` profile name* in`config.toml` on the`beachpatrol_profile` key. 3. For the parts of SHOBR requiring LLMs, `any-llm-sdk` reads provider keys from env (`OPENAI_API_KEY` ,`SHOBR_AI_MODEL` , and`OPENAI_BASE_URL` ). Naturally, you can use any LLM API provider you want through`any-llm-sdk` (or even hijack a locally available LLM agent subscription by using[`faaah`](https://github.com/sebastiancarlos/faaah) ). 4. For the parts of SHOBR requiring to read your **main CV** , you can refer to it via the env`SHOBR_MAIN_CV_PATH` (or see next step). 5. For the parts of SHOBR requiring authoring CVs and application directories, you need to configure a **CV toolchain.** - The first time you reach the `tailor` step,`shobr` will offer to clone the latest[`roffume`](https://github.com/sebastiancarlos/roffume) release into`~/shobr-resumes` (or point`cv_toolchain_dir` in`config.toml` at an existing checkout). This folder will keep track of all your resume variation inputs (markdown) and outputs (PDFs). - Then, the *main CV* defaults to`/resume.md` (`SHOBR_MAIN_CV_PATH` overrides). 6. The first time you reach the SHOBR breaks the job search process into *5 pipeline stages*. You can run: - **`shobr status`** - See details about every pipeline stage. - **`shobr next`** - Have SHOBR automatically prompt you for the next logical action across the entire pipeline (rather than running the manual, "plumbing" command directly). **Example `shobr status` output:** ``` bash $ shobr status - DISCOVERY - Total Leads Found: 59 - Rejected by Filter: 10 - Pending Enrichment: 3 - ENRICHMENT - Total Enriched: 48 - Rejected by Filter: 2 - Pending Screening: 24 - SCREENING - Total Screened: 45 - Skipped: 6 - Lacking LLM Review: 1 - Pending Human Review: 23 - LLM Scores: Human Scores: 5: 11 5: 2 (1 to tailor) 4: 9 4: 6 (5 to tailor) 3: 9 3: 4 (4 to tailor) 2: 8 2: 4 (4 to tailor) 1: 8 1: 6 - Pending Tailoring: 14 - TAILORING - Packages Built: 2 - Pending Review: 0 - TRACKING - Applied: 2 - Interviewing: 0 - Offer: 0 - Rejected: 0 - Ghosted: 0 - Withdrawn: 0 ``` Scrapes the LinkedIn job search results based on your `config.toml` keywords and locations, running them through a basic regex pre-filter. - **`shobr discovered`** - Prints the stored leads summary without fetching. - **`shobr discover`** - Triggers `beachpatrol` to search and scrape leads. - Triggers Visits individual job pages to extract full descriptions, salary ranges, and Easy Apply links. Done one at a time to pace requests and avoid rate-limits. - **`shobr enriched`** - Prints all enriched jobs. - **`shobr enrich-next`** - Fetches the detail page for the oldest non-enriched lead. - **`shobr enrich `** - Fetches a specific job. Scores enriched jobs against your personal Markdown profile and deal-breakers. - **`shobr screen-llm-next`** - Asks the LLM to score the next lead (1-5) and write reasoning. - **`shobr screen-llm-all`** - Batch runs the LLM against all unscored leads. - **`shobr screen-next`** - Records your own verdict for the next lead (score 1-5 plus reason), via `$EDITOR` or`--score` /`--reason` . - Records your own verdict for the next lead (score 1-5 plus reason), via For jobs marked "Pursue", SHOBR uses the LLM to rewrite your base `resume.md` to highlight relevant skills. It then uses the `roffume` (Groff/Pandoc) toolchain to ensure the rewrite perfectly fits on one page, looping rewrites if it overflows. - **`shobr tailor-next`** - Builds the application package (Resume + Cover Letter) for the next pursue-able job. - **`shobr tailored`** - Prints all generated packages. Local Kanban-style tracking for your applications. - **`shobr track [--note TEXT]`** - Updates pipeline status (` applied` ,`interviewing` ,`offer` ,`rejected` , etc.). - Updates pipeline status ( - **`shobr tracked`** - Prints a high-level overview of your entire funnel. Everything SHOBR knows about *you* lives under `$XDG_CONFIG_HOME/shobr/` (default `~/.config/shobr`): one `config.toml` plus a `profile/*.md` folder. `shobr setup` scaffolds all of them with instructional templates. ``` config.toml # filter rules, geo map, toolchain + browser wiring profile/ user-detail.md fit-criteria.md deal-breakers.md # screen-llm inputs resume-guide.md cover-guide.md # tailor-only inputs cv_toolchain_dir = "~/shobr-resumes" ``` Home of the *CV toolchain* (a `roffume` git checkout). The *main resume* defaults to `/resume.md`. The *CV toolchain* directory will ultimately contain all the generated CVs and other data, in its internal "per-application" directories. ``` beachpatrol_profile = "job-hunter" ``` `beachpatrol` browser profile holding the logged-in LinkedIn session. ``` beachpatrol_browser = "chromium" ``` `beachpatrol` browser to drive. ``` titles = ["Technical Lead", "Software Engineer", "Senior Software Engineer"] ``` Job titles fed to LinkedIn search as one ORed keyword query. Like "Software Engineer", "Fullstack Developer", etc. ``` workplace_types = ["on-site", "hybrid", "remote"] ``` Appended to the same search OR query. Possible values are: `on-site`, `hybrid`, `remote`. ``` geo = ["new-york-city", "san-francisco-bay-area"] ``` Geo targets for the query, referred to BY NAME through the `[geo_ids]` map. ``` [geo_ids] new-york-city = "111111111" san-francisco-bay-area = "222222222" ``` Maps each geo name to a LinkedIn geoId. The names are totally customizable, but should represent the name of a real-world location. You have to obtain the id directly from the LinkedIn Jobs URLs (`geoId=`), after performing a search for a given location. Note that LinkedIn often has several ids per place (city vs metro area). ``` reject_employment_type = ["Internship"] ``` Employment types rejected at enrichment. Possible values are: `Full-time`, `Part-time`, `Contract`, `Temporary`, `Internship`. ``` presence_locations = ["New York"] ``` Places acceptable for presence-required work. Values are literal strings of names of locations (matched case-insensitive). Remote postings pass anywhere. "On-site" and "hybrid" postings must name a listed location. ``` [reject_title] golang = "\\bgolang\\b" devops = "\\bdevops\\b" ``` Filters by pre-filter. Matched against job title. The leads are rejected with reason `title contains '