ClawHub

Swelist

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Helps you find tech internships and new-grad jobs, track every application locally, and prep for interviews — no sign-up needed

Install

openclaw skills install swelist

swelist

swelist is a job-seeker CLI for finding software engineering internships and new-grad positions in real time, plus a local SQLite tracker for every application — no account, no API key, no subscription required. It pulls live data from SimplifyJobs, filters by role, location, and timeframe, and pairs with an AI writing assistant (jobgpt) for interview prep.

When to Use This Skill

Trigger when the user asks to:

  • Find or browse internship openings ("show me tech internships this week", "any CS internships remote?")
  • Search for new-grad software engineering jobs ("new grad SWE jobs in NYC", "entry-level software engineer positions")
  • Filter job listings by location, company, or recency ("jobs in Seattle last 7 days", "remote internships last month")
  • Track a job application locally ("add Amazon SDE intern to my tracker", "log this application")
  • Update or check an application status ("mark Stripe offer as done", "set Google to rejected")
  • List or export tracked applications ("show all my applications", "export tracker as JSON")
  • Get AI interview prep answers ("why do you want to work at Meta?", "STAR answer for conflict question")
  • Get general career advice via jobgpt ("how do I negotiate salary?", "tips for my first internship")

Keywords: find internships, new grad jobs, job listings, job search, application tracker, track my application, update application status, swelist, job search CLI, software engineer jobs, tech internship, interview prep, STAR answer, why this company

Tool Identity

  • Name: swelist
  • Type: Command-Line Interface (CLI)
  • Language: Python
  • Distribution: PyPI
  • Execution Model:
    • swelist run — stateless, read-only (fetches live data, no local state)
    • swelist jobgpt — stateless (calls OpenAI API, no local state)
    • swelist tracker — stateful (reads/writes local SQLite at ~/.offerplus/applications.db)

Purpose

swelist is a job-seeker CLI with three subcommand groups:

  • swelist run — retrieves recently added technology internship and new‑graduate job postings from curated public GitHub repositories and renders them in a predictable, text-based format.
  • swelist jobgpt — AI-powered writing assistant for interview prep (behavioral answers, "why this company", general career Q&A).
  • swelist tracker — local SQLite application tracker; add, update, query, list, and export job applications without any external account.

It is optimized for: automation pipelines, periodic polling agents, and human-in-the-loop job search workflows.

Data Sources

  • SimplifyJobs / Summer2025-Internships
  • SimplifyJobs / New-Grad-Positions

Data is fetched live at runtime.

Installation

pip install swelist

Invocation Contract

swelist [--role ROLE] [--timeframe TIMEFRAME] [--location LOCATION]

The tool accepts only CLI flags. No stdin is consumed.

Parameters

--role

Controls which category of jobs to retrieve.

Value Meaning

internship Internship roles (default) newgrad New‑graduate roles

Example:

swelist --role newgrad
swelist --role internship --timeframe lastweek --location "Seattle, Remote"

--timeframe

Controls recency filtering.

Value Time Window

lastday Last 24 hours lastweek Last 7 days lastmonth Last 30 days

Example:

swelist --timeframe lastweek
swelist --role newgrad --timeframe lastmonth --location "New York, Boston"

--location

Filters job postings by geographic location.

Input Meaning

Single location Canada or Toronto Multiple locations (comma-separated) "Boston, New York, Remote" State code (2-letter) CA matches "San Francisco, CA"

Example:

swelist --location Toronto
swelist --location "Boston, New York, Remote"
swelist --role newgrad --timeframe lastweek --location "San Francisco, Remote"
swelist --role internship --timeframe lastmonth --location CA

tracker subcommand

Local SQLite tracker for job applications synced from Gmail.

Before invoking any tracker command, read ~/.claude/profile.md:

  1. Check Integrations > Tracker Backend — if tracker_backend is notion, the tracker subcommand is not applicable; use the application-manager skill instead.
  2. If tracker_backend is sqlite (or blank, defaulting to sqlite for local use), read SQLite DB path from profile.md. Use that value as --db PATH. Fall back to ~/.offerplus/applications.db only if the field is blank.
# Resolve DB_PATH from profile.md first, then:
swelist tracker init   [--db DB_PATH]
swelist tracker add    "<Company — Role>" --status <S> [--job-id <id>] [--applied-on YYYY-MM-DD] [--db DB_PATH]
swelist tracker update "<Company — Role>" --status <S> [--notes <text>] [--db DB_PATH]
swelist tracker get    "<Company — Role>" [--db DB_PATH]     # JSON; exit 1 + null if not found
swelist tracker list   [--status S] [--company C] [--db DB_PATH]
swelist tracker export [--format csv|json] [--db DB_PATH]

If the DB does not exist yet, run tracker init before any other tracker command.

tracker add output sample

Input: swelist tracker add "Stripe — Backend Engineer" --status "In progress" --job-id S789 --applied-on 2026-05-18

Added: Stripe — Backend Engineer → In progress

Input (duplicate):

Skipped (already exists): Stripe — Backend Engineer

tracker update output sample

Input: swelist tracker update "Amazon — SDE, AWS" --status "Rejected"

Updated: Amazon — SDE, AWS → Rejected

Input (not found):

Not found: Acme — Engineer

tracker get output sample

Input: swelist tracker get "Amazon — SDE, AWS"

{"name": "Amazon — SDE, AWS", "status": "Rejected", "job_id": "10414382", "applied_on": "2026-05-17", "notes": null, "updated_at": "2026-05-18 01:10:00"}

Input (not found) — exits with code 1:

null

tracker list output sample

Input: swelist tracker list

 Application                                    Status        Job ID      Applied       Updated
 Amazon — Software Development Engineer, AWS    In progress   10414382    2026-05-17    2026-05-17 18:30:00
 Amazon — Software Development Engineer         In progress   3146673     2026-04-21    2026-04-21 20:50:00
 Amazon — Software Development Engineer, Playback Team  Rejected  3083065  2025-12-02  2025-12-02 17:20:00

3 application(s)

Input: swelist tracker list --status "In progress" --company amazon

 Application                                    Status        Job ID      Applied       Updated
 Amazon — Software Development Engineer, AWS    In progress   10414382    2026-05-17    2026-05-17 18:30:00
 Amazon — Software Development Engineer         In progress   3146673     2026-04-21    2026-04-21 20:50:00

2 application(s)

tracker export output sample

Input: swelist tracker export --format json

[
  {
    "name": "Amazon — Software Development Engineer, AWS",
    "status": "In progress",
    "job_id": "10414382",
    "applied_on": "2026-05-17",
    "notes": null,
    "updated_at": "2026-05-17 18:30:00"
  },
  {
    "name": "Amazon — Software Development Engineer, Playback Team",
    "status": "Rejected",
    "job_id": "3083065",
    "applied_on": "2025-12-02",
    "notes": null,
    "updated_at": "2025-12-02 17:20:00"
  }
]

Input: swelist tracker export --format csv

name,status,job_id,applied_on,notes,updated_at
Amazon — Software Development Engineer,In progress,3146673,2026-04-21,,2026-04-21 20:50:00
Amazon — Software Development Engineer Playback Team,Rejected,3083065,2025-12-02,,2025-12-02 17:20:00

Output Contract

  • Output is written to STDOUT
  • Format is human- and agent-readable plain text
  • No JSON or structured serialization

Job Posting Fields

Each job entry contains:

  • Company (string)
  • Title (string)
  • Location (string) — may be a single string or a list
  • Link (URL)

Realistic output sample

Input: swelist --role internship --timeframe lastweek --location "Seattle, Remote"

Welcome to swelist.com
Last updated: Sun May 17 18:42:01 2026
Found 1284 tech internships from 2025Summer-Internships
Found 892 new-grad tech jobs from New-Grad-Positions
Sign-up below to receive updates when new internships/jobs are added

Found 3 postings for location 'Seattle, Remote' in TimeFilter.lastweek

Company: Amazon
Title: Software Development Engineer Intern
locations: ['Seattle, WA', 'Remote']
Link: https://www.amazon.jobs/en/jobs/2345678

Company: Microsoft
Title: Software Engineering Intern
locations: ['Redmond, WA', 'Remote']
Link: https://jobs.careers.microsoft.com/us/en/job/1234567

Company: Stripe
Title: Software Engineer, Intern
locations: ['Remote']
Link: https://stripe.com/jobs/listing/software-engineer-intern/9876543

Empty-result output

When no postings match the filters:

# No jobs in the selected timeframe:
No postings found in TimeFilter.lastday

# Jobs exist but none match the location:
No postings found for location 'Austin' in TimeFilter.lastday

Agents should treat either message as a zero-result signal and not retry with the same flags.

Execution Guarantees

  • No side effects
  • No persistent storage
  • Safe for repeated execution
  • Deterministic given identical upstream data
  • No authentication required

Error Behavior

  • Network issues may raise runtime errors or result in empty output
  • Invalid flags produce CLI usage errors
  • Zero matching jobs produces valid empty result output

Environment Requirements

  • Python 3.8+
  • Internet access
  • Supported on macOS, Linux, Windows

Agent-Oriented Use Cases

  • Daily polling for new internship postings
  • Weekly new‑grad job aggregation
  • Feeding results into ranking, scoring, or alerting agents
  • Execution via cron, CI pipelines, or autonomous agents
  • Parsing job postings into structured JSON for downstream processing
  • Integration with AI agents for automated job application workflows

Known Limitations

  • No built‑in alerting
  • No local caching
  • No deduplication beyond source data
  • No JSON output format

Safety & Compliance

  • Uses only public data
  • No user tracking
  • No credential usage
  • No scraping of private systems

Versioning

Behavior may evolve with upstream data sources. CLI flags are considered stable within a major version.

jobgpt

This document defines the operational capabilities, invocation contract, and usage semantics of the jobgpt command for AI-powered job application assistance.

Tool Identity

  • Name: jobgpt
  • Type: Command-Line Interface (CLI)
  • Language: Python
  • Distribution: Part of swelist package
  • Execution Model: Stateless, AI-powered writing assistant

Purpose

jobgpt is an AI writing assistant that helps job seekers prepare for interviews and job applications by generating compelling answers and career advice.

It is optimized for: - Interview preparation - Application materials - Career guidance

Subcommands

ask

Ask any career or job-search question.

jobgpt ask "Your question here"

Parameters:

  • question (positional, required): Any job-search or career question

Options:

  • --model: OpenAI model to use (default: gpt-4o)
  • --copy: Copy output to clipboard (flag)

Example:

jobgpt ask "What should I focus on in my first internship?"
jobgpt ask "How do I negotiate salary?" --copy

Sample output:

╭─ Career Advice ────────────────────────────────────────────────────╮
│                                                                     │
│  Focus on three things in your first internship:                   │
│                                                                     │
│  1. **Shipping something real** — even a small feature that goes   │
│     to production teaches you more than any tutorial.              │
│  2. **Building relationships** — your intern cohort and your       │
│     manager are your longest-lasting professional network.         │
│  3. **Asking good questions** — juniors who ask precise, well-     │
│     researched questions are remembered positively.                │
│                                                                     │
╰─────────────────────────────────────────────────────────────────────╯

why-company

Generate a compelling answer to "Why do you want to work at [Company]?"

jobgpt why-company "Company Name" --background "Your background"

Parameters:

  • company (positional, required): Company name

Options:

  • --background: Your background summary (optional, recommended)
  • --model: OpenAI model to use (default: gpt-4o)
  • --copy: Copy output to clipboard (flag)

Example:

jobgpt why-company "Google" --background "Software Engineering student with Python and web development experience"
jobgpt why-company "Meta" --background "Full-stack developer with React and Node.js expertise" --copy

behavioral

Generate a STAR-format answer to a behavioral interview question.

jobgpt behavioral "Question here" [--resume path/to/resume.txt]

Parameters:

  • question (positional, required): The behavioral interview question

Options:

  • --resume: Path to resume text file (optional)
  • --model: OpenAI model to use (default: gpt-4o)
  • --copy: Copy output to clipboard (flag)

Example:

jobgpt behavioral "Tell me about a time you dealt with conflict on a team"
jobgpt behavioral "Describe your biggest failure and how you learned from it" --resume resume.txt --copy

Sample output:

╭─ STAR Answer ───────────────────────────────────────────────────────╮
│                                                                      │
│  **Situation:** During my internship at Acme, our team disagreed    │
│  on the API design for a new service — two engineers wanted REST,   │
│  one pushed for GraphQL.                                            │
│                                                                      │
│  **Task:** As the engineer writing the initial spec, I needed to    │
│  drive us to a decision before our sprint planning the next day.    │
│                                                                      │
│  **Action:** I drafted a one-page trade-off doc comparing latency,  │
│  client complexity, and team familiarity for both options, then     │
│  scheduled a 30-minute sync. I proposed REST with a versioning      │
│  convention that addressed the GraphQL advocate's flexibility       │
│  concerns.                                                          │
│                                                                      │
│  **Result:** We reached consensus in 20 minutes. The service        │
│  shipped on time and has had zero breaking-change incidents in      │
│  six months.                                                        │
│                                                                      │
╰──────────────────────────────────────────────────────────────────────╯

Output Contract

  • Output is written to STDOUT
  • Format is human-readable markdown with rich text formatting
  • Organized in clear panels with proper section headers
  • Optimized for reading and copying to clipboard

Execution Guarantees

  • Requires OpenAI API key (set via OPENAI_API_KEY environment variable)
  • No persistent storage
  • Safe for repeated execution
  • Deterministic given identical input (within model capabilities)

Error Behavior

  • Missing OPENAI_API_KEY environment variable produces clear error message
  • Invalid OpenAI model names produce API errors
  • Missing resume file produces OSError with clear message
  • Network issues may raise runtime errors

Environment Requirements

  • Python 3.8+
  • OpenAI API key (OPENAI_API_KEY)
  • Internet access for API calls
  • Supported on macOS, Linux, Windows

Agent-Oriented Use Cases

  • Preprocessing candidate data for interview coaching bots
  • Generating interview preparation materials at scale
  • Feeding career advice into autonomous career planning agents
  • Integration with resume builders and application workflows

Known Limitations

  • Requires OpenAI API key and internet connection
  • Responses vary based on model capability
  • No local caching of responses
  • No built-in follow-up question handling

Safety & Compliance

  • Uses only public OpenAI API
  • No data persistence
  • No credential sharing or exposure
  • Respects OpenAI usage policies

End of document.