Job Market Intel logo

Job Market Intel

OrganizationPopular
tinyfish-io
job-market-intel

Get a live job market snapshot for any role and tech stack by deploying parallel TinyFish agents across LinkedIn, Indeed, and Glassdoor. Use when a developer wants real salary data, in-demand skills, top hiring companies, and remote ratios from actual job postings published this week — not survey averages or historical data. Triggers on phrases like "what is the market paying for", "salary range for", "is my salary competitive", "what should I ask for", "what skills do I need for", "who is hiring for", "how in-demand is", "before I negotiate", "job market for", "what does a [role] make", or any request to research compensation or demand for a specific role and tech stack before a job search or negotiation.

Overview

Publishertinyfish-io
Repositorytinyfish-cookbook
Skill namejob-market-intel
Stars
2.2K
Forks
333
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by tinyfish-io on GitHub. Read the source before you install it.

Installation

Install the Job Market Intel AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/tinyfish-io/tinyfish-cookbook.git /tmp/tinyfish-cookbook
mkdir -p .claude/skills
cp -r /tmp/tinyfish-cookbook/skills/job-market-intel .claude/skills/job-market-intel
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Job Market Intel in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Job Market Intel on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Job Market Intel is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Job Market Intel — Live Salary & Demand Data Before You Negotiate

You have access to the TinyFish CLI (tinyfish), a tool that runs browser automations from the terminal using natural language goals. This skill deploys agents across LinkedIn, Indeed, and Glassdoor in parallel, navigates login walls and search forms, and synthesizes live job listings into a single market snapshot.


Pre-flight Check (REQUIRED)

Before making any TinyFish call, always run BOTH checks:

1. CLI installed?

PowerShell:

powershell
Get-Command tinyfish; tinyfish --version

bash/zsh:

bash
which tinyfish && tinyfish --version || echo "TINYFISH_CLI_NOT_INSTALLED"

If not installed, stop and tell the user:

Install the TinyFish CLI: npm install -g @tiny-fish/cli

2. Authenticated?

bash
tinyfish auth status

If not authenticated, stop and tell the user:

You need a TinyFish API key. Get one at: https://agent.tinyfish.ai/api-keys

Then authenticate:

Option 1 — CLI login (interactive):

tinyfish auth login

Option 2 — PowerShell (current session only):

powershell
$env:TINYFISH_API_KEY="your-api-key-here"

Option 3 — PowerShell (persist across sessions):

powershell
[System.Environment]::SetEnvironmentVariable("TINYFISH_API_KEY", "your-api-key-here", "User")

Then close and reopen PowerShell for it to take effect.

Option 4 — bash/zsh (Mac/Linux):

bash
export TINYFISH_API_KEY="your-api-key-here"

Option 5 — Claude Code settings: Add to ~/.claude/settings.local.json:

json
{
  "env": {
    "TINYFISH_API_KEY": "your-api-key-here"
  }
}

Do NOT proceed until both checks pass.


What This Skill Does

Given a job title and tech stack (e.g. "Senior Backend Engineer, Go + Kubernetes"), this skill:

  1. Searches LinkedIn Jobs for matching postings, extracting salary ranges, seniority levels, and company names
  2. Searches Indeed for the same role, pulling compensation data and required skills from job descriptions
  3. Searches Glassdoor for salary data, company ratings, and remote/hybrid/onsite ratios

It then synthesizes all three sources into a structured market snapshot: what the role actually pays, which skills show up most, who is hiring, and whether remote is realistic.


Core Command

bash
tinyfish agent run --url <url> "<goal>"

Flags

FlagPurpose
--url <url>Target website URL
--syncWait for full result before returning
--asyncSubmit and return immediately
--prettyHuman-readable formatted output

Step-by-Step Workflow

Inputs Required

Before running, collect from the user:

  • Job title — e.g. Senior Backend Engineer, ML Engineer, Staff iOS Developer
  • Tech stack — e.g. Go Kubernetes, Python PyTorch, Swift SwiftUI
  • Location (optional) — e.g. San Francisco, Remote, London

Derive URL-encoded search strings from these inputs for each platform.


Step 1 — Search LinkedIn Jobs

bash
tinyfish agent run --sync \
  --url "https://www.linkedin.com/jobs/search/?keywords=<title+stack>&location=<location>&f_TPR=r604800" \
  "Extract the top 10 job listings as JSON: [{\"title\": str, \"company\": str, \"location\": str, \"salary_range\": str, \"posted_date\": str, \"remote_type\": str, \"url\": str}]. If salary is not shown, set salary_range to null. The f_TPR=r604800 filter means postings from the last 7 days — note the recency in each result."

Example for Senior Backend Engineer, Go + Kubernetes:

bash
tinyfish agent run --sync \
  --url "https://www.linkedin.com/jobs/search/?keywords=Senior+Backend+Engineer+Go+Kubernetes&location=Remote&f_TPR=r604800" \
  "Extract the top 10 job listings as JSON: [{\"title\": str, \"company\": str, \"location\": str, \"salary_range\": str, \"posted_date\": str, \"remote_type\": str, \"url\": str}]. Set salary_range to null if not listed."

Step 2 — Search Indeed

bash
tinyfish agent run --sync \
  --url "https://www.indeed.com/jobs?q=<title+stack>&l=<location>&fromage=7&sort=date" \
  "Extract the top 10 job listings as JSON: [{\"title\": str, \"company\": str, \"location\": str, \"salary_range\": str, \"required_skills\": [str], \"posted_date\": str, \"remote_type\": str, \"url\": str}]. Pull required_skills from the snippet or description preview shown in results. Set salary_range to null if not listed."

Example:

bash
tinyfish agent run --sync \
  --url "https://www.indeed.com/jobs?q=Senior+Backend+Engineer+Go+Kubernetes&l=Remote&fromage=7&sort=date" \
  "Extract the top 10 job listings as JSON: [{\"title\": str, \"company\": str, \"location\": str, \"salary_range\": str, \"required_skills\": [str], \"posted_date\": str, \"remote_type\": str, \"url\": str}]. Pull required_skills from visible description snippets."

Step 3 — Search Glassdoor

bash
tinyfish agent run --sync \
  --url "https://www.glassdoor.com/Job/jobs.htm?sc.keyword=<title+stack>&locT=N&locId=1&jobType=all&fromAge=7" \
  "Extract the top 10 job listings as JSON: [{\"title\": str, \"company\": str, \"company_rating\": str, \"salary_range\": str, \"location\": str, \"remote_type\": str, \"posted_date\": str, \"url\": str}]. Include company_rating (out of 5) if shown. Set salary_range to null if not listed."

Example:

bash
tinyfish agent run --sync \
  --url "https://www.glassdoor.com/Job/jobs.htm?sc.keyword=Senior+Backend+Engineer+Go+Kubernetes&locT=N&locId=1&jobType=all&fromAge=7" \
  "Extract the top 10 job listings as JSON: [{\"title\": str, \"company\": str, \"company_rating\": str, \"salary_range\": str, \"location\": str, \"remote_type\": str, \"posted_date\": str, \"url\": str}]."

Parallel Execution

All three steps are independent — run them simultaneously. Do NOT wait for LinkedIn before starting Indeed or Glassdoor.

bash/zsh — Parallel calls (Mac/Linux):

bash
tinyfish agent run --sync \
  --url "https://www.linkedin.com/jobs/search/?keywords=<title+stack>&location=<location>&f_TPR=r604800" \
  "Extract top 10 listings as JSON: [{\"title\": str, \"company\": str, \"location\": str, \"salary_range\": str, \"posted_date\": str, \"remote_type\": str, \"url\": str}]" \
  > /tmp/linkedin_results.json &

tinyfish agent run --sync \
  --url "https://www.indeed.com/jobs?q=<title+stack>&l=<location>&fromage=7&sort=date" \
  "Extract top 10 listings as JSON: [{\"title\": str, \"company\": str, \"location\": str, \"salary_range\": str, \"required_skills\": [str], \"posted_date\": str, \"remote_type\": str, \"url\": str}]" \
  > /tmp/indeed_results.json &

tinyfish agent run --sync \
  --url "https://www.glassdoor.com/Job/jobs.htm?sc.keyword=<title+stack>&locT=N&locId=1&jobType=all&fromAge=7" \
  "Extract top 10 listings as JSON: [{\"title\": str, \"company\": str, \"company_rating\": str, \"salary_range\": str, \"location\": str, \"remote_type\": str, \"posted_date\": str, \"url\": str}]" \
  > /tmp/glassdoor_results.json &

wait

PowerShell — Parallel calls using Start-Job / Wait-Job:

powershell
$linkedinJob = Start-Job {
    tinyfish agent run --sync `
      --url "https://www.linkedin.com/jobs/search/?keywords=<title+stack>&location=<location>&f_TPR=r604800" `
      "Extract top 10 listings as JSON: [{`"title`": str, `"company`": str, `"location`": str, `"salary_range`": str, `"posted_date`": str, `"remote_type`": str, `"url`": str}]"
}

$indeedJob = Start-Job {
    tinyfish agent run --sync `
      --url "https://www.indeed.com/jobs?q=<title+stack>&l=<location>&fromage=7&sort=date" `
      "Extract top 10 listings as JSON: [{`"title`": str, `"company`": str, `"location`": str, `"salary_range`": str, `"required_skills`": [str], `"posted_date`": str, `"remote_type`": str, `"url`": str}]"
}

$glassdoorJob = Start-Job {
    tinyfish agent run --sync `
      --url "https://www.glassdoor.com/Job/jobs.htm?sc.keyword=<title+stack>&locT=N&locId=1&jobType=all&fromAge=7" `
      "Extract top 10 listings as JSON: [{`"title`": str, `"company`": str, `"company_rating`": str, `"salary_range`": str, `"location`": str, `"remote_type`": str, `"posted_date`": str, `"url`": str}]"
}

Wait-Job $linkedinJob, $indeedJob, $glassdoorJob | Out-Null

$linkedinResults  = Receive-Job $linkedinJob
$indeedResults    = Receive-Job $indeedJob
$glassdoorResults = Receive-Job $glassdoorJob

Remove-Job $linkedinJob, $indeedJob, $glassdoorJob

Bad — Sequential calls (avoid):

bash
# Don't do this — three times slower, same results
tinyfish agent run --url "https://linkedin.com/..." "...also check Indeed and Glassdoor..."

Each platform is its own call. Always run in parallel.


Step 4 — Synthesize Into a Market Snapshot

Once all three sources return results, synthesize findings into this structure:

## Job Market Snapshot: <Role> — <Stack>
As of: <current date> | Sources: LinkedIn · Indeed · Glassdoor

### Salary Range
- Low end:   $<X>k   (entry/mid, from listings with explicit salary data)
- Midpoint:  $<X>k   (most common range across listings)
- High end:  $<X>k   (senior/staff/principal listings)
- Sample size: <N> listings with salary data out of <total> scraped

### Top Hiring Companies
| Company | Platform | Rating | Remote? |
|---------|----------|--------|---------|
| ...     | ...      | ...    | ...     |

### Most Required Skills
(ranked by frequency across all listings)
1. <skill> — seen in X/30 listings
2. <skill> — seen in X/30 listings
...

### Remote Ratio
- Remote:        XX%
- Hybrid:        XX%
- Onsite only:   XX%

### Demand Signal
- Total listings found this week: ~<N>
- Platform with most postings: <LinkedIn / Indeed / Glassdoor>
- Trend note: <any notable pattern — e.g. most postings are contract, or salary listed in <20% of roles>

### Raw Listings Sample
<3–5 representative listings with title, company, salary, and URL>

### Negotiation Takeaway
<2–3 sentences: what this data means in a negotiation context — floor, target, and stretch numbers based on the listings>

Only use data returned by TinyFish. Do not estimate or fill in salary figures not present in the results. If salary data is sparse (fewer than 5 listings show it), say so explicitly and note which platform surfaces it most.


Keyword Strategy

Search quality depends on how you phrase the query. Derive 2–3 variants from the user's input:

Role + StackPrimary keywordsVariant keywords
Senior Backend, Go + K8sSenior Backend Engineer Go KubernetesBackend Engineer Golang cloud-native, Platform Engineer Go
ML Engineer, PyTorchMachine Learning Engineer PyTorchML Engineer deep learning Python, AI Engineer LLM PyTorch
Staff iOS, SwiftStaff iOS Developer Swift SwiftUISenior iOS Engineer Swift, iOS Architect SwiftUI
Frontend, React + TypeScriptSenior Frontend Engineer React TypeScriptFrontend Developer React Next.js, UI Engineer TypeScript

If the first pass returns fewer than 10 total listings across all platforms, run a second parallel pass with a variant keyword set.


Handling Login Walls

LinkedIn and Glassdoor may prompt for login. TinyFish handles this automatically when authenticated sessions are stored. If an agent returns a login page instead of results:

  1. Run tinyfish auth status to verify your session
  2. Re-authenticate with tinyfish auth login and select the affected platform
  3. Re-run the failed step individually before re-joining the parallel flow

Indeed typically does not require login for search result extraction.


Managing Runs

bash
# List recent runs
tinyfish agent run list

# Get a specific run by ID
tinyfish agent run get <run_id>

# Cancel a running automation
tinyfish agent run cancel <run_id>

Output Format

The CLI streams data: {...} SSE lines by default. The final result is the event where type == "COMPLETE" and status == "COMPLETED" — extracted data is in the resultJson field. Read output directly; no custom parsing script needed.

Frequently asked questions

What does the Job Market Intel AI skill do?

Get a live job market snapshot for any role and tech stack by deploying parallel TinyFish agents across LinkedIn, Indeed, and Glassdoor. Use when a developer wants real salary data, in-demand skills, top hiring companies, and remote ratios from actual job postings published this week — not survey averages or historical data. Triggers on phrases like "what is the market paying for", "salary range for", "is my salary competitive", "what should I ask for", "what skills do I need for", "who is hiring for", "how in-demand is", "before I negotiate", "job market for", "what does a [role] make", or...

Why use Job Market Intel on TypingMind?

Because you install it once and use it with any model. Job Market Intel is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Job Market Intel in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tinyfish-io/tinyfish-cookbook/tree/main/skills/job-market-intel. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Job Market Intel?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Job Market Intel?

As many as you like. As long as a model supports skills, you can use Job Market Intel with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Job Market Intel AI skill free?

Yes. It is published on GitHub by tinyfish-io under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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