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Agent skill

AI Job Scout

by luongnv89 in luongnv89/skills

Find open AI engineering jobs that fit a candidate, verify location and apply links, and assess each employer's product.

MITAuto-check passedBusiness, Finance & HR

Install AI Job Scout

skills CLI
$ npx skills add luongnv89/skills --skill ai-job-scout -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install luongnv89/skills ai-job-scout --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/luongnv89/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai-job-scout .claude/skills/ai-job-scout && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ai-job-scout
GitHub stars
131
Token cost
~2.5k tokens
SKILL.md length
1,412 words
Files
5 (incl. references)
Skills in repo
35
Repo updated
First seen
Licence
MIT

At a glance

Find open AI engineering jobs that fit a candidate, verify location and apply links, and assess each employer's product.

  • Works in 4 steps: Derive a candidate-fit brief → Discover and verify live listings → Research each company's product and the… → …
  • One-off shortlists
  • SKILL.md covers When to Use, Instructions, Inputs and branches and 1. Derive a candidate-fit brief, plus 8 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

AI Job Scout is an agent skill from luongnv89/skills. Find open AI engineering jobs that fit a candidate, verify location and apply links, and assess each employer's product. Use for one-off shortlists or a job alert's research step. Don't use for résumé writing, job applications, or company news.

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including reference files (for example `docs/README.md`, `evals/evals.json` and `references/interactive-report.md`).

It sits in Business, Finance & HR, covering Job search and resumes. The repository describes itself as: Supercharge your AI agents/bots with reusable skills. The licence is MIT.

When your agent uses it

  • One-off shortlists
  • A job alerts research step
  • Job applications

Example prompts

  • “s product. Use for one-off shortlists or a job alert”
  • “/ai-job-scout”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Derive a candidate-fit brief
  2. Discover and verify live listings
  3. Research each company's product and the role's project
  4. Rank and report

What it can do on your machine

Read from SKILL.md and the folder at commit b5ef695. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

AI Job Scout loads about 2.5k tokens when it runs, and up to ~3.9k if it reads all its reference files. Until then it costs about 64 tokens; SKILL.md has 1,412 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~64
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from luongnv89/skills at commit b5ef695, republished under its MIT licence (© luongnv89). 1,412 words, ~2,488 tokens.

Download SKILL.mdSave it as .claude/skills/ai-job-scout/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
ai-job-scout
description
Find open AI engineering jobs that fit a candidate, verify location and apply links, and assess each employer's product. Use for one-off shortlists or a job alert's research step. Don't use for résumé writing, job applications, or company news.
license
MIT
effort
high
metadata.version
1.1.1
metadata.author
Luong NGUYEN

AI Job Scout

Find the requested number of open roles (three by default) that match the candidate, then examine the company and project, not just the job ad. Research-only: never apply, contact recruiters, or change a job tracker without a separate request.

When to Use

Use when a candidate asks for an AI job shortlist, the research step of an already configured job alert, or whether the projects behind open roles are worth pursuing. The skill produces a report, not a schedule: use a scheduler only when the user asks, and never claim a schedule exists without verifying it.

Instructions

Run steps 1–4 in order. Research a company (step 3) only for roles that passed step 2. After each step, print its Step Completion Report.

Terms used throughout:

  • Verified listing: the role's page on the employer's careers site or its ATS (Greenhouse, Lever, Ashby, Workable, and similar), opened and read on the run date. Aggregators (LinkedIn, Welcome to the Jungle, remote boards) and search snippets only help discovery; they are never a verified listing.
  • AI-central: the listing's responsibilities make building, evaluating, securing, or operating AI/ML systems a primary duty.
  • Prior report: an earlier report or list of listing URLs that the user supplied or that this run can open.
  • Near-miss: a shortlisted role that failed the step 3 gate; report it with its evidence, and never rank it.

Inputs and branches

  • Candidate: Public GitHub/profile URL and any résumé or constraints the user has supplied. If no profile is available, ask for one before any personal-fit claim. If none arrives, or no one can answer (an unattended alert run), label the search unpersonalized. Re-read the public profile on each run; do not store a biography in this skill.
  • Geography: Build the location rule, the candidate's accepted work arrangements as testable conditions, from the user's work arrangement and home location. Hybrid in a city: admit a role only when its verified listing names that city and states an office-attendance policy. Fully remote: admit a role only when its verified listing says applicants in the user's country are eligible. Example: "Paris hybrid or fully remote from France" admits Paris hybrid roles with a stated attendance policy and remote roles open to France-based applicants. "Remote" with no eligible country or region is unknown, not worldwide. If hybrid is only inferred (for example, from where the candidate lives), label the fit conditional and ask the candidate to confirm.
  • Cadence: If a recurring alert has a prior report, compare against it and skip repeats unless the listing materially changed. If no prior report is available, state that deduplication was not verified; do not claim every pick is new.
  • Count: Default to three roles; use the user's count when given. Fewer qualifying roles is a valid result.
  • Format: Write the text report by default. If the count is above five, or the user asks to filter roles (for example by salary or remote policy), read references/interactive-report.md and build the HTML report instead, unless the user asks for text. If HTML cannot be produced here, say so and write the text report.

1. Derive a candidate-fit brief

Read the profile and note supported seniority, projects, languages, AI specialties, adjacent domain experience, and geographic constraints with links. Distinguish observed repository code/activity from a README's self-description and from independently unverified career claims. Do not infer work authorization outside the candidate's home country, education, spoken languages, relocation willingness, or production deployment from project descriptions alone.

Gate: The brief records at least one source for each positive fit claim and the location rule. Missing essential constraints are listed, not guessed.

2. Discover and verify live listings

  1. Search employer career pages and ATS boards for several role families that match the brief.
  2. Open the verified listing for each candidate role. If the page is blocked, retry with another retrieval method or the employer's ATS endpoint, then with an interactive browser. If every method fails, reject the role.
  3. Open the application form when one exists. A reachable application tab on the listing URL counts as a working apply path.
  4. Record from the verified listing: title, employer, responsibilities, required seniority, location/remote eligibility (quoted), posting date or "unknown", and salary and equity or "not listed". Do not turn an old posting date into a new one.
  5. Apply the hard rejects. Record each rejected role with the check it failed.

Hard reject: unreachable verified listing; closed or missing application; the location rule fails or remote eligibility stays ambiguous; the role is not AI-central; a junior or intern role outside the requested level; a prior report's listing with no material change.

Gate: Every shortlisted role has a verified listing URL and a quoted location policy that passes the location rule. Never weaken a hard reject to reach the count.

Show full SKILL.md (616 more words)Show less

3. Research each company's product and the role's project

For each shortlisted role, read the employer's product/about pages, docs or engineering blog; consult independent coverage only where it adds substantiated context. Explain:

  1. The product, its users or problem, and the role's documented responsibilities. Name the specific assigned project only when the listing or another employer source identifies it; otherwise label the assignment unknown.
  2. Where AI actually fits in the product and what is technically distinctive versus generic AI marketing.
  3. An interestingness verdict for this candidate: High, Medium, or Low, supported by concrete engineering problems, not prestige or funding alone.
  4. Unknowns or risks (e.g. unclear product maturity, vague AI remit, domain mismatch). Include funding, customer counts, revenue, and stage only when sourced and relevant; never infer traction from a polished site.

Gate: At least one directly read company/project source beyond the job ad supports the product description for every ranked role. If none can be read, report the role as a near-miss instead of ranking it.

4. Rank and report

Rank by (in order) hard-gate certainty, relevant demonstrated skills, centrality of AI work, strength of the actual project, and posting recency. Do not assign numerical scores. State why #1 beats #2, including tradeoffs.

Read references/report-format.md and draft the report in its order: Result (first line: Complete, Partial, or None, with K of N roles), Search evidence, Ranked roles, Uncertainty, and Decision ("No approval needed" plus the candidate's next actions).

  1. Check the draft against the Acceptance Criteria.
  2. Print step 4's Step Completion Report.
  3. Deliver.

Step Completion Reports

After each step, print one block. Its Gate line restates that step's Gate:

text
◆ Step N of 4 — <step name>
  <check>:  √ pass | × fail — <reason>
  Gate:     √ met | × not met — <what is missing>
  Status:   PASS | PARTIAL | FAIL
StepChecks
1Profile read, Fit claims sourced, Location rule, Missing constraints
2Listings opened, Hard rejects, Shortlisted (count)
3Product sources read, Near-misses
4Ranked, Acceptance criteria, Format (HTML: delivery check)

Step 1's Status is PASS or FAIL on its gate. Steps 2–3 are PASS when the gate holds for the requested count, PARTIAL for fewer, FAIL for none. Step 4's gate is the Acceptance Criteria. A FAIL still continues to step 4.

Expected output

See references/report-format.md for fields and a full example.

Edge Cases

  • An aggregator shows a role active, but its verified listing is closed or unreachable: Reject it.
  • No qualifying roles, or only a prior report's unchanged listings: Report None — 0 of N roles with the filters applied; never pad the list or fabricate roles.

Acceptance Criteria

Check every item before delivery. Remove from the ranking any role with a hard-reject or gate failure; label a missing soft detail "unknown".

Correctness

  • The ranking has no more than the requested count.
  • Each ranked role has a verified listing and a separately read company/project source, both linked.
  • Every location statement quotes the employer's policy and shows it passes the location rule.
  • Every product assessment covers what the company builds, the role's documented duties, the interestingness verdict, and what remains unknown.
  • Every fit claim points to candidate evidence; salary, dates, traction, and language claims without a source are omitted or marked unknown.
  • No application, outreach, or tracker change was made.

Understanding

  • The report's first line is the Result line, giving the status and count without expanding anything.
  • Verified facts name their source; inferences, conditional fit, and unverified items are labeled.
  • Each material claim's evidence matches its scope; a reachable apply form is never reported as eligibility.
  • The Decision line says "No approval needed" and names the candidate's remaining actions.

These checks verify the report against its instructions. Only reviewer feedback confirms that a human understood it; without that feedback, report understanding as unconfirmed.

Testing

Use evals/evals.json for positive, boundary, and negative-trigger tests. Grade every non-negative eval against both lists above.

© luongnv89, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (references) in skills/ai-job-scout of luongnv89/skills.

  • SKILL.md
  • docs/README.md
  • evals/evals.json
  • references/interactive-report.md
  • references/report-format.md

Open the folder on GitHubat commit b5ef695

Compare with similar skills

AI Job Scout next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

AI Job Scout compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
AI Job Scout this skillluongnv89/skills131—~2.5kAutomated safety check: PassMIT
Career-Ops Job Search Centercareer-ops-hq/career-ops74k—~3.6kAutomated safety check: PassMIT
Reactive Resume Builderreactive-resume/reactive-resume44k—~2kAutomated safety check: PassMIT
freehire Tech Job SearchMadsLorentzen/ai-job-search45k—~2.7kAutomated safety check: PassMIT
Interview Prepreactive-resume/reactive-resume44k—~10kAutomated safety check: PassMIT
LinkedIn Job SearchMadsLorentzen/ai-job-search45k—~1.2kAutomated safety check: PassMIT

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Categories

Questions about AI Job Scout

What does AI Job Scout do?

Find open AI engineering jobs that fit a candidate, verify location and apply links, and assess each employer's product. AI Job Scout is an agent skill from luongnv89/skills. Find open AI engineering jobs that fit a candidate, verify location and apply links, and assess each employer's product.

When should I use AI Job Scout?

AI Job Scout fits situations like: one-off shortlists; A job alerts research step; job applications.

How do I install AI Job Scout in Claude Code?

Run `npx skills add luongnv89/skills --skill ai-job-scout -a claude-code`. Or copy the skill folder (skills/ai-job-scout in luongnv89/skills) into .claude/skills/ai-job-scout in your project. Claude Code loads it when a task matches its description.

How do I install AI Job Scout in Codex?

Run `npx skills add luongnv89/skills --skill ai-job-scout -a codex`. Or copy the skill folder (skills/ai-job-scout in luongnv89/skills) into .agents/skills/ai-job-scout in your project. Codex loads it when a task matches its description.

Can I use AI Job Scout in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add luongnv89/skills --skill ai-job-scout -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ai-job-scout, .gemini/skills/ai-job-scout, .github/skills/ai-job-scout and .opencode/skills/ai-job-scout in your project.

What does AI Job Scout need to run?

SKILL.md names no scripts, command-line tools or credentials: AI Job Scout is instructions for the agent only.

Does AI Job Scout access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is AI Job Scout safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does AI Job Scout use?

AI Job Scout is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does AI Job Scout use?

About 2.5k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.4k tokens, read only when the agent opens those files.

What are the alternatives to AI Job Scout?

Skills that share tags, products or a category with AI Job Scout: Career-Ops Job Search Center (career-ops-hq/career-ops, 74k stars), Reactive Resume Builder (reactive-resume/reactive-resume, 44k stars), freehire Tech Job Search (MadsLorentzen/ai-job-search, 45k stars) and Interview Prep (reactive-resume/reactive-resume, 44k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains AI Job Scout?

luongnv89 (a GitHub user) maintains it in luongnv89/skills, which has 131 GitHub stars. The repository holds 35 skills in this directory. The repository was last updated on October 9, 2026.

Source: luongnv89/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.