Most job search tools optimize one slice of the process.
Resume tools rewrite bullets. Job boards save listings. Spreadsheets track applications if you remember to update them. Interview prep tools give you practice questions.
The annoying part is that the job search itself is not one task. It is a pipeline.
A real job search has stale applications, half-written follow-ups, roles you should not apply to, referrals you forgot to ask for, interview stories that need cleanup, and rejections that should teach you something.
So I would not build a Job Search Agent as a resume writer.
I would build it as a command center.
Start with the pipeline
The core object is not a resume. It is an opportunity record.
company + role + source + fit score + stage + next action + due date + notes
That sounds boring. Good. Boring structure is what keeps a job search from turning into a pile of browser tabs.
The Agent should be able to take a vague status like this:
I applied to a few PM roles last week and heard back from one recruiter.
and turn it into something operational:
- Which companies?
- Which roles?
- Which stage is each one in?
- What is the next action?
- Is there a follow-up due?
- Do we need a different resume version?
- Is there an interview to prepare for?
That is the main behavior I want from this Agent. Less inspiration. More next action.
The Skill I used
The open Skill is here: Job Search Consultant Skill.
Direct file: SKILL.md.
The Skill turns the assistant into a job-search command center. It covers:
- target role strategy
- job description intake
- fit scoring
- resume tailoring
- application pipeline tracking
- networking and follow-up messages
- STAR/CARL interview prep
- weekly reviews
- offer comparison
The important part is the boundary: the Agent can help organize and draft, but it should not invent the candidate's experience or act without approval.
Fit scoring before resume editing
A common mistake is to start with the resume.
Someone pastes a job description and asks, "Can you tailor my resume for this?"
Sometimes the better answer is: don't apply.
The Skill starts new job intake by extracting the boring details:
title
company
URL or source
location
compensation
full job description
required skills
preferred skills
responsibilities
seniority signals
keywords
interview themes
Then the Agent can score fit across practical dimensions:
role match
skill match
seniority
location and compensation
company interest
referral path
application effort
That changes the workflow. Instead of treating every job as a resume-editing task, the Agent can recommend one of four actions:
apply now
apply with referral
save for later
skip
That one decision saves a lot of wasted effort.
Resume tailoring without lying
Resume agents are risky because they are very good at making weak claims sound polished.
That is not the same as making them true.
The Skill has a hard rule: rewrite only from truthful user experience.
A useful Agent should ask for the evidence behind a bullet before improving it:
What did you actually do?
What tools did you use?
Who was affected?
What changed after your work?
Do you have a number, percentage, time saved, revenue impact, cost reduction, quality improvement, or user impact?
Then it can rewrite in a structure like:
Accomplished [result] by doing [action] using [skill/tool], resulting in [metric/business impact].
If there is no metric, the Agent should not invent one. It can suggest where a metric might exist, or write the bullet without a number.
That is less flashy. It is also safer.
The memory this Agent needs
A generic assistant might remember that the user likes concise answers.
Fine. Not enough.
A Job Search Agent needs job-shaped memory:
- target roles
- target industries
- location and remote constraints
- compensation range
- dealbreakers
- master resume
- resume versions
- achievement bank
- application pipeline
- recruiter contacts
- referral contacts
- interview feedback
- STAR/CARL story bank
- follow-up history
- weekly review notes
This is why a persistent Agent is useful here. The user should not have to explain their target role, resume history, and active applications every time they ask for help.
Interview prep should reuse stories
Most interview prep agents generate questions. That is useful, but not enough.
The better workflow is to build a story bank.
For each story:
situation
problem
action
result
skills shown
roles it fits
weak spots
Then the Agent can map stories to interview themes:
conflict
leadership
ambiguity
technical tradeoff
failure
cross-functional work
customer impact
execution under pressure
Now practice becomes cumulative. Each mock interview improves the same set of stories instead of starting from scratch.
Weekly review is the feature people skip
A job search without review turns into anxiety.
The weekly review should ask plain questions:
Which opportunities moved forward?
Which ones are stale?
Which follow-ups are due?
Where are responses coming from?
Which resume version performed better?
What caused rejections?
What is the bottleneck this week?
What experiment should we run next week?
This is where the Agent becomes more than a document helper.
It can notice patterns. Too many cold applications and no referrals. Good recruiter screens but weak hiring manager interviews. Lots of saved jobs, not enough submitted applications. Follow-ups consistently late.
None of that requires magic. It requires a pipeline and memory.
Using the Skill in your own setup
If your agent client can install or reference GitHub-based Skills, use the Job Search Consultant Skill as the source.
For repo-based agent projects, keep it as a normal project file:
skills/job-search-consultant/SKILL.md
Then wire your Agent to load it when the user asks about resumes, job descriptions, applications, interview prep, recruiters, referrals, offers, or weekly reviews.
For Claude Code or similar coding agents, this works naturally as project context. The coding agent can read the Skill and update your agent config, templates, tests, or database schema around it.
For ChatGPT-style custom assistants, the same rule applies: install or reference the Skill if your setup supports it, and keep the candidate's private job history separate from the reusable Skill.
The Skill is the process. The pipeline is the user's data.
If you want the hosted version
I also set this up as a hosted Agent on ClawMama: Job Search Command Center Agent.
Direct create link: create the Agent.
The hosted version is for people who do not want to wire up memory, runtime, and chat channels themselves. The Agent uses the existing Skill and keeps the job-search context over time.
Current listed channels for this Agent:
WeChat
Telegram
Feishu
Email
That makes sense for this use case. A job search has lots of small updates: a recruiter reply, a new job link, an interview time, a follow-up reminder. It should live somewhere easy to reach.
A simple test
Give your Agent this prompt:
Here is a job description. Should I apply, and what should I do next?
A weak Agent rewrites your resume immediately.
A better Agent scores the opportunity, asks for missing constraints, separates supported claims from unsupported ones, and gives you the next concrete action.
That is the bar I would use for a job search command center.
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