job-preferences
Skill132 starsChanged 4 months agoMITView on GitHub
Set or update job-search preferences such as titles, salary, remote work, and filters for the other Job Apply skills.
In this repository3 other files it gives its agents.
- answer-memorySkill
- job-applySkill
- job-searchSkill
The file
We split this skill into the rules it gives an agent
5 rules. About 0 are common, 1 is distinctive, and 4 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
4
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 17
Follow the bundled answer-memory skill ($job-apply:answer-memory in Codex.
- Very specific: line 21
Use the JSON object returned by preferences-get.
- Distinctive: line 41
Use the host's structured question surface when available.
- Very specific: line 80
Write the collected values to a private temporary JSON object, then call preferences-set --input <preferences.json> through the bundled …
- Very specific: line 108
When the user invokes job-preferences and preferences already exist, show current values and let them update selectively.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Job Preferences
A Codex and Claude Code skill for managing persistent job search preferences. Set your target titles, salary floor, remote preference, and exclusion filters once; the bundled job-search skill reuses them automatically.
Workflow
Step 1: Load Profile
Follow the bundled answer-memory skill ($job-apply:answer-memory in Codex; /job-apply:answer-memory in Claude Code). Resolve <plugin-root> as that skill directs, run python3 "<plugin-root>/scripts/job-apply-store.py" init, then load saved preferences with preferences-get. Never read or write persistent Job Apply files directly.
Step 2: Check for Existing Preferences
Use the JSON object returned by preferences-get.
If preferences exist, display them:
Your current job search preferences:
- Target titles: Staff AI Engineer, Principal ML Engineer
- Min base salary: $250K
- Remote preference: Remote only
- Exclude patterns: junior, associate, intern, entry level
- Default time range: last week
Would you like to update any of these?
Then wait for the user. If they say no, stop. If they want to update, ask only about the fields they want to change using the host's structured question surface when available, or concise direct questions otherwise.
If no preferences exist, run the full Q&A below.
Step 3: Collect Preferences (full Q&A)
Use the host's structured question surface when available; otherwise ask concise direct questions. Ask no more than 4 questions at a time.
Question batch 1:
- Target titles (multi-select + custom)
- Header: "Titles"
- Question: "Which job titles are you targeting?"
- Options: Staff AI Engineer, Principal ML Engineer, Director of AI, Head of ML
- Multi-select: true
- The user can add custom titles via "Other"
- Min base salary
- Header: "Salary"
- Question: "What is your minimum base salary?"
- Options: $200K, $250K, $300K
- Multi-select: false
- Remote preference
- Header: "Remote"
- Question: "What is your remote work preference?"
- Options: Remote only, Remote preferred, Open to hybrid, Open to all
- Multi-select: false
- Exclude patterns (multi-select)
- Header: "Exclude"
- Question: "Which patterns should be excluded from results?"
- Options: junior, associate, intern, entry level
- Multi-select: true
Question batch 2:
- Default time range
- Header: "Time range"
- Question: "What default time range should job searches use?"
- Options: Last week, 2 weeks, Month
- Multi-select: false
Step 4: Save Preferences
Write the collected values to a private temporary JSON object, then call preferences-set --input <preferences.json> through the bundled helper and remove the temporary file. The helper merges supplied keys while preserving the rest of the preferences and profile. Never patch profile.json directly.
Schema:
{
"preferences": {
"targetTitles": ["Staff AI Engineer", "Principal ML Engineer"],
"minBaseSalary": "$250K",
"remotePreference": "remote only",
"excludePatterns": ["junior", "associate", "intern", "entry level"],
"defaultTimeRange": "last week"
}
}Step 5: Confirm
Display the saved preferences and confirm:
Preferences saved to your local Job Apply store at
~/.job-apply/profile.json.These will be used automatically by the bundled
job-searchskill. Invokejob-preferencesagain any time to update them.
Updating Preferences
When the user invokes job-preferences and preferences already exist, show current values and let them update selectively. Only overwrite the fields they change — keep the rest intact.
Safety Rules
- Use only the helper — initialize, read, and merge preferences through the bundled
answer-memoryskill; never directly edit files under~/.job-apply/ - Preserve existing profile — use
preferences-setwithout--replacefor selective updates - No defaults without user input — always ask the user, never assume values
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Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

