job-search
Skill1 starsChanged 3 months agoMITView on GitHub
Run the application pipeline: target list, stage tracking, follow-up cadence and a weekly read on where applications die. Use when planning job search pipelines, target companies, or warm outreach.
32 other projects use a version of this.
Adopted by
Most recently taken up by
- openaidom/skillstheir own version, first seen Nov 8, 2026
- ClaireAll/skillstheir own version, first seen Nov 8, 2026
- ouxxyy/ouba-classmatetheir own version, first seen Nov 8, 2026
- Gianni03/jobtracker-skillstheir own version, first seen Nov 8, 2026
- BrickerP/Personal-Skillstheir own version, first seen Nov 1, 2026
- Gianni03/gianni-skillstheir own version, first seen Nov 1, 2026
Used with
Projects with it also use application-tracker, job-application and 2 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
xiding-codeSkillapplication-tracker
Automatically log and track all job applications submitted. Use this skill after every application submission to record the details in a tracking spreadsheet and save relevant documents. Also trigger when the user asks "application status", "what have I applied to", "tracking", or wants to review their application history. Maintains a single source of truth for all job search activity.
Also in 4 projects that use this
pepebot-spaceSkilljob-application
Write tailored cover letters and job applications using your CV and preferred style
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claude-office-skillsSkillcompany-research
Conduct comprehensive company research and due diligence. Analyze business model, competitive landscape, management, and market position.
Also in 3 projects that use this
claude-office-skillsSkillresume-tailor
>
Also in 3 projects that use this
Other versions32 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 32
- proficientlyjobs/proficiently-claude-skills★ 389same name
- neonwatty/job-apply-plugin★ 132same name
- thesun4sky/jobstack★ 50same name
- squerne/open-career-skills★ 26same name
- muggl3mind/career-manager★ 24same name
- ouxxyy/ouba-classmate★ 23same name
- ua-job-search/job-search-skill★ 9same name
- Gianni03/gianni-skills★ 7same name
- Gianni03/jobtracker-skills★ 7same name
- gagangulyani/job-search-skill★ 4same name
22 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
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The file
We split this skill into the rules it gives an agent
4 rules. 0 are common, 4 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 28 edited versions we read keep these.
4
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 39
Never apply cold on the careers page if a 2nd-degree connection exists.
- Distinctive: line 57
If Recruiter Screen conversion $< 40\%$: Resume bullets lack quantified impact or tech keywords.
- Distinctive: line 58
If Technical Screen conversion $< 50\%$: Brush up on DSA or distributed systems fundamentals.
- Distinctive: line 59
If Onsite-to-Offer conversion $< 33\%$: Behavioral answers (STAR method) or culture fit signals need calibration.
“Common” is measured: kept by at least 6 in 10 of the 28 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
job-search
Core Philosophy
Submitting 400 blind applications through LinkedIn "Easy Apply" is a statistical dead end that yields a $< 1%$ interview rate and massive emotional burnout. A professional technical job search must be managed like an enterprise B2B sales pipeline: strict target account qualification, reverse-engineering company engineering stacks, activating warm 2nd-degree referrals, and demonstrating proof-of-work through custom technical artifacts before the first interview.
4-Step Technical Job Search Pipeline
Step 1: Target Account Curation & Tiering (30 Accounts)
- Tiered Account Architecture:
- Tier 1 (Top 5 - Dream Accounts): Uncompromising fit, high compensation, exceptional culture. Every application gets customized work samples and multi-threaded warm outreach.
- Tier 2 (15 Accounts - Strong Fits): High alignment with current skills, growing engineering organizations, Series B through Pre-IPO. Standard tailored resume + warm intro.
- Tier 3 (10 Accounts - Benchmark/Safety): Solid roles for pipeline velocity, interview practice, and establishing market compensation baselines.
- Qualification Filters:
- Tech stack match ($\ge 75%$ overlap with your strengths).
- Financial runway (profitable or raised within last 12 months).
- Engineering leadership pedigree and low Glassdoor/Levels.fyi red flags.
Step 2: Proof-of-Work Artifact Engineering
- The Pre-Interview Work Sample:
- For Tier 1 accounts, never submit just a PDF resume. Provide a tangible technical artifact:
- Audit their public GitHub repositories and open a well-crafted PR or detailed issue.
- Build a 1-page technical memo analyzing their API architecture or performance bottlenecks.
- Record a 2-minute Loom demonstrating a solution to a problem their job description mentions.
- For Tier 1 accounts, never submit just a PDF resume. Provide a tangible technical artifact:
Step 3: Warm Referral & Multi-Threading Playbook
- Network Triangulation:
- Never apply cold on the careers page if a 2nd-degree connection exists.
- Map connections: Search LinkedIn for engineering alumni from your previous companies or universities working at the target account.
- The 3-Touch Referral Outreach (Under 100 Words):
- Message to Peer Engineer:
- "Hey [Name], saw your team at [Company] is scaling the distributed data team. Loved your recent post on [Topic]. I've spent the last 3 years optimizing ClickHouse clusters at [Previous Org]. Would love to ask 2 quick questions about how your team handles schema migrations before I apply formally. No pressure at all!"
- Message to Peer Engineer:
- Direct Hiring Manager Outreach:
- Identify the Director/VP of Engineering or Engineering Manager owning the headcount. Send a concise email highlighting your 3 core relevant wins and attaching your custom technical artifact.
Step 4: Pipeline CRM Tracking & Conversion Diagnostics
- Pipeline Stage Metrics:
- Prospecting: Target identified, contact mapped.
- Outreach Active: Message sent, awaiting referral response.
- Applied / Referred: Formal application submitted with internal tag.
- Recruiter Screen: 30-minute culture and background alignment.
- Technical Screen / Take-Home: Coding or system design round.
- Onsite Loop: Multi-round executive and peer panel.
- Offer Stage: Negotiation and closing.
- Funnel Health Diagnostics:
- If Recruiter Screen conversion $< 40%$: Resume bullets lack quantified impact or tech keywords.
- If Technical Screen conversion $< 50%$: Brush up on DSA or distributed systems fundamentals.
- If Onsite-to-Offer conversion $< 33%$: Behavioral answers (STAR method) or culture fit signals need calibration.
Deliverable Format: Job Search Pipeline Spec (JOB-SEARCH-PIPELINE.md)
# Technical Job Search Pipeline & CRM
## 1. Target Criteria & Constraints
- **Target Role**: [e.g. Senior Staff Backend Engineer]
- **Target Comp Band**: [$X - $Y base / total package]
- **Location / Mode**: [Remote / Hybrid / City]
- **Core Tech Stack**: [Go, Rust, PostgreSQL, Kubernetes]
## 2. Tier 1 Target Account Matrix
| Company | Team / Division | Engineering Manager / Recruiter | Warm Angle / Connection | Proof-of-Work Artifact | Status |
|---|---|---|---|---|---|
| [Target A] | Core Infra | [Name/Profile] | Ex-colleague at company | Audited open source CLI | Referral requested |
| [Target B] | Platform | [Name/Profile] | Met at GopherCon | 1-page migration memo | Recruiter screen scheduled |
## 3. Funnel Velocity Dashboard
- **Total Accounts Mapped**: [Count]
- **Active Referrals / Conversations**: [Count]
- **Technical Screens Active**: [Count]
- **Onsite Loops Scheduled**: [Count]
- **Offers In Hand**: [Count]Worked Example: Senior Infrastructure Engineer Transition
- Strategy: Bypassed cold job boards. Built a target list of 25 companies using Kubernetes in production.
- Proof-of-Work: Contributed a bug fix to the target company's open-source Terraform provider before reaching out.
- Outreach: Messaged the author of the pull request on LinkedIn with a 3-sentence note referencing the fix.
- Outcome: Secured direct VP of Engineering interview within 48 hours; received offer with 25% compensation increase in 3 weeks.
Verification Checklist
- Target account list is capped at 30 high-conviction companies categorized into Tiers 1–3.
- Cold applications are prohibited for Tier 1 targets; warm intro or custom artifact is mandatory.
- Outreach messages are strictly $< 100$ words with zero generic flattery.
- Weekly funnel stage conversion rates are calculated to diagnose drop-offs.
- Salary expectations and minimum walk-away numbers are documented prior to initial screens.
Anti-Patterns
- Volume Spam: Spraying 50 resumes a day to random job posts without customizing a single bullet point.
- Passive Waiting: Waiting for recruiters to discover an unoptimized LinkedIn profile.
- Skipping the Hiring Manager: Relying solely on automated portal submissions rather than finding the engineering leader owning the role.
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