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Found 202 Skills
Master hiring processes, interviewing, candidate assessment, and building strong engineering teams
Post jobs and search candidates on Indeed's job marketplace.
Finds qualified candidates for a role by searching LinkedIn, Indeed, GitHub, and other professional platforms using Nimble Web Search Agents. Accepts a job description, role title, or freeform request and returns a ranked candidate list with profiles, skills, and contact signals. Use this skill when the user wants to find, source, or recruit candidates for a role. Common triggers: "find candidates for", "source engineers in", "who can I hire for", "find me a [role]", "recruiting for", "talent search", "find a [role] in [city]", "build a candidate list", "sourcing for [role]", "who's available for", "find potential hires". Also triggers on a pasted job description followed by a sourcing request. Do NOT use for job market research or salary benchmarking — use market-finder instead. Do NOT use for researching a single known person — use company-deep-dive or meeting-prep instead.
Source and recruit candidates with LinkedIn's professional recruiting platform.
Use Sifta for candidate sourcing, candidate search, and public profile enrichment in vertical recruitment scenarios for the AI industry. This skill is intended for users looking to find, screen, enrich, or evaluate AI engineers/developers, embodied intelligence talents, solopreneurs/independent developers, founders, AI product managers, GTM/GMT/global expansion/AI marketing talents, and research-focused paper talents. This skill is NOT applicable to general talent search outside these personas, company intelligence, sales leads, outreach, ATS, KOL collaborations, or general web research.
Search for candidate leads in LinkedIn Recruiter — name, title, location, skills, contact signals. Use when the user wants to find qualified candidates for a role.
Point Cowork at a folder of resumes plus a job description -- screens every candidate against the actual requirements, produces a ranked shortlist with evidence, drafts advance/decline emails, and builds interview kits for the top picks. Pairs with hiring-scorecard for the interview stage.
Rigor Intake helper for README-first deep learning repo reproduction. Use when the task is specifically to scan a repository, read the README and common project files, extract documented commands, classify inference, evaluation, and training candidates, and return the smallest trustworthy reproduction plan to the main orchestrator. Do not use for environment setup, asset download, command execution, final reporting, paper lookup, or end-to-end orchestration.
Rigor Improve implementation leaf skill for auditable candidate implementation in deep learning research repositories. Use when the researcher explicitly authorizes exploratory work on an isolated branch or worktree to transplant modules, adapt a backbone, add LoRA or adapter layers, replace a head, or stitch together meaningful low-risk migration ideas with rollback-aware records in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline reproduction, conservative debugging, environment setup, verified contribution claims, or default repository analysis.
Take a markdown file of raw material and shape it into an article through a conversational session — drafting candidate openings, growing the piece paragraph by paragraph, arguing about format (lists, tables, callouts, quotes) at each step. Use when the user has a pile of notes, fragments, or a rough draft and wants help turning it into something publishable.
Sub-skill for the intake phase of README-first AI repo reproduction. Use when the task is specifically to scan a repository, read README and common project files, extract documented commands, classify inference or evaluation or training candidates, and return a minimum trustworthy plan to the main skill. Do not use for environment setup, asset download, command execution, final reporting, paper lookup, or end-to-end orchestration.
Turn Caveman's exact report-only repository observations into an operator-chosen optimization candidate with a paired baseline/candidate evaluation. Use when the user asks to inspect an optimization observation, evaluate a candidate change, or act on the current Caveman optimization report. Require a logged-in Caveman CLI connection and explicit approval; never infer money or actuation from a profile.