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Found 202 Skills
Discover, vet, and install agent skills by searching ACROSS every major registry at once — skills.sh, clawhub.ai, and GitHub — presenting each board on its own native metric (installs / stars) with the top entry per board, security-scanning the top candidates' real SKILL.md for risky patterns, and flagging what's already installed. Use when the user asks "how do I do X", "find a skill for X", "is there a skill that…", "what skill should I install for…", or wants to extend the agent with a capability that might already exist as a published skill. Unlike single-registry search, this surfaces the best of every platform side by side, so you recommend the genuinely relevant, popular, well-maintained, and SAFE one — not whatever ranked first on one site.
Automates release management with changelog generation, semantic versioning, and release readiness checks. Use when preparing releases, generating changelogs, bumping versions, or validating release candidates.
Day 2 morning move of a Foundation Sprint. Forces generation of 3 to 7 candidate approaches as one-page summaries before the team converges on a top bet. Use after Day 1 is signed and before Magic Lenses on Day 2 afternoon. Enforces a minimum of 3 approaches to prevent first-idea anchoring. Each approach summary names what it is, why it serves the differentiators, and includes a simple visual.
Propose multiple visual design variants for on-screen elements and let the human pick in the Argent Lens window. Use when the user asks for design alternatives / options / A-B choices for a screen or component, or any time you have produced more than one candidate look for an element and want a human decision before committing.
Draft an offer letter with comp details and terms. Use when a candidate is ready for an offer, assembling a total comp package (base, equity, signing bonus), writing the offer letter text itself, or prepping negotiation guidance for the hiring manager.
Executes real-user QA sessions through public interfaces using personas, journeys, exploratory charters, test tours, edge-case probes, CFR checks, and browser evidence. Reads qa-report artifacts from <qa-output-path>/qa/ when present, captures issues/screenshots/reports under the same output tree, and classifies bugs by user impact. Use when validating a release candidate, migration, refactor, or user-facing change against production-like behavior. Do not use for AI implementation audits, task-status reconciliation, CI gate runs, integration/security/performance templates, or flaky-test triage; use agent-output-audit for those.
Use when a Luma / 拾光 / 拾光智能体 / 拾光工具 agent needs to inspect local material libraries, describe material groups, upload or understand materials, search candidates, or prepare PIP matching inputs.
Extract technical implementation evidence from developed code projects, generate algorithm/software specification-style technical disclosure documents around candidate patent solutions, and use the two-step method of "Claim Layout Card → Invention Patent Draft" to continue generating draft materials for Chinese invention patents that are close to the declarable version. Trigger scenarios include: writing technical disclosure documents after reading code repositories, mapping manually summarized patent solutions to specific implementations, mining patentable technical solutions from code, and preparing claim layouts and invention patent drafts for patent attorneys.
Compare the differences in business quality, growth, profitability, valuation and catalysts of peer candidate companies horizontally, and provide conclusions on relative strengths and weaknesses. It is applicable to scenarios such as choosing between two candidate stocks, selecting the best among peers in an industry, and establishing a priority tracking order.
Scan stocks for Poor Man's Covered Call (PMCC) suitability. Analyzes LEAPS and short call options for delta, liquidity, spread, IV, and yield. Use when user asks about PMCC candidates, diagonal spreads, or LEAPS strategies.
Analyze candidate algorithms for time/space complexity, scalability limits, and resource-budget fit (CPU, memory, I/O, concurrency). Use when feasibility depends on input growth or latency/memory constraints and quantitative bounds are required before implementation; do not use for persistence schema or deployment topology decisions.
This skill should be used when the user needs to create a personalized, compelling cover letter from a resume and job description. Use when writing job application letters, addressing specific role requirements, handling career change narratives, or structuring persuasive arguments for candidacy.