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Found 202 Skills
Screen core candidate stocks with high capital returns, stable moats, long-term compound interest potential, and strong earnings quality, and output priorities, valuation disciplines, and key points for continuous tracking. Applicable to scenarios such as long-term core position stock selection, compound interest asset pool construction, and high-quality company comparison.
Runs a sequenced monolith-to-modular pipeline that sizes and inventories components, finds shared domain duplication, addresses flattening and hierarchy issues, analyzes coupling, then groups components into candidate domain-aligned units, with optional embedded DDD strategic analysis for bounded contexts. Use when asking how to split a monolith, size components before extraction, find duplicated domain logic, clean up module hierarchy, measure coupling between modules, or group components into services. Do NOT use for phased extraction roadmaps or prioritization without the prior analysis steps (use decomposition-planning-roadmap after this pipeline), end-to-end legacy migration strategy writeups (use legacy-migration-planner), pure infrastructure capacity sizing, or when you only need DDD without the structural pipeline (install domain-analysis standalone).
Comprehensive ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiling for drug candidates. Integrates ADMET-AI predictions, SwissADME drug-likeness, PubChemTox experimental toxicity, ChEMBL clinical data, Lipinski rule-of-five, and CYP interaction data. Use for drug-likeness assessment, BBB penetration, bioavailability, hepatotoxicity prediction, ADME/PK profiling, or screening compound libraries before lab testing.
Generate 10 brilliant ideas for powerful new functionality. Use when brainstorming features, improvements, or innovations for a system. Internally generates 100 candidates and filters to the top 10 most impactful, pragmatic, and innovative ideas.
Generate candidate fixes for verified security findings. Consumes TRIAGE.json (preferred), VULN-FINDINGS.json, or a vuln-pipeline results directory. Pipeline input is delegated to the execution-verified `vuln-pipeline patch` ladder; static-analysis input gets a per-finding patch subagent + independent reviewer and is written as inert diffs for human review. Writes PATCHES/bug_NN/{patch.diff,patch_result.json}, PATCHES.md, and PATCHES.json. Use when asked to "fix the findings", "patch these vulns", "generate fixes", or "close the loop on triage".
Convert evidence gaps, conflicts, and anomalies into traceable candidate innovation points, and screen them based on contribution, feasibility, and falsifiability criteria. Use when the user asks for "finding research innovation points", "generating research directions from literature gaps", "screening candidate innovation points", "brainstorming research directions", or requests the rw-research-novelty workflow. Runs without a private local workspace or preset research-lab; use user-provided material and bundled public-source methods.
Think like a product manager before changing React Doctor's public surface — CLI commands/flags, the 0–100 score, config (doctor.config.*), the JSON report schema, package APIs (inspect()/diagnose()), the GitHub Action, the website, and the canonical prompts. A step-by-step runbook for a user-facing change — locate the surface, search for a reuse candidate, wire one telemetry metric, add the compatibility artifacts (changeset / schemaVersion / action tag), update docs, and record a kill metric. Not for lint rules, which have their own pipeline. Also runs when the user types `/product-thinking`.
Discover novel small molecule binders for protein targets using structure-based and ligand-based approaches. Creates actionable reports with candidate compounds, ADMET profiles, and synthesis feasibility. Use when users ask to find small molecules for a target, identify novel binders, perform virtual screening, or need hit-to-lead compound identification.
Recruit CRM integration. Manage Candidates, Jobs, Companies, Users, Notes, Files and more. Use when the user wants to interact with Recruit CRM data.
Route creator discovery requests into the right collection strategy before platform execution. Use this when the user wants to find creators, KOLs, KOCs, influencers, or partnership candidates and the request includes constraints such as follower range, niche, audience, geography, language, or collaboration fit. This skill decides whether to use handle-first, content-first, graph-first, or mixed discovery, then hands off to TikTok, Instagram, X, and creator-outreach skills.
[Hyper] Analyze vague or relayed non-developer stakeholder requests (client, executive, PM, sales/support) by mapping them to codebase impact, presenting interpretation candidates with risks, then implementing only after confirmation. Use for stakeholder-message analysis, not browser QA testing, CI/build failures, or already-clear technical tasks.
User research planning and synthesis at the observed behaviour layer — produces candidate job stories with confidence ratings