「candidates」の検索結果

Hunts for the other instances of a bug already found — the variants of one root cause across a codebase. Use immediately after a vulnerability, logic bug, or bad pattern turns up in a specific file and the question becomes where else it occurs, including the bare conversational form ("are there others like this?", "is this the same bug?"). Also for generalizing one known instance into a CodeQL or Semgrep query for its whole pattern family, and for triaging a set of look-alike candidates against a known root cause. Not for initial discovery with no bug in hand.

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Use when triaging public-equity idea candidates. Do not use for final trade recommendations, pitches, memos, or models.

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Design new protein binders with Boltz. Use when generating protein, peptide, antibody, nanobody, or custom binder candidates for a target. Not for screening existing proteins or small molecules.

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Apply a prompt-agnostic execution and verification loop to any substantial task while preserving the original request. Use when the user asks to fan out work, use subagents or independent reviewers, loop until done, benchmark against references, apply a harsh critic, compare candidates blind, improve an existing prompt with verification, or continue until explicit quality gates pass.

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Generate bounded independent candidates, score them against a frozen rubric, and verify the selected result with a proof log.

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This skill helps users automatically find social media profiles across platforms like Facebook, Twitter, Instagram, LinkedIn, etc. using the BrowserAct API. Agent should proactively apply this skill when users express needs like finding someone's social media accounts, discovering a brand's social media presence, tracking down social profiles of job candidates, finding contact info for sales prospects, researching a person's digital footprint, verifying the identity of someone met online, monitoring the social media reach of influencers, checking social accounts of business competitors, gathering public profiles for background research, locating official customer service accounts across platforms, or compiling contact databases for networking.

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Generate extremely simple, cute, personified square character images with rounded heavy forms, two purposeful character colors, one solid background color, and a dominant lower-corner composition. Use when creating an animal, creature, robot, ghost, plant, object, or other character image, including when the agent should infer three product-relevant directions and propose six independent candidates for approval.

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Pseudo-mutation analysis ONLY: answer whether tests would catch a bug if production code changed, which meaningful changes would still pass, or which caller-visible mutations existing assertions would miss; verify candidates when requested, then optionally close verified gaps. Includes explicit read-only per-test composition for grading. Activate for behavioral blind spots tied to production behavior. Polyglot. DO NOT USE FOR: suite taxonomy, metadata, or distribution reports (test-tagging); .NET line-vs-branch or Cobertura interpretation, arithmetic, plateaus, project-wide coverage gaps, or coverage-backed test/CRAP priorities (coverage-analysis; use native coverage tooling outside .NET); named-target CRAP (crap-score); new suites (code-testing); assertion/smell audits; or mutation tools.

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Execute and verify Apache OpenDAL release-manager work, including weekly ATR candidates, RC tagging, required GitHub Actions checks, ASF SVN dist uploads, Nexus staging close/release, vote discussions, language package readiness, GitHub release, announcements, and release postmortems.

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External recon for software supply-chain attack surface — package-namespace squatting candidates, dependency-confusion vulnerabilities, GitHub Actions injection openings, container image registry exposure, SBOM mining, internal-package-name leakage, and CI/CD configuration exposure. Reconnaissance and identification ONLY — actual package publishing / typosquat attacks are EXTERNAL-OFFENSIVE and require explicit written sign-off because they can affect the entire npm/PyPI ecosystem. Use when the target has a public GitHub org, when their build artifacts/SBOMs are reachable, when their docker images are on Docker Hub/GHCR, or when you find internal package names in their JS bundles.

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Score Hong Kong and China A-share dividend candidates across yield quality, durability, cash flow, valuation and momentum. Use when the user asks to dividend strategy with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.

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Compare option candidates and score components, with funding, expiry and assignment risks. Use when the user asks to options strategies with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.

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NOTE: molecule and target inputs and your NGC_API_KEY are transmitted to external NVIDIA-hosted API endpoints on every call. Use local NIM containers for confidential or proprietary data. Run a complete computational drug discovery pipeline using NVIDIA BioNeMo NIMs: generate drug-like molecules with GenMol, dock them to a protein target with DiffDock, then predict binding affinity with Boltz2. Use this skill whenever the user wants to generate and screen small molecule drug candidates, perform hit discovery, optimize leads against a protein target, or do virtual screening combining molecule generation, docking, and affinity prediction. Triggers on: drug discovery pipeline, hit discovery, lead optimization, virtual screening, molecule generation, molecular docking, binding affinity, GenMol, DiffDock, Boltz2, SMILES, SAFE notation, NIM microservice. This is a multi-step pipeline composing three BioNeMo NIMs.

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[omh] Turn repeated lessons into written rules: extract repeated principles from skills, prompts, traces, reviews, and failures into reviewed rule candidates without auto-mutating guidance. Use when the user says: rules-distill, rules distill, distill rules, rule distillation, principle distill, skill principles, extract agent rules, turn traces into rules.

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[omh] Recurring market or topic research: research operations department - coordinate Scout, Analyst, and Briefer work with source-inbox and status boundaries; for one decision brief use research-brief, and for typed candidates before research starts use source-finder. Use when the user says: research-department, research department, research ops department, research operations department, scout analyst briefer, scout analyst brief, daily research department, competitor research department.

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[omh] Existing skills to reuse before authoring: prepare a metadata-only search-before-creation report for local, marketplace, GitHub, and web skill candidates with risk review and adoption options. Use when the user says: skill-scout, skill scout, skill candidate, skill candidate search, skill discovery, find a skill, find skills, top skills.

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[omh] Gathering candidate papers, datasets, or repos: source candidate inventory - prepare typed source candidates and acquisition status before downstream work; use ulw-research to fetch and cite them, or research-brief to turn them into a decision-ready brief. Use when the user says: source-finder, source finder, source acquisition, source intake, find papers and datasets, find datasets and repos, find papers, find arxiv link.

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[omh] Recurring lessons worth promoting: turn repeated project or cross-project lessons into atomic, confidence-scored instinct candidates with scoped promotion and export boundaries. Use when the user says: instinct-ledger, instinct ledger, project instincts, project-scoped instincts, project scoped instincts, global instincts, instinct review, instinct candidate.

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[omh] Importing prompt files for Hermes: prompt import readiness - review and normalize external CLI-agent prompt files before offering slash-command candidates; use external-connector-readiness for plugin or API adoption and toolbelt-readiness for missing runtime capabilities. Use when the user says: prompt-import-readiness, prompt import readiness, slash prompt import, slash prompts import, slash command prompt import, prompt library import, prompt folder import, prompt directory import.

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Turning a described need (several constraints, a gift, a choice between candidates already in view, a search that came back empty or sold out) into a shortlist and a pick. Not needed when one search for the thing the customer named answers the request, or when the customer wants to learn what matters in a category first (purchase-research).

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Teaching a customer how to choose before any candidates appear, when they ask what matters in a category, how two approaches to it differ, or where to begin. Not needed once the customer has named the thing they want, however considered the purchase (that is search work), nor for outfitting a space or an event (planning-goals).

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Score and prioritize neoantigens and epitopes for immunogenicity using multi-factor models combining MHC binding, processing, expression, and sequence features. Rank candidates for vaccine design. Use when prioritizing epitopes for vaccine development or identifying the most immunogenic neoantigens.

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Screen US stocks for Stockbee-style selling-exhaustion hammer setups using prior momentum, pullback depth, undercut/reclaim, long lower-wick geometry, close-location, volume confirmation, quality/liquidity gates, and risk-distance scoring. Use when the user asks for Stockbee, Pradeep Bonde, exhaustion setup, selling exhaustion, hammer reversal, undercut reclaim, near-close reversal candidates, or pullback entries in high-quality funds-owned stocks.

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Screen S&P 500 stocks for Mark Minervini's Volatility Contraction Pattern (VCP) and detect historical VCPs in a single ticker's price path. Identifies Stage 2 uptrend stocks forming tight bases with contracting volatility near breakout pivot points; in historical single-ticker mode walks a multi-year history and emits every VCP that formed with forward-outcome stats (breakout / stop-hit / timeout). Use when user requests VCP screening, Minervini-style setups, tight base patterns, volatility contraction breakout candidates, Stage 2 momentum stock scanning, or historical VCP pattern study on a specific ticker (e.g. FIX, TSLA).

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