「candidates」の検索結果

Search Agentlas Cloud and Hub agent candidates without invoking.

日本語の概要は準備中です。原文の説明を表示しています。

Generate headline candidates from a story's raw facts: news-style headlines, press-release headlines, and pitch subject lines. A pure generation skill — it finds the charge in the facts, then runs ten proven moves (consequence, picture, number-as-hero, two-beat turn, naming, reader's-own-story, open question, voice, sound, sized claim), each calibrated by real, verified headlines that made history.

日本語の概要は準備中です。原文の説明を表示しています。

Read-only evo run reporting. Use when the user invokes /evo:report, asks what happened overnight, asks what improved recently, asks for the best/frontier candidates, asks for a quick score chart without opening the dashboard, or wants the scatter plot in chat output. Never run benchmarks, gates, Slurm commands, evo run, or ad-hoc verification scripts for report requests.

日本語の概要は準備中です。原文の説明を表示しています。

Gated checklist to choose or review a library, framework, storage, data format, model, build-vs-buy or architecture. Run favorites-search before external research when installed. Compare candidates and trade-offs; select within authorization or return user-owned choices. Use for 用哪个 / 选什么框架 / 要不要自建 / A 还是 B / 这个方案行不行 and before committing to one. Research reports → deep-research.

日本語の概要は準備中です。原文の説明を表示しています。

Review a bill of materials for cost, risk, and supply exposure — cost rollup, top-10 cost drivers, single-source and EOL risk, MOQ vs forecast mismatch, cost-down candidates, and tariff/logistics sensitivity. Use when asked to review a BOM, find cost-down opportunities, check component sourcing risk, or sanity-check BOM cost against target. Produces a structured BOM review with a cost driver Pareto, risk flags per line, and a prioritised cost-down list.

日本語の概要は準備中です。原文の説明を表示しています。

Help users design and execute a high-signal interview process that prioritizes real-world performance over superficial charisma or pedigree.

日本語の概要は準備中です。原文の説明を表示しています。

Detect Server-Side Template Injection (SSTI) vulnerabilities in a codebase using a three-phase approach: recon (find template rendering sites that use dynamic strings), batched verify (trace user input to those sites in parallel subagents, 3 candidates each), and merge (consolidate batch results). Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/ssti-results.md. Use when asked to find SSTI or template injection bugs.

日本語の概要は準備中です。原文の説明を表示しています。

Use when picking the next Spark expression to implement natively in Comet, or when asked whether a specific codegen-dispatched expression is worth a native Rust implementation. Scores the candidate on how likely a native path can be 100% Spark-compatible and how much throughput or allocation it would save versus the JVM codegen dispatcher, records the verdict in the per-expression audit log so disqualified candidates are not re-litigated, and files a GitHub issue for the recommendation.

日本語の概要は準備中です。原文の説明を表示しています。

CP-7 retrospective: audit checkpoints, evidence quality, action items, and harvest candidates. Closes the V-model cycle and feeds the next run. Use via /retro after ship, escalate, or significant session.

日本語の概要は準備中です。原文の説明を表示しています。

Rank and prioritize neoantigen/epitope candidates by likely T-cell response using NeoFox feature annotation, PRIME2.0, BigMHC-IM, the Łuksza/Balachandran fitness model (agretopicity + foreignness), and pVACtools tiering. Encodes the field's hard truths that immunogenicity is the least-solved layer (dedicated scores ~AUROC 0.6-0.7, modest PPV), that scores are valid only for RANKING within one patient (never absolute go/no-go or cross-patient), that DAI has anchor-inflation and WT-denominator traps, and that stacking weak correlated scores into one number is a red flag. Use when ordering a candidate list for a vaccine. Binding lives in mhc-binding-prediction; calling in neoantigen-prediction.

日本語の概要は準備中です。原文の説明を表示しています。

Infer or annotate TCR antigen specificity by unsupervised clustering (TCRdist/tcrdist3, GLIPH2, clusTCR, GIANA) and database lookup (VDJdb, IEDB, McPAS-TCR), and rank candidates with supervised predictors (ERGO-II, NetTCR-2.x, pMTnet) under explicit caveats. Encodes the central truth that general TCR-epitope prediction for UNSEEN epitopes essentially does not work (collapses to near-random; IMMREP22, Grazioli 2022) because labeled data is dominated by a few immunodominant epitopes and there is no true negative set — so clustering for discovery is the honest task and de-novo binding needs wet-lab validation. Use when annotating TCR specificity or grouping a repertoire. Epitope/MHC context lives in mhc-binding-prediction.

日本語の概要は準備中です。原文の説明を表示しています。

Predict peptide-MHC class I binding and natural presentation with MHCflurry, NetMHCpan-4.1, and MixMHCpred to nominate candidate CD8 T-cell epitopes. Covers the binding-affinity (BA) vs eluted-ligand (EL/presentation) distinction, why %Rank beats raw nM for cross-allele work, the MS abundance bias that misranks low-expression neoantigens, allele-coverage inequity, and length bias. Use when scanning a protein or peptide set for class I epitopes, scoring neoantigen candidates, or choosing a binding predictor. For CD4/HLA class II see mhc-class-ii-prediction.

日本語の概要は準備中です。原文の説明を表示しています。

Builds and applies 3D pharmacophore models using RDKit Pharm3D, the apo2ph4 receptor-based workflow (Heider et al. 2023), Pharmer / Pharmit for search, and PharmacoForge for protein-pocket-conditioned pharmacophore generation (Flynn et al. 2025), covering ligand-based pharmacophores from active-set alignment and receptor-based pharmacophores from binding-pocket geometry. Explicitly handles feature types, geometric tolerances, partial matching, and pharmacophore-based virtual screening. Use when identifying scaffold-hopping candidates, building shape-and-feature search queries, or transferring SAR across chemotypes.

日本語の概要は準備中です。原文の説明を表示しています。

Evaluate ecommerce price candidates using unit economics, historical observations, elasticity analysis, scenario modeling, and controlled experiments. Use when a seller asks what price to test, how price changes could affect contribution or revenue, how to estimate elasticity, how to optimize a bundle or tier, or how to design a price experiment across Amazon, Shopify, TikTok Shop, Walmart, eBay, or other channels. Do not claim a proven optimal price without sufficient clean data, and do not change live prices without explicit authorization.

日本語の概要は準備中です。原文の説明を表示しています。

Classify a batch of emails into action categories (reply now, reply later, archive, delete, unsubscribe) and surface unsubscribe candidates. Use after a busy week, running inbox zero, or triaging email overload.

日本語の概要は準備中です。原文の説明を表示しています。

Automates release management with changelog generation, semantic versioning, and release readiness checks. Use when preparing releases, generating changelogs, bumping versions, or validating release candidates.

日本語の概要は準備中です。原文の説明を表示しています。

Build and maintain one coherent company story across employees, investors, customers, candidates, and partners. Use when preparing all-hands meetings, investor updates, board presentations, recruiting narratives, or crisis communications.

日本語の概要は準備中です。原文の説明を表示しています。

Screen a client, counterparty, payer or corporate against the sanctions lists published by the designating authorities themselves — the UK Sanctions List (FCDO), OFSI, the UN Security Council, the EU consolidated list and OFAC's SDN and non-SDN lists. Use when onboarding, before taking a payment, before completing a transaction, or whenever a name must be checked against sanctions. Produces candidates and provenance, never a clearance: the record names every list searched with its publication date and count, states what was NOT searched, and ends at a blank decision block. Fails closed — a cache that is missing, damaged, truncated or stale is reported NOT SEARCHED and the process exits non-zero, so nothing can read an incomplete run as clean. Matches across transliteration, diacritics, homoglyphs and initials, because nobody spells a name the way the publisher does. Standard library only; screening opens no network connection.

日本語の概要は準備中です。原文の説明を表示しています。

Turn an app idea into validated, App-Store-ready name candidates. Use when the user says "name my app", "what should I call it", "app name ideas", "help me name this app", "is this name available", or needs to pick a brandable, ownable name before reserving it in App Store Connect.

日本語の概要は準備中です。原文の説明を表示しています。

Generate high-potential viral title candidates for content across WeChat public account articles, X/Twitter posts, YouTube videos, Bilibili videos, and similar content platforms. Use when the user needs headline/title ideation, batch title generation, platform-specific title adaptation, title-library reuse, title selection, title scoring, or a reusable title workflow based on universal formulas plus separated platform methods such as 公众号, X, YouTube, and B站.

日本語の概要は準備中です。原文の説明を表示しています。

Speculative parallel implementation methodology — dispatch N candidate implementations, audit all, score, pick best. Auditability mandate: ALL candidates logged (not just winner).

日本語の概要は準備中です。原文の説明を表示しています。

Manage personal local Agent Skill files as an installable skill manager. Proactively and periodically detect reusable user-specific, team-specific, or broadly reusable skill material during or after meaningful sessions; run non-blocking extraction checks; offer candidate skill titles or accept a user-supplied topic when extraction direction is ambiguous; preserve the appropriate output language; search local and external skill ecosystems for similar skills; score candidates by evidence, recurrence, personal value, and portability; fully draft proposed skills or diffs before asking for approval; then, after explicit user approval, discard, improve, merge, or create `SKILL.md` folders.

日本語の概要は準備中です。原文の説明を表示しています。

Manage a v3 Session — inspect status/resume-view, review knowledge candidates, complete, archive/unarchive

日本語の概要は準備中です。原文の説明を表示しています。

Create per-subsection evidence packs (NO PROSE): claim candidates, concrete comparisons, evaluation protocol, limitations, plus citation-backed evidence snippets with provenance.

日本語の概要は準備中です。原文の説明を表示しています。