「by」の検索結果

Sort incoming scout messages and agent-forwarded openings by what they actually disclose: whether the employer is named at all, which of pay, location, employment type, and duties are stated, whether the message refers to the applicant's own history or reads as a mass send, whether a stated salary is an offer or a "up to" figure, and whether the same employer has arrived through more than one route. Use when a user has a backlog of scout messages and wants to know which ones can be researched, what to ask before replying, and what to settle before handing over a resume; do not use to rate how promising an opening is, to judge an employer or an agent, or to reply on the user's behalf.

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。

Research a specific company before applying or interviewing by gathering facts from primary and official sources, recording the source and date behind every claim, and keeping anonymous or unverifiable material clearly separated from confirmed fact. Use when a user asks what a company actually does, how it makes money, what its published conditions are, whether a posting matches the company's own disclosures, or what to verify before an interview; do not use to predict hiring outcomes, to repeat review-site content as fact, or to bulk-crawl sites that prohibit automated access.

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。

Prepare a request to change the terms of a Japanese job offer before accepting it, sorting what the user wants changed (pay components, start date, work style, location, title, probation) by priority, tying each ask to a basis the user can show (the written offer, the job posting, what was said in interviews, their own records), working the timing back from the acceptance deadline, and drafting the message to the employer or recruiting agent. Use when a user has an offer and wants to ask for higher pay, a later start date, more remote days, or other changed terms, or wants to know how to raise it without losing the offer; do not use to predict whether the request will succeed, to set a target figure from market rates, to fabricate a competing offer, to decide whether to accept, or to send anything to the employer.

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。

Put two or more Japanese job offers on one basis so they can be read side by side, separating fixed overtime allowance and unguaranteed bonus from the stated annual figure, converting each offer to guaranteed annual pay and to an hourly rate that includes deemed overtime hours, and listing non-monetary terms without scoring them. Use when a user holds more than one offer, wants to know what a stated 年収 actually contains, or wants to see where the ranking changes once the components are separated; do not use to recommend which offer to accept, to score or rank offers, to estimate take-home pay as fact, or to reply to an employer.

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。

Prepare for a Japanese job interview by working from the documents already submitted: find where an interviewer will dig in (claims the applicant cannot back up, work described in the first person plural, outcomes with no measure, gaps or overlaps in the employment history, must-have requirements the documents never answer), and turn them into questions to prepare evidence for. Use when a user has an interview scheduled and wants to know what will be asked and what to have ready, or wants to prepare reverse questions from earlier company research; do not use to predict whether they will pass, to write answers containing experience they did not report, or to schedule or contact anyone.

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。

Draft or review an employer's Otameshi Tenshoku listing so a candidate can decide whether to apply: check item by item that the work, deliverable, out-of-scope tasks, required experience, support, hours, schedule, location, compensation, contract form and payment timing are stated, find open-ended, unpaid-company-work, guarantee, personal-attribute and unverifiable wording, and confirm that the hours, weekly availability, period, rate and budget hold together. Use when a hiring manager, recruiter or small-business owner wants a listing written from a plan, wants an existing draft or published listing reviewed before or after posting, or wants to know why applications are not coming; do not use to design the work itself (trial-work-design), to reply to applicants (applicant-reply), to state market rates, legal, tax or service conditions from memory, or to post or update the listing on the user's behalf.

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。

Reply to an applicant for an employer's Otameshi Tenshoku listing: match the application against the listed requirements item by item on what the applicant actually stated, list the questions still open, and draft an invitation, a request for clarification, a hold notice or a decline that sticks to the listed conditions, keeps other applicants and internal notes out, and presents any new work or dates as a proposal rather than an agreed term. Use when a hiring manager, recruiter or small-business owner has applications to answer, wants to know what to ask before inviting, or has to decline; do not use to judge an applicant's aptitude or predict hiring outcomes, to screen on personal attributes unrelated to the work, to design the work (trial-work-design) or the listing (trial-listing-draft), or to send the message or update applicant records on the user's behalf.

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 and revise Japanese job-application documents (entry sheets, rirekisho, shokumu-keirekisho) by checking each answer against the question asked, the stated character limit, and the evidence the applicant can actually support, then propose concrete rewrites the applicant decides on. Use when a user wants an ES or resume answer critiqued, tightened, or drafted from their own experience; to assemble a whole shokumu-keirekisho from an experience inventory, use resume-draft instead; do not use to invent experience, to judge pass or fail probability, or to submit an application.

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。

INTERNAL/HELPER skill — do NOT invoke directly from a user query. This is the shared formatter/poster used by create-issue, create-issue-from-issue-number, and update-issue. It formats an implementation-ready GitHub Issue body, runs the pre-posting checklist, executes `gh issue create` or `gh issue edit`, and optionally posts a 確認事項 follow-up comment. Invoke this skill ONLY from one of the three parent skills via the Skill tool, after the parent has completed analysis. If a user asks to 'format an issue body' or similar, route them to the appropriate parent skill (/create-issue, /create-issue-from-issue-number, or /update-issue) rather than invoking this one directly.

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。

INTERNAL/HELPER skill — do NOT invoke directly from a user query. This is the shared formatter/poster used by breakdown-issues. It formats a scope GitHub Issue body (label cc-triage-scope, used before code analysis), runs the pre-posting checklist, and executes `gh issue create`. Invoke this skill ONLY from one of the parent skills via the Skill tool, after the parent has finalized the task breakdown. If a user asks to 'create a scope issue' or similar, route them to the parent skill (/breakdown-issues) rather than invoking this one directly.

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。

Triage a single GitHub PR by PR number. Check out the PR's branch, detect conflicts with the target branch via `gh pr status` (and label the PR with `cc-resolve-conflict` if any are found), collect unresolved review comments and CI status and judge only whether each item must be fixed, then take action (add cc-fix-onetime label if fixes are needed; if release-ready, add cc-release-ready label for an Epic PR marked `cc-epic-issue` instead of merging, otherwise merge the PR).

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。

Keep GPT-5.6 and other coding-agent work proportional to the request by locking scope, choosing the smallest sufficient implementation, matching verification to risk, and stopping when success is proven. Use for implementations, bug fixes, refactors, reviews, or maintenance where scope creep, speculative abstractions, unrelated cleanup, repeated test runs, unnecessary dependencies, or polishing beyond the request are risks.

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 an interactive HTML dashboard visualizing the Agent Skills ecosystem by fetching live data from github.com/agentskills/agentskills. Use this skill whenever the user wants to explore, research, or visualize Skills architecture, the SKILL.md spec, ecosystem adoption, repo stats, or any Skills intelligence dashboard. Trigger when the user mentions "skills dashboard", "skills ecosystem", "skills architecture overview", "agentskills repo", or wants to analyze how different tools implement Agent Skills. Optimized for Perplexity Computer — Computer fetches all data live, no static files required.

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。

Take a guided tour of Open SaaS — walks you through step-by-step through the project structure, features, and customization checklist.

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。

DON'T USE THAT SKILL. IT'S JUST AN EXAMPLE. Summarize today's Gmail inbox by category with anonymized senders and subjects, then surface the top 3 emails to focus on next. Run only when the user types /email-summary.

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。

Renders Hi-C contact matrices honestly and reproducibly with matplotlib, cooltools, HiCExplorer, pyGenomeTracks, FAN-C, CoolBox, and plotgardener. Covers the raw/ICE-balanced/observed-over-expected transform choice, LogNorm vs symmetric-diverging colormaps with vmax/percentile clipping, resolution-to-feature matching (compartments 100-500kb, TADs 10-40kb, loops 5-10kb), square vs rotated-triangle track-stacking, NaN/white-stripe handling, virtual 4C, APA/saddle/on-diagonal pileups, two-condition side-by-side and log2-ratio maps, and interactive (HiGlass) vs scripted-static publication figures. Use when plotting a contact matrix, choosing a normalization or color scale, building a multi-track Hi-C figure, making a virtual 4C profile, piling up loops/boundaries, or comparing two conditions.

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。

Designs and ranks guide RNAs (sgRNAs) for CRISPR-Cas9/Cas12a gene knockout by scanning a target for PAM sites (NGG SpCas9, NNGRRT SaCas9, TTTV Cas12a, NG SpCas9-NG, near-PAMless SpRY), enumerating candidate spacers, applying hard filters (Pol-III TTTT terminator, 5' G, GC), ranking on-target activity with the context-appropriate model (Rule Set 2/Azimuth for U6/lentiviral, CRISPRscan for T7/embryo, DeepHF for high-fidelity variants, DeepCpf1 for Cas12a), and predicting the indel/frameshift outcome (Bae out-of-frame score, inDelphi, FORECasT, Lindel). Use when selecting sgRNAs to knock out a gene, choosing a nuclease/PAM for a constrained locus, picking which exon to target, or shortlisting guides before an off-target check. Off-target specificity, base/prime editing, and HDR donors are separate skills.

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。

Handles BED-format genomic intervals (BED3 through BED12, narrowPeak/broadPeak) and the coordinate-system substrate the whole interval category rests on, with bedtools (CLI) and pybedtools/pyranges/pandas (Python). Covers the 0-based half-open vs 1-based-closed convention boundary and the start-1/end-unchanged conversion, the silent failures (chrom-name mismatch, CRLF, lexicographic-vs-version sort under -sorted), genome/chrom.sizes generation, sorting contracts, BED12 block invariants, validation, makewindows, cross-assembly liftover (liftOver/CrossMap), and BED<->VCF/BAM/FASTA conversion. Use when reading, creating, validating, sorting, lifting between genome builds, or converting interval files, preparing inputs for bedtools/tabix/bigBed, or debugging an off-by-one or empty-overlap result.

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。

Nominates and assesses CRISPR off-target sites genome-wide. Enumerates candidate sites by mismatch and bulge tolerance with Cas-OFFinder/CRISPRitz, ranks them with the published CFD score (SpCas9-only, relative ranker) or MIT/CRISTA/energy models, runs variant-aware screening against gnomAD/individual genomes (CRISPRme), and frames the empirical genome-wide discovery assays (GUIDE-seq, CIRCLE-seq, CHANGE-seq, DISCOVER-seq, Digenome-seq) and high-fidelity nuclease choice (HiFi Cas9, Sniper-Cas9, eSpCas9, SpCas9-HF1). Use when assessing guide RNA specificity, choosing among candidate guides, screening a therapeutic guide against population variation, or planning empirical off-target validation. Distinguishes predicted vs detected vs validated. On-target activity scoring and deaminase (Cas-independent) base/prime-editor off-targets are separate skills.

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。

Assembles a genome de novo from Illumina short reads with SPAdes (isolate/careful/sc/meta/plasmid/rna modes), MEGAHIT (low-memory, huge datasets), Unicycler (bacterial finishing/hybrid), MaSuRCA (large hybrid), ABySS (Bloom-filter), and Platanus (heterozygous diploids), using multi-k de Bruijn graphs. Covers the repeat-resolution limit, why N50 plateaus at the genome not the depth, GenomeScope2 k-mer profiling first, the heterozygosity/haplotig trap, error-correction erasing rare alleles, GC dropout, and NG50/auN/BUSCO reporting. Use when assembling a bacterial isolate, fungal, small-eukaryotic, single-cell, or metagenome genome from Illumina reads, or when deciding whether short reads can even produce the assembly being asked for.

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。

Performs set operations on genomic intervals - intersect (-wa/-wb/-wo/-wao/-loj/-c/-v/-u), subtract (-A), merge (-d, -c/-o), complement, cluster, multiinter, unionbedg, map, and groupby - with bedtools (CLI) and pybedtools/pyranges/bioframe (Python). Covers the sorted-input contract and the -sorted chromosome-order footgun, reciprocal/fractional overlap (-f/-F/-r/-e) and the A-vs-B asymmetry, -split for spliced/BED12/BAM features, and jaccard/fisher as mechanics only. Use when finding overlapping or unique regions between BED/peak/feature files, building consensus peaksets, removing blacklisted regions, transferring annotation values onto intervals, or computing interval-set similarity; route overlap-significance testing to overlap-significance.

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。

Orders and orients assembled contigs into chromosome-scale scaffolds from long-range linking data, inserting N-gap spacers (adds no sequence). Covers Hi-C/Omni-C scaffolding (YaHS, SALSA2, 3D-DNA/Juicer), Hi-C read-mapping prerequisites (map each end separately, no mate rescue, dedup, enzyme-aware), reading the contact map for misjoins/inversions/false-duplications, manual curation in Juicebox/PretextView (the VGP/DToL standard), reference-guided scaffolding (RagTag) and its karyotype-erasure hazard, genetic-map (ALLMAPS) and Bionano optical-map integration, chimera-breaking before scaffolding, gap-filling, and telomere/contig-vs-scaffold-N50 QC (tidk). Use when turning contigs into chromosomes with Hi-C, integrating a linkage map or optical map, choosing a scaffolder by available linking data, or judging whether a chromosome-scale assembly is trustworthy.

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。

Tests whether two genomic interval sets overlap (colocalize) more than expected by chance using a permutation test against a structured-genome null model. Covers bedtools fisher (analytic 2x2 screen), bedtools shuffle + jaccard permutation, GAT (isochore/GC-conditioned simulation with FDR), regioneR (flexible permutation, randomizeRegions vs circularRandomizeRegions, localZScore), LOLA (universe-relative Fisher against a region database), and GREAT/rGREAT (regulatory-domain binomial + hypergeometric for ontology-from-regions). Stresses the universe/background choice, matched background, blacklist exclusion, and multiple-testing control. Use when asking whether peaks/regions are enriched at enhancers/TFBS/features, scoring region-set colocalization or region-set enrichment, comparing CNV/SV concordance, or turning an overlap count into a defensible p-value.

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。