「taxonomy」の検索結果

Processes eDNA metabarcoding from raw paired-end reads to species tables, navigating ASV (DADA2, UNOISE3) vs OTU (swarm v2) decision (Callahan 2017 vs Schloss multi-copy-16S critique), marker/primer choice (Leray COI, MiFish 12S, 515F/806R 16S, ITS2) with primer-specific bias, OBITools3 v3 command-name break (obi stats plural; .tar.gz taxonomy), tag-jumping with dual-indexing (Schnell 2015; NovaSeq 10x MiSeq), decontam as screening-not-classifier (Davis 2018), read-counts-not-abundance critique (Lamb 2019), site-occupancy modeling (Ficetola 2015), Naive-Bayes calibration limits (Bokulich 2018), and eDNA decay (Strickler 2015). Use when going from raw eDNA FASTQ to species tables, picking marker + denoising pipeline, deciding whether read counts represent abundance, applying occupancy modeling, configuring OBITools3 v3, or interpreting decontam output. Not for clinical 16S microbiome (see microbiome/amplicon-processing).

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Search NCBI databases using Biopython Bio.Entrez (ESearch, EInfo, EGQuery, ESpell). Use when finding records by keyword, building reproducible field-qualified queries, navigating the Entrez Query Translator, exploiting the history server for large result sets, handling retmax caps, or interpreting weekly index lag. Covers PubMed, Nucleotide, Protein, Gene, SRA, GEO, Assembly, Taxonomy, ClinVar, dbSNP.

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Run remote BLAST searches against NCBI servers using Biopython Bio.Blast.NCBIWWW. Use when identifying unknown sequences, finding homologs, picking the correct BLAST program (blastn/blastp/blastx/tblastn/tblastx/psiblast/megablast/dc-megablast), interpreting Karlin-Altschul E-values, avoiding the max_target_seqs trap (Shah 2019), choosing composition-based statistics, or limiting searches by organism. Covers RID lifecycle, database choice (nt/nr/refseq_select/swissprot), word-size and CBS taxonomy.

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Build local BLAST databases and run searches using NCBI BLAST+ command-line tools. Use when running >50 queries, building custom databases with -parse_seqids and -taxid, downloading prebuilt NCBI databases via update_blastdb.pl, choosing -task variants (megablast/dc-megablast/blastn/blastn-short), tuning soft/hard masking, scaling threads, or extracting hits with blastdbcmd. Encodes BLAST v5 vs v4 database format, taxonomy filtering, makeblastdb pitfalls.

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Retrieve records from NCBI databases using Biopython Bio.Entrez (EFetch, ESummary). Use when downloading sequences, fetching GenBank/GenPept records, getting document summaries, parsing nested XML, navigating GI deprecation, choosing between rettype+retmode combinations, and parsing into Biopython SeqRecord/SwissProt objects. Covers nucleotide, protein, gene, pubmed, sra, gds, taxonomy, snp, clinvar.

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Detects prompt injection attacks targeting LLM-based applications using a multi-layered defense combining regex pattern matching for known attack signatures, heuristic scoring for structural anomalies, and transformer-based classification with DeBERTa models. The detector analyzes user inputs before they reach the LLM, flagging direct injections (system prompt overrides, role-play escapes, instruction hijacking) and indirect injections (encoded payloads, multi-language obfuscation, delimiter-based escapes). Based on the OWASP LLM Top 10 (LLM01:2025 Prompt Injection) and Simon Willison's prompt injection taxonomy. Activates for requests involving prompt injection detection, LLM input sanitization, AI security scanning, or prompt attack classification.

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Creates consistent error handling with custom error classes, HTTP status mapping, structured logging, safe client messages, and error taxonomy. Use when standardizing "error handling", "logging", "error responses", or "exception management".

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Use when authorship order, CRediT contribution roles, or AI-assistance attribution must be recorded transparently for a manuscript, when an author dispute needs an evidence trail, or before a contributorship statement is required at submission; this skill maintains the human-attribution ledger of who contributed what and in which role (authorship order, CRediT taxonomy, revision responsibilities), which is distinct from the disclosure-metadata audit owned by ai-disclosure-auditor.

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Skill-set loader for /hunt orchestrator. Fingerprints the target, picks the right platform attack skills, and loads the Red Team or WAPT skill set. Use when /hunt has just received a mode answer (redteam or wapt + blackbox|greybox) and needs to load the appropriate skills and print the taxonomy. Not for direct user invocation.

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Hunting skill for auth bypass vulnerabilities. Built from 12 public bug bounty reports across SAML XSW / parser-differential (GitHub Enterprise CVE-2025-25291/25292), SAML signature stripping (Uber, Rocket.Chat, samlify CVE-2025-47949), SAML domain enforcement bypass via control characters (HackerOne 2024), partner-portal cross-IdP assertion reuse (Slack), WordPress XMLRPC bypassing SSO (Uber), JWT alg-confusion HS256/RS256 (Jitsi), JWT signature-validation skip (Linktree, Newspack), and token-audience confusion (Argo CD CVE-2023-22482). For standalone JWT signature/crypto forging (alg:none, key confusion, kid/jku) see hunt-jwt-crypto; this skill covers JWT only inside SSO/SAML/token-trust bypass chains. SAML assertion-layer attacks (XSW, comment injection, signature stripping, XXE-in-assertion) are owned by hunt-saml; this skill owns the broader cross-protocol auth-bypass taxonomy. Use when hunting auth bypass — see the Legacy-Protocol Matrix for branded-UI vs legacy-endpoint patterns.

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Complete reference for 26 web2 bug classes with root causes, detection patterns, bypass tables, exploit techniques, and real paid examples. Covers IDOR, auth bypass, XSS, SSRF (11 IP bypass techniques), SQLi, business logic, race conditions, OAuth/OIDC, file upload (10 bypass techniques), GraphQL, LLM/AI (ASI01-ASI10 agentic framework), API misconfig (mass assignment, JWT attacks, prototype pollution, CORS), ATO taxonomy (9 paths), SSTI (Jinja2/Twig/Freemarker/ERB/Spring), subdomain takeover, cloud/infra misconfigs, HTTP smuggling (CL.TE/TE.CL/H2.CL), cache poisoning, MFA bypass (7 patterns), SAML attacks (XSW/comment injection/signature stripping), error disclosure / debug endpoints (stack trace regex per framework, chain templates), CSS injection (attribute-selector exfiltration, opacity clickjacking, @import). LFI / file inclusion -> RCE (php://filter source disclosure, iconv filter-chain RCE with no upload, log/environ poisoning, .user.ini/.htaccess auto_prepend, data:// + expect:// wrappers, session inclusion, traversal bypass table). Insecure deserialization (PHP __wakeup bypass / phar:// POP chains, Java ysoserial CommonsCollections gadgets + magic bytes, Python pickle __reduce__ + signed-cookie forgery, Node node-serialize). Dependency confusion / supply chain (internal package-name discovery, unclaimed-name confirmation, callback-only PoC, npm/pip/Maven/RubyGems variants). Use when hunting a specific vuln class or studying what makes bugs pay.

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