175 件 ・ 関連度順
概要と使いどころ
Deep learning for drug discovery. 60+ models (GCN, GAT, AttentiveFP, MPNN, ChemBERTa, GROVER), 50+ featurizers, MoleculeNet benchmarks, HPO, transfer learning. Unified load-featurize-split-train-evaluate API. For fingerprints use rdkit-cheminformatics; for featurization-only use molfeat.
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。
MS spectral matching and metabolite ID with matchms. Import spectra (mzML, MGF, MSP, JSON), filter/normalize peaks, score similarity (cosine, modified cosine, fingerprint), build reproducible pipelines, identify unknowns vs spectral libraries. Use pyopenms for full LC-MS/MS proteomics.
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。
Guides agents using the datamol Python package for RDKit-first molecular IO, preparation, fingerprints, similarity, structure generation, visualization, and utility workflows.
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 Chemprop 2.2.3 for molecular property prediction: CLI training, prediction, fingerprints, data validation, Python APIs, reaction/atom-bond tasks, uncertainty, hpopt, and conversion workflows.
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。
Enterprise SSL VPN attack - vendor fingerprinting, CVE matrix (Cisco, Fortinet, Citrix, Palo Alto, Pulse/Ivanti), default credentials, pre-auth exploit commands. Wiki-first, FIND schema output.
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。
Insecure deserialization hunting across Java / .NET / PHP / Python / Ruby / Node. Gadget-chain RCE, OOB-gated blind detection, magic-byte fingerprinting. Wiki-first, FIND schema output.
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。
CTF challenge router - fingerprint a challenge (file type / prompt / artifacts) into its category (pwn, rev, crypto, forensics, stego, web, osint, hash) and route to the matching wiki page, tools, and first moves. Wiki-first.
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。
This skill should be used when the user asks to "create a style sheet", "style guide", "house style", "keep the voice consistent", "voice drift", "British or American spelling", "character voices", "lint the prose", "prose check", "filter words", "said-bookisms", "overused words", "repeated phrases", "similar character names", "voice fingerprints", or wants to record and enforce the voice and surface conventions of a story project. NOT for rewriting dialogue to make the voices distinct when everyone sounds the same (use line-editing), or a character's profile or arc (use character-management).
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。
Detects and removes AI writing patterns from text. Use when polishing drafts, cleaning AI-generated content, or ensuring writing sounds authentically human. Invoke with "run through slop detector", "clean this up", or "remove AI fingerprints".
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。
Real-world Active Directory environment constraints that silently break attacks when ignored: NTLM disabled (Kerberos fallback), AES-only KDCs (RC4 blocked by GPO), LDAP signing and channel binding requiring LDAPS on 636, Kerberos clock skew (KRB_AP_ERR_SKEW), SPNs that must be FQDNs never short names or IPs (the misleading invalidCredentials), Protected Users / LAPS / gMSA / MachineAccountQuota, LDAP 1000-object paging, and VPN latency. Use this whenever a bind, TGT request, LDAP query, or SMB negotiation fails in a way that looks like bad credentials but is really hardening, or before running any AD tooling against a domain you have not fingerprinted. Standard-tooling knowledge (netexec, impacket, certipy, bloodyAD, kerbrute), no vendor-specific engine.
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。
Think and work like an expert Economic Geologist. Use when a task calls for Economic Geologist judgment. Reasons from mineral systems and deposit-type models (porphyry, VMS, orogenic Au, SEDEX, IOCG) through regolith and lithogeochemistry, LA-ICP-MS sulfide fingerprinting, and geophysical vectors to JORC/CIM/NI 43-101 MRE domaining, variography, OK/MIK estimation, and classification while treating transported regolith, dispersion shadows, pXRF false highs, and Inferred-overclaim as first-class failure modes.
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。
Think and work like an expert Climate Scientist. Use when a task calls for Climate Scientist judgment. Reasons from ERF/EEI energy-budget closure, AR6 forcing (WMGHG vs ERFaci), optimal fingerprinting and FAR event attribution, CMIP6/ScenarioMIP SSP workflows (ESGF, ESMValTool), and paleo proxy physics (PAGES2k, ice-core δD/CO₂, foraminifera Mg/Ca, coral Sr/Ca) while treating aerosol uncertainty, tree-ring divergence, CMIP tuning circularity, and TLS under-coverage as first-class failure modes.
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。
Think and work like an expert Climatologist. Use when a task calls for Climatologist judgment. Characterizes climate via WMO CLINO baselines (1991–2020 vs 1961–1990), ETCCDI indices, and teleconnection modes; bridges ERA5 climatology to CMIP6/ScenarioMIP SSP deltas (xsdba/QDM), optimal-fingerprint attribution, AR6 ERF/ECS/TCR, and proxy reconstructions (CPS/EIV, PAGES2k, MXD divergence)—distinct from weather forecasting and generic physical-climate narration.
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Think and work like an expert Cheminformatician. Use when a task calls for Cheminformatician judgment. Reasons from Standard InChI identity and RDKit sanitization through ChEMBL pChEMBL harmonization, Morgan/ECFP fingerprints, scaffold splits and applicability domains, while treating tautomer collapse, KNIME-vs-Python canonicalization drift, and random-split QSAR leakage as first-class failure modes.
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Think and work like an expert Algorithms Researcher. Use when a task calls for Algorithms Researcher judgment. Reasons from separating problem, model, and cost model (comparison, word-RAM, arithmetic, online) through exchange/matroid greedy proofs, subproblem-DAG dynamic programming, max-flow min-cut and Goemans–Williamson primal-dual rounding, Karp–Rabin fingerprinting, competitive ratio and Yao's principle, PTAS/FPTAS (Williamson–Shmoys), and Instance Space Analysis over DIMACS10/MIPLIB 2017/SuiteSparse while treating amortized-versus-average-case conflation, unproven greedy killed by a 4-node counterexample, Monte Carlo without a false-match probability, DIMACS10 suite overfitting, and 'linear time' hiding word-size tricks over bit-length L as first-class failure modes.
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。
Assesses ChIP-seq quality across antibody specificity, fragmentation, enrichment, replicate concordance, and library complexity. Computes FRiP, NSC/RSC (phantompeakqualtools), library complexity (NRF/PBC1/PBC2), deepTools plotFingerprint (JS distance, AUC, synthetic JS), ChIPQC, IDR with ENCODE Nself/Nt rules, and detects hyper-ChIPable artifacts. Use when validating an antibody, diagnosing failed peak calls, deciding whether to proceed with downstream analysis, grading against ENCODE thresholds, or auditing replicate concordance.
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。
Send a saved contact (as a Contact card) to a recipient over the relay — introduce one peer to another, nickname and all, instead of making them retype a 32-hex fingerprint. `<user>` is this session's user directory (absolute path; from init).
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Audit a whole regression suite and prune/restructure it with evidence: per-test coverage fingerprinting, AST near-duplicate clustering, CI-history mining for never-failing and flaky tests, prune decision rules (redundant/obsolete/low-value/keep), smoke/core/extended tiering by risk and defect-detection history, and a defensible "what we deleted and why" record. Deletion is destructive — quarantine and human sign-off are mandatory. Use when: "audit the test suite," "prune redundant tests," "find duplicate tests," "which tests can we delete," "restructure into smoke/core/extended," "is this test pulling its weight," "shrink the regression suite." Not for: Judging whether an individual test is WELL-WRITTEN (smells, assertions) — that is ai-qa-review. Healing one flaky test at runtime — that is test-reliability. Bulk selector regeneration after a UI refactor — that is selector-drift-recovery. Related: ai-qa-review, coverage-analysis, test-reliability, risk-based-testing, qa-project-context.
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。
Censys search engine for internet-connected hosts, TLS certificates, and web properties. Use when: certificate transparency monitoring, finding hosts by certificate fingerprint, alternative to Shodan for TLS/SSL analysis, discovering hosts running specific services, or tracking infrastructure changes via cert issuance.
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。
De-slop pass for any text. Scans for the statistical fingerprints of AI writing (negative parallelism / "not X but Y", em-dash density, rule-of-three, false ranges, puffery vocabulary, uniform cadence, hedged both-sidesing), rewrites by meaning, then re-scans until the text is clean and sits in the right register for its genre: academic article, tweet, reddit post, email, blog, docs, marketing. Use when the user says "deslopify", "deslop", "de-slop this", "remove the AI tells", "humanize this", "make this not sound like AI", or invokes /deslopify. Also use before publishing any agent-drafted prose.
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。
Transparent, itemized impressions of AI-generated WRITING STYLE — the repo's ONLY non-integrity track. Two passes: a deterministic defensive-hedge density screen (tools/check_ai_style.py, AIS-DEFENSIVE-HEDGE) plus a fresh cross-model GROSS-cases-only semantic pass over the 13 AIS-* style tells (broken narrative arc, LLM phrase tics, jargon-stuffing, invented codenames, clause/formula walls, gratuitous pseudocode, bullet overuse, bold-module spam, restatement loops, focus drift, single-style figures, appendix dumping). Every finding is named, LOCATED, span-anchored to the evidence ledger (claims.json), carries not_integrity_finding:true + false_positive_risk:high + an fp_case, and gets ZERO verdict weight: the adjudicator forces it to info, excludes it from overall_verdict, and renders it in a SEPARATE report section. NOT an AI-text classifier — no scores, no "this is AI-written", no authorship probability; a paper can be CLEAN_GIVEN_EVIDENCE and still list many. Emits ai-style-impressions.findings.json; computes NO verdict; if a tell is actually substantive it routes to the integrity auditor. Triggers: "AI style", "vibe check", "writing fingerprint", "AI 文风", "vibe paper".
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。
Anti-detection behaviour for Genesis browser automation - what the Camoufox tools already do, what the agent must still do, Cloudflare Turnstile handling, and per-site notes for bot-hostile sites
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。
Verify mini-program implementations and fixes with risk-calibrated evidence across static checks, unit tests, integration tests, state matrices, simulators, real devices, cloud environments, and release artifacts. Use when users ask to test, validate, accept, regression-check, quality-check, confirm readiness, or determine whether a mini-program feature is actually complete. Binds results to a source and build fingerprint, records commands and observable evidence, separates passed, failed, blocked, and not-run layers, prioritizes the next highest-information check, and never converts local success into device, cloud, release, or formal acceptance claims.
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。
Structural pattern analysis for writing-mode review. Performs paragraph-level decomposition and pattern aggregation. Invoked by the team lead during editorial review.
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。