'classification' 검색 결과

Stellar spectral analysis from first light to chemical abundance. Covers continuum emission and absorption, the OBAFGKM classification sequence, luminosity classes, line identification, Doppler shifts, curve-of-growth abundance analysis, and the astrophysical conclusions that follow from a spectrum. Use when classifying a star, measuring radial velocity, inferring composition or temperature, or teaching why the Sun is mostly hydrogen.

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Classify the information-need of a query and dispatch it to the appropriate retrieval or reasoning strategy. Use before read-side memory access, before multi-strategy retrieval, or any time you'd otherwise default to "one retriever for everything". Returns a strategy label, a token budget, and a retrieval depth so downstream handlers can be specialised. Backed by Pre-Route (arxiv 2605.10235v2) and MemFlow (arxiv 2605.03312v1), which together show LLMs possess latent routing ability elicitable via a structured prompt — and that externalising the routing decision improves small-model performance by ~2x. Triggers: "route this", "what strategy", "before retrieving", "intent classification", or any query whose ideal handling depends on what KIND of question it is.

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Query NCBI ClinVar for variant clinical significance. Search by gene/position, interpret pathogenicity classifications, access via E-utilities API or FTP, annotate VCFs, for genomic medicine.

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Measure ACSets better than authors via surjectivity gadgets, Betti numbers, Möbius classification, and p-adic ultrametric distance

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This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

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用户审计自己的内容结构占比、判断单条帖子传播上限、或困惑"发了感叹/观点帖没人理"时调用。 关键 trigger: "内容原型/content archetype""哪种内容容易爆/which content goes viral" "传播上限/viral ceiling""普通表达型/资源入口型/工具教程型/AI工具发现型" "为什么没人互动/why no engagement""内容方向有问题/content direction wrong"。 不适用于: 已确定写某种短内容类型并需要组织结构(用 x-short-content-craft)、 检查帖子五件套完备性(用 x-five-piece-checklist)、做账号定位取舍(用 x-positioning-tradeoff)。

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Run a custom computer-vision prompt against an image with a local vision LLM. Wraps kuzco's tool_llm_image_custom() ellmer tool. Use when none of the specialized kuzco image skills (classification, OCR, recognition, sentiment, alt text) fit the task.

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Runs several language models as independent coders on the same labeling or discovery task and reads their disagreement as data — panel assembly for model diversity, keeping votes independent, consensus rules, agreement statistics (Cohen's and Fleiss kappa, Krippendorff's alpha), the correlated-errors caveat that stops agreement being mistaken for validity, human validation beyond the panel, and reporting. Use when the user asks about a council, panel, ensemble, or jury of models, asks how to combine several models' labels, or asks what kappa or alpha to report for model coders. Single-model codebook and validation work goes to text-classification, per-item confidence to llm-calibration-logprobs.

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Runs several language models as independent coders on one labeling or discovery task and treats disagreement as data. Covers diverse panel assembly, independent voting, consensus rules, Cohen’s and Fleiss kappa, Krippendorff’s alpha, correlated-error limits on validity claims, human validation, and reporting. Use when the user asks about a council, panel, ensemble, or jury of models, how to combine model labels, or what kappa or alpha to report for model coders. Single-model codebook and validation work goes to text-classification, per-item confidence to llm-calibration-logprobs.

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Advises on setting up and validating topic models for survey and experimental text. Covers the discovery-versus-measurement question, choosing STM, LDA, or BERTopic, preprocessing consequences, prevalence and content formulas, topic-count selection across coherence, exclusivity and FREX, held-out likelihood, and residuals, validation of topics against human judgment, robustness, and DA-RT-compliant reporting. Use when the user has open-ended responses or another corpus and asks what topics are present, how many topics to use, about STM, searchK, coherence, or FREX, or wants topic prevalence compared across treatment arms or countries. Fixed-codebook coding goes to text-classification.

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Reads model uncertainty from token log-probabilities. Covers collecting logprobs and aggregating multi-token labels, confidence tiers and margins for triage, calibration assessment with ECE, Brier scores, and reliability diagrams against human labels, responsible downstream use of confidence, and reproducibility archives. Use when the user asks about classifier confidence by item, logprobs, top-k tokens, calibration, ECE, Brier, reliability diagrams, or routing low-confidence cases to human review. Codebook and validation design go to text-classification.

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Reads a model's own uncertainty from token log-probabilities: collecting and aggregating label logprobs, confidence tiers and margins for triage, calibration against human labels (ECE, Brier, reliability diagrams), downstream use, and reproducibility. Use for per-item classifier confidence or routing low-confidence cases to human review; codebook and validation design go to text-classification.

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Advises on setting up and validating topic models for survey and experimental text: discovery vs. measurement, STM/LDA/BERTopic choice, preprocessing, covariate formulas, choosing K across coherence, FREX, held-out likelihood, and residuals, validating topics against human coding, robustness, and reporting. Coding against a fixed codebook goes to text-classification.

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Use when the PLAN artifact from SAM Stage 2 needs contextualization against actual codebase state — grounds the design plan in reality by performing scope analysis (NEW/MODIFY/COMPLETE classification), conflict detection between plan assumptions and codebase patterns, and resource mapping to concrete file paths and integration points. Produces an updated ARTIFACT:PLAN registered via MCP with a Contextualization section appended.

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Classify changed prose files into review tiers before deciding SKIP. Use when reviewing a diff containing prose files.

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Assess agentic layer maturity using the 12-grade classification system (Class 1-3). Use when evaluating codebase readiness, identifying next upgrade steps, or tracking progress toward the Codebase Singularity.

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audioFlux is a deep learning tool library for audio and music analysis and feature extraction, supporting dozens of time-frequency transforms and hundreds of feature combinations for classification, separation, MIR, and ASR tasks.

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Credential operations: secret discovery, classification, extraction, conversion, offline cracking of hashes the operator already has as a credential job, policy-aware spraying, NetNTLM capture, generic SMB relay, and Windows/Linux harvest. Do not hijack an in-progress /ad-attack Kerberoast/AS-REP chain (that module cracks and uses the ticket itself). Do not DCSync, read LAPS LDAP, or escalate cloud IAM. Usable credentials are recorded; the operator chooses the next module.

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Explainability (XAI) audit for products containing AI components. Five-stage tier-scaled check: risk classification, stakeholder×question matrix, fidelity audit, system card, recourse path. Honest about what was validated functionally vs what needs user testing.

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Evaluates mineral and hydrocarbon reserves with classification methodology, resource conversion rates, and valuation per unit analysis. Use when analyzing reserve reports, validating resource estimates, or assessing depletion profiles.

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Process external specifications through transformation, classification, gap analysis, component discovery, and decomposition. Creates workflow items with classified context.

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Implement regenerative recovery using triage, scaffolding, and progressive rebuild. Covers damage assessment, wound classification, emergency stabilization, scar tissue management, and resilience strengthening for systems that have sustained structural damage. Use when a system has suffered an incident needing structured recovery, when a failed transformation left the system in a damaged intermediate state, when accumulated technical debt has caused partial failure, or when a system is functional but degraded and the degradation is worsening.

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Audit, classify, and selectively forget stored memories. Covers memory enumeration and classification by type/age/access frequency, staleness detection for outdated references, fidelity checks using external anchors, a decision tree for selective withdrawal, tombstoned deaccession so removed content stays recoverable, counter-memory inoculation for failed strategies that would otherwise be re-derived, preemptive filtering rules for what should never become memories, and an audit trail so forgetting itself is reviewable. Use when memory has grown large and uncurated, when project state has shifted significantly since memories were written, when retrieval quality has degraded, or as periodic maintenance alongside manage-memory.

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Qualify a vendor or supplier of GxP-relevant software or services. Covers vendor risk classification, assessment questionnaire design, desk and on-site audit approaches, quality agreement evaluation, SLA review, and ongoing monitoring cadence definition. Use when selecting a new vendor for a GxP-critical system, onboarding a cloud provider for regulated data, performing a periodic re-qualification, when a vendor audit finding requires re-assessment, or when EU Annex 11 or ICH Q10 requires supplier qualification.

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