575건 · 관련도순
Capture confidence ratings before and after a learning attempt to identify overconfidence and underconfidence patterns. Use when a student wants to understand how well they actually know something versus how well they think they know it.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Capture confidence ratings before and after a learning attempt to identify overconfidence and underconfidence patterns. Use when a student wants to understand how well they actually know something versus how well they think they know it.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Assigns confidence scores to agent outputs based on multiple factors including source quality, consistency, and reasoning depth. Produces calibrated confidence estimates. Activate on 'confidence score', 'how confident', 'certainty level', 'output confidence', 'reliability score'. NOT for validation (use dag-output-validator) or hallucination detection (use dag-hallucination-detector).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when assigning a confidence level to an analytical judgment, the user asks "how confident are we?" / "what is the confidence on X?", or the orchestrator's tradecraft pipeline calls for a confidence level before publishing. Provides three named levels (High, Moderate, Low) with 0-100 score ranges aligned to STIX, and the rule that confidence cannot exceed what the weakest load-bearing claim supports.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate candidate positions and orientations for small molecules binding to proteins with DiffDock and DiffDock-L. Prepare single or batch docking runs, organize predicted poses by model confidence, and review validation needs without treating confidence as binding affinity.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Compare evidence from cyber operations to assess which attacker may be responsible. Weigh shared infrastructure, attack methods, malware similarities, timing, and language clues against competing explanations, then report attribution findings with justified confidence levels.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Diagnose bugs, intermittent failures, and performance regressions through structured logging and reproduction. It ranks possible causes with confidence scores, tests competing explanations, and reports whether the evidence proves the cause or leaves the diagnosis incomplete.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
VRChat エコシステムツール（ModularAvatar / liltoon / VRCFury / AvatarOptimizer 等）の ナレッジを 4 フェーズで段階的に調査・検証し `knowledge/*.md` に再現可能な形で蓄積する。 ソースコード読解 → inspect 実測まで進めると confidence を `high` に昇格できる。 トリガー: 新しい VRChat コミュニティツールに初めて遭遇した、 既存ナレッジの confidence が low で作業に支障がある、 ユーザーから「このツールについて調べて」と依頼された場合。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
양식, 청구서, 문제집처럼 일정한 형식의 텍스트에서 정보를 추출하는 흐름을 설계합니다. 정규 표현식으로 추출한 뒤 신뢰도가 낮은 항목만 LLM으로 확인하고 수정하도록 안내합니다.
저장소를 읽기 전용으로 조사해 유형, 권장 에이전트 구성, 위험도, 테스트 신뢰도, 공개 준비 상태를 보고서로 만듭니다. 위험도 기준에 따른 CI 판정에도 활용합니다.
Build an OpenCTI pipeline that adds external context to indicators of compromise, such as suspicious IP addresses. Connect intelligence services, relate indicators to known attackers and campaigns, and score confidence to help analysts prioritize investigations.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Plan user research by matching methods to study goals and explaining participant numbers and confidence. Synthesize tagged observations into recurring findings, distinguish isolated anecdotes, and organize evidence so research results can be found and reused.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Find people and assess identity matches using public information from social media, official records, and reverse lookups. The skill cross-checks independent sources, reports confidence levels, and requires at least three matching identifiers for a high-confidence result.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Build probability estimates from historical frequencies and supporting or opposing evidence. Structure forecasts with explicit assumptions and outcome criteria, update them as evidence changes, and assess past predictions using accuracy and confidence calibration.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Structure open-source intelligence research, meaning investigation using publicly available information, for authorized external assessments. Map organizational assets, record evidence and confidence levels, prioritize exposures, and prepare reports that explain technical findings and business risks.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Predict structures of proteins, DNA, RNA, and complexes with small molecules using OpenFold3. Guides hosted API requests or local Docker setup, saves structure files, and helps interpret confidence scores supplied with the predictions.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Predict how small molecules may bind to proteins using DiffDock through NVIDIA NIM. Prepare molecular inputs, run hosted API or local Docker requests, and save ranked binding poses with confidence scores for inspection.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Routes, triages, flags and rates a piece of text with a probability for every option: which department or queue a ticket goes to, which intent a message expresses, whether a yes/no condition holds (is this tool call grounded, should the agent ask first, is the customer angry), and how severe or urgent it is on a rubric, each with a calibrated confidence. Runs Claude Haiku in the request and response shapes of OpenAI's Decisions API, for when no purpose-built decision model is at hand. Use for "route this ticket", "triage these", "classify with a confidence", "give me a probability that", "rate the severity", "score against this rubric", guardrail checks before a tool call, or "a Decisions API without OpenAI".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
DiffDock and DiffDock-L diffusion-based molecular docking. Use for blind protein-small-molecule pose prediction from a PDB file or sequence plus SMILES/SDF/MOL2, batch docking over a CSV of complexes, virtual screening triage, sampling multiple poses per complex, and reading the confidence score correctly. Also trigger on DiffDock, DiffDock-L, inference.py, confidence_model, samples_per_complex, ESM embedding preparation for docking, or blind docking without a defined box. Not for binding affinity prediction — the confidence score ranks pose plausibility, not potency.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Epistemic transparency — acknowledging uncertainty, flagging limitations, avoiding overconfidence, and communicating what is known, unknown, and uncertain with proportional confidence. Maps the HEXACO personality dimension to AI reasoning: truthful calibration of confidence, proactive disclosure of gaps, and resistance to the temptation to appear more certain than warranted. Use before presenting a conclusion, when answering questions where knowledge is partial or inferred, after noticing a temptation to state uncertain information as certain, or when a user is making decisions based on provided information.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Identify insects using body plan analysis, dichotomous keys to order, wing venation, mouthpart type, antennae form, leg and tarsal structure, and confidence levels. Covers the fundamental hexapod body plan verification, a simplified dichotomous key to major orders, wing venation and type analysis, mouthpart classification, antennae morphology, leg specialization and tarsal formula, and a structured confidence assessment framework. Use when you need to identify an unknown insect beyond preliminary order placement, are working through a specimen for taxonomic study, want to distinguish between similar orders or families, or need to assign a confidence level to a field identification.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Add an external probabilistic classifier to a decision path without letting it take the path over. Covers finding the externally-graded rows you are already logging, measuring the confidence separation between the overrides it got right and the ones it got wrong, choosing an operating point your data licenses, failing open at the call site, and proving the whole arrangement offline with no API key. Applies to any service returning a score you can order — TypeSafe AI's System One models (Jev) are the worked example. Most of the method transfers to a local model or a second heuristic, with the parts that do not called out where they arise. Use when a rule-based path is wrong often enough to hurt, when someone proposes replacing a heuristic with a model, when a confidence threshold needs a defensible value, or when an oracle already in production has never been graded.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.