「gate」の検索結果

Enforce evidence bar, coverage gates, and regression guards. Use when adding tests or claiming done. Do not use to run suites; use night-market-operations.

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 when a target journal has no profile yet. Reads the journal's author guidelines and writes a detailed /write-paper profile plus a compact /find-journal profile, public or user-local private, in the canonical format with quality gates.

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 when a clinical CSV/Excel dataset needs profiling and cleaning before analysis (missing values, outliers, duplicates, type mismatches). Profiles, flags and generates cleaning code in three stages, each gated on the researcher's approval. Never auto-cleans.

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Use when preparing a medical-imaging dataset (DICOM/NIfTI) for modelling. Profiles spacing, orientation, intensity, label integrity, foreground fraction and target volume, gates them against the plan, then plans and audits preprocessing and augmentation for leakage.

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Use when one validated cohort analysis must be repeated across many exposure/outcome pairs. Generates one R/Python script per combination from a single methodology template, changing only the variables, and aggregates the results into a summary matrix.

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Use when validating or evaluating a trained medical-imaging model. Audits split leakage and validation design, computes task-correct held-out metrics (Dice + HD95, AUROC + AUPRC, FROC, calibration), and covers uncertainty/OOD and Grad-CAM explainability, each with a gate.

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Use when a trained medical-imaging model needs its documentation. Fills a Model Card and a Datasheet for its dataset from facts you supply, adds a METRIC-informed data-quality pass and gates that no required section is empty. Never invents numbers, provenance or licence.

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Use ExecuTorch to export PyTorch models to edge runtime artifacts, build host/device runtimes, choose delegates, profile/debug execution, and operate specialized Qualcomm, Cortex-M, LLM, and binary-size workflows.

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Guides Researchers through Faiss installation, dense and binary similarity search, index training and composition, persistence and evaluation, and explicitly gated CPU/GPU interoperability workflows.

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Operate and extend GeoNode geospatial content-management deployments, resource APIs, uploads, metadata/catalogues, GeoServer security, harvesting, and administration with explicit service and safety gates.

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Use Fairlearn to assess group fairness, compute disparity metrics, visualize subgroup performance, and mitigate unfairness with preprocessing, reductions, postprocessing, or adversarial learning.

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Use Diffusion Policy for robot imitation-learning configs, zarr replay data, policy/model interfaces, training/evaluation workflows, and safety-gated real robot operations.

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Operate the Bindu agent microservice framework: Python bindufy agents, A2A protocol, security, gRPC SDKs, deployment, Gateway, and Inbox.

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Use when you have a spec, design doc, structured plan, or prompt_plan and need to generate a pipeline Graphviz DOT file for headless autonomous building. The agent reads the spec, extracts build pipeline phases, determines tech stack, asks about human gates and model preferences, generates a validated DOT digraph with rich self-contained prompts, and saves it to the project root. Handles any language or framework.

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Decide when to delegate to Honey's read-only subagents (hive-scout, hive-reviewer) instead of working inline, so the expensive tokens — large file reads and review passes injected back into the orchestrator's context — come back compressed (Honey Lever 3). Use when a task is search-heavy or review-heavy, spans many files, or you want to keep the orchestrator's context small. Not for trivial one-file work.

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。

Delegate search/review to subagents; compressed returns.

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Delegate search- and review-heavy work to Honey's read-only subagents so large reads return compressed instead of bloating 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。

Finding validation gate - 7-Question triage adapted for FIND schema. Run before moving any FIND from Research to Completed. One NO = keep in Research. All YES = move to Completed, then run /evidence.

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Fast PARALLEL wiki lookup engine over wiki/techniques + wiki/payloads + wiki/tools + wiki/cheatsheets for a surface/service/vuln-class. Two modes - quick (one qmd search, cheap, fire constantly) and deep (4 parallel subagents, one per area, merged ready-to-use arsenal card, cached). This is the fast path arsenal that `arsenal` delegates to; the hunt-* skills each inline their own qmd_query and can hand off here for a parallel lookup. Use for "what do I use against <surface>", "arsenal for <X>", "deep/full arsenal", "tool + payload + technique + cheatsheet for <X>", "fast wiki lookup", "parallel wiki search", any "how do I attack/exploit <service|vuln-class>" where you want the documented tooling + payloads before hand-rolling.

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Drive Burp Suite over its MCP server as an AI triage + attack layer - review proxy history for signals, replay via Repeater/send, OOB-gate blind bugs with Collaborator, fuzz via Intruder (RoE-safe), then hand off to the matching vuln-class hunt. Wiki-first, FIND schema output.

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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.

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Shared discipline for every hunt-* skill: scope and authorization gating, the two-account rule, the confirmation gate that separates a real finding from a false positive, enumeration limits, stop conditions, marker discipline, wiki-first query and self-heal, FIND output, Deadends, and wiki distillation. ALWAYS LOADED alongside any hunt skill. Also trigger directly on "is this in scope", "is this a real bug", "how do I confirm this", "should I keep going", "how many IDs should I test", "what severity", "how do I report this", "I got someone else's data". Every hunt-* skill assumes this file; without it they run without their safety and quality layer.

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。

Active Directory attack hunting - enumeration to domain dominance. Spray-safe (lockout gate), AS-REP/Kerberoast, ACL + ADCS (ESC1-16), delegation, DCSync, lateral movement. Wiki-first, FIND schema output.

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Post-engagement knowledge harvest AND harness retrospective - after a box/bugbounty/pentest/CTF is completed, first diff how the engagement was EXECUTED against the skills/hooks that governed it (what discipline was skipped) and improve the harness, then sweep the whole engagement for GENERIC reusable knowledge NOT already in wiki/ and land it via the leak-gated stage->promote pipeline. Use at close-out or when asked to "extract learnings", "what did we learn", "harvest lessons into wiki", "distill this engagement", "post-mortem into the wiki", "what did we do wrong", "improve the harness from this box".

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。