「gates」の検索結果

Delegates execution to eight CLIs (Gemini, Qwen, MiniMax, GLM, Muse, Codex, OpenCode, Glimmer). Use for execution tasks. Do not use for secrets.

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。

Delegates tasks to the MiniMax CLI (mmx) via delegation-core. Use when delegation-core selects MiniMax or large-context batch work is needed.

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。

Delegates tasks to Qwen CLI via delegation-core for Alibaba's models. Use when delegation-core selects Qwen or large-context batch processing is needed.

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。

Delegates tasks to the OpenAI Codex CLI (codex exec) via delegation-core. Use when delegation-core selects Codex or OpenAI credentials are already configured.

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。

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。

Catalog every config axis, its defaults and guards. Use when adding or auditing configuration. Do not use for running gates; 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。

Triage night-market failures by symptom (hooks, CI, tests). Use when a check fails unexpectedly. Do not use for routine gates; 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。

Wire a System One model (Jev, or an open reproduction like Von) into a product feature — routing, guardrails, scoring, classification. Use when replacing an LLM call that returns a label rather than prose, when adding a typed decision to an agent loop, or when deciding between the hosted Jev API and a local open model. Covers question design, the eval-set-first workflow, threshold calibration, confidence gates, and the traps measured on real data.

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Search and extract contact information for people or companies including names, phone numbers, emails, job titles, and LinkedIn profiles. Aggregates data from multiple sources and provides enriched contact details. Use when users need to find contact information, build prospect lists, or enrich existing contact data.

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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 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.

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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 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 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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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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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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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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Autonomous CTF / boot-to-root campaign driver. Runs a box end to end with no operator approvals - the deterministic driver (scripts/campaign.py) owns pass state, generates the killchain board from recon, and prints the exact next action (Skill + tool) every turn. Use when handed a box/IP to own end to end, "run the ctf workflow", "root this box", "foothold to root". Single agent, wiki-first, tool-first. OSINT is OFF unless you invoke with an osint argument. Delegates box recipes to ctf-box.

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Runs a bounded spec-build-review development loop with explicit scope, stop conditions, and human approval gates for risky or ambiguous 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。

Audit local AI coding-agent sessions with agenttrace for cost, tool failures, latency, anomalies, health, diffs, and CI 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。

Secure AI coding agents (Claude Code, Cursor, Codex, Copilot) with permission boundaries, secret protection, code review gates, and safe sandbox configurations for team environments.

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Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution.

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Build automated evaluation suites for AI agents using golden datasets, rubrics, and regression gates. Use when shipping agent features, validating prompt changes, or gating deployments on quality.

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 reviewing mathematical validity, evidence, reproducibility, figures or a submission draft.

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。