277 件 ・ 関連度順
概要と使いどころ
Use for Deepline GTM work that searches, enriches, scores, collects signals, or automates a workflow: find companies or people, enrich a CSV, find emails or LinkedIn, compare providers, build a waterfall, create a webhook or cron, or write a Play. For live information work, run a small heterogeneous experiment, exploit the observed winner, and reopen misses. Skip pure copywriting and non-GTM research.
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。
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.
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。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for malware configuration recovery, staged payload boundaries, beacon parameter extraction, and IOC decoding. Use when the user asks to recover a malware config, decode C2 or beacon fields, unpack staged payloads, extract bot or campaign IDs, or tie recovered config to observed protocol behavior under sandbox assumptions. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
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 RocketRide to compose, run, observe, and deploy portable AI pipelines from an IDE or CLI across model providers, vector stores, and agent nodes.
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。
Gives Claude Code operators a live dashboard for multi-agent sessions, tool calls, file activity, and nested task progress so debugging starts from what the agents are actually doing.
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。
Display input/output args, return object and cost time of a Python method invocation in a live process. Use this to observe function behavior at runtime — see what arguments go in, what comes back, and how long it takes. This is the most frequently used diagnostic command.
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。
用户需要从外部可观测信号推断内部隐藏状态时; 当需要"从表象看本质"时; 当直接观测不可行, 必须通过间接指标推断时。 不适用于: 可以直接观测内部的场景、纯数据查询、不需要推断的场景。
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。
Analyze credential-free public website monitoring evidence from server access logs, HTTP fetches, robots/llms/sitemap checks, and exported summaries while protecting raw IP and private query data. Use this skill when the user asks whether crawlers or AI bots reached a site, whether public SEO/LLM discovery files respond, or how to separate observed facts from unknown Search Console, rank, analytics, or citation 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。
Observe and assess a garden using a structured sensory protocol adapted from Coordinate Remote Viewing. Covers pre-entry clearing (meditate checkpoint), Stage I gestalt impression, Stage II sensory layer (leaf, stem, root, soil), Stage III pattern recognition with AOL management, and garden health triage matrix (heal checkpoint). Use before any intervention, when plants show stress symptoms, at seasonal transitions, when evaluating a new garden site, during regular health monitoring, or after extreme weather events such as frost or heat waves.
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。
Guide a person in systematic observation of systems, patterns, or phenomena. AI coaches neutral attention, field notes methodology, pattern recognition, hypothesis formation, and structured reporting for debugging, research, and system understanding. Use when a person wants to understand a system's behavior before intervening, when someone keeps jumping to conclusions and needs the discipline of observation first, when preparing an evidence-based report, or when studying team dynamics or process effectiveness through direct observation.
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。
Sustained neutral pattern recognition across systems without urgency or intervention. Maps naturalist field study methodology to AI reasoning: framing the observation target, witnessing with sustained attention, recording patterns, categorizing findings, generating hypotheses, and archiving a pattern library for future reference. Use when a system's behavior is unclear and action would be premature, when debugging an unknown root cause, when a codebase change needs its effects witnessed before further changes, or when auditing own reasoning patterns for biases or recurring errors.
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。
Conduct structured insect behavior observations using sampling protocols, ethogram categories, event recording, interaction logging, environmental context, and summary analysis. Covers focal animal sampling, scan sampling, all-occurrences sampling, and instantaneous sampling methods. Defines a standard insect ethogram with locomotion, feeding, grooming, mating, defense, communication, and rest categories. Includes timestamped event recording, intraspecific and interspecific interaction logging, environmental covariate documentation, and time budget analysis. Use when studying insect behavior for ecological research, documenting behavioral repertoires for a species, observing pollinator activity or predator-prey dynamics, or supporting conservation assessments with behavioral data.
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。
Build and analyze discrete or continuous Markov chains including transition matrix construction, state classification, stationary distribution computation, and mean first passage times. Use when modeling a memoryless system with observed transition counts or rates, computing long-run steady-state probabilities, determining expected hitting times or absorption probabilities, classifying states as transient or recurrent, or building a foundation for hidden Markov models or reinforcement learning MDPs.
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。
Close out a finished memex milestone slice by writing the handoff trail: update `docs/CONTINUE_HERE.md` §1 current-state and §3 next-slice, tick the `docs/ROADMAP.md` scoreboard (milestone header ✅ + version, `- [x]` items), confirm any new observation is logged (deferring to memex-observe), and propose the git tag plus a `MN Slice X:` commit subject. Use after you finish a milestone slice in the memex repo and before you commit/tag it, when handing the next slice to a fresh session, or whenever CONTINUE_HERE and ROADMAP have drifted behind what actually shipped.
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。
Evolve SKILL.md files from agent execution traces using a three-stage pipeline: trajectory collection from observed runs, parallel multi-agent patch proposal for error and success analysis, and conflict-free consolidation of overlapping edits via prevalence-weighting. Based on the Trace2Skill methodology.
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Separate expensive observation from cheap decision-making in autonomous agent loops using a two-clock architecture. A fast clock accumulates data into a digest file; a slow clock reads the digest and acts only when something is pending. Idle cycles cost nothing because the action clock returns immediately after reading an empty digest. Use when building autonomous agents that must observe continuously but can only afford to act occasionally, when API or LLM costs dominate and most cycles have nothing to do, when designing cron-based agent architectures with observation and action phases, or when an existing heartbeat loop is too expensive because it calls the LLM on every tick.
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。
Master skill composing the 5-step synoptic cycle for panoramic synthesis across multiple domains. Orchestrates meditate, expand-awareness, observe, awareness, integrate-gestalt, and express-insight into a coherent process that produces unified understanding rather than sequential compromise. Use when a problem genuinely spans 3+ domains and the interactions between domains matter more than depth in any one, when sequential analysis feels like compromise rather than integration, or before major architectural decisions affecting multiple stakeholders.
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。
Assemble the human genetic evidence for and against a target before a programme commits to it — the evidence class that most improves the odds of surviving clinical development. Use this skill to pull gnomAD constraint metrics (LOEUF, pLI, observed/expected) that show whether loss of function is tolerated in people, retrieve GWAS Catalog associations and fine-mapped credible sets for a gene, and read a natural human knockout as a safety readout. Also trigger on gnomAD, LOEUF, pLI, loss-of-function intolerance, mutational constraint, GWAS Catalog, credible set, human knockout, genetic support, or target safety dossier.
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 LibAFL fuzzer needs an executor, observer, feedback, mutator, scheduler, or objective composed around a target. Not for remote, credential, publish, deploy, or irreversible changes.
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。
Independently attempt to falsify one Proto UI autonomous-maintenance finding in a fresh non-mutating context. Use after pui-observe produced a candidate finding and route the result directly to remediation or closure when authority is clear.
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Freeze one eligible Proto UI autonomous-maintenance candidate into a bounded mission packet and lease. Use before observation when a current request or governed queue supplies scope, oracle, baseline, capability, risk, budget, completion, and stop conditions. Do not observe, verify, repair, or invent a mission.
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。
Execute one bounded read-only Proto UI autonomous-maintenance observation mission. Use for drift hunting, external-oracle comparison, repeatability control, or a no-finding maintenance run. Stop before verification or remediation.
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。
Command-line interface for HiAgent Python SDK - Observe service (API Token, Trace Spans) via CLI
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。
The Arbor research-coordinator protocol: the OBSERVE -> IDEATE -> SELECT -> DISPATCH -> DECIDE cycle over a durable Idea Tree. Preloaded automatically on /auto-research sessions. The coordinator never edits code or runs commands — executors do that in isolated worktrees; the coordinator steers the tree with idea_tree / dispatch_experiments / merge_experiment.
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。