Network-science primitives: centrality, influence seeding, communities, contagion vs homophily, GNN failures. Use when seeding a referral campaign or finding critical graph nodes.
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@vasilyu1983
CAIDO at Altery (UK EMI) · 20 yrs fintech · I build and operate multi-agent systems in regulated environments
144 个技能1 个仓库 101
Network-science primitives: centrality, influence seeding, communities, contagion vs homophily, GNN failures. Use when seeding a referral campaign or finding critical graph nodes.
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Sizes queues and worker pools. Use when estimating Kingman G/G/1 mean waiting time, Erlang or Little's Law sizing, p99 under load, retry storms, worker counts.
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PID/MPC/Kalman/anti-windup control primitives for feedback loops. Use when an autoscaler flaps, a controller overshoots after saturation, or budget pacing overspends at peak.
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Builds Excel charts, pivots, validation, exports, Office Scripts/Graph automation, and workbook audits. Use when automating workbooks; for plain edits prefer official xlsx skill.
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Analyzes hazards in systems or AI agents: STPA, guardrails, safety cases, incident analysis. Use when an agent can take actions or send payments.
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Team-theory primitives for cooperative agents: what each subagent sees, when to communicate, aggregation, Shapley credit, coalition topology. Use when organizing subagents.
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Generates or validates Conventional Commits messages from staged diffs. Use when drafting commit messages, checking repo rules, or inferring scope from changed files.
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Writes implementation plans before coding: phases, plan files, specs, definition of done. Use when planning a multi-step feature or breaking a large refactor into phases.
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Writes and reorganizes docs-as-code for software repos. Use when updating READMEs, runbooks, onboarding docs, API references, or agent instruction files.
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Builds local-first note vault retrieval for Obsidian, markdown notebooks, and NotebookLM-adjacent exports. Use when packaging notes into LLM-ready context.
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Builds docxtpl templates, DOCX extraction for RAG, and EAA accessibility checks. Use when templating Word or extracting tables; plain edits and redlines: official docx skill.
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Handles PDF/UA accessibility, real redaction, metadata scrubbing, signing, and extraction. Use when redacting or signing PDFs; for plain create/merge prefer the official pdf skill.
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Builds and edits PowerPoint (.pptx) slide decks and templates with PPTX-Automizer. Use when you make or build a slide deck or presentation, or repair one.
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Applies planning/search theory (A*, CSP, MCTS, PDDL, HTN) to agents. Use when ordering tool calls, enforcing preconditions before side effects, or validating a plan.
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16 evidence-graded behavioral-economics primitives. Use when asking will this nudge work, is a pricing decoy or dark pattern lawful, or what effect size to expect.
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Tests whether changes caused a metric move without a clean A/B test: DiD, synthetic control, IV, RDD. Use when asking did a launch cause lift.
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Evaluates neuromarketing studies and neural-data law. Use when judging EEG, fMRI, eye-tracking, or GSR ad study or vendor, or if signals are regulated neural data.
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Diagnoses stalled teams or agent hierarchies with Beer's VSM and Ashby's Law. Use when orchestrators overload, escalations pile up, or team boundaries need redesign.
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Chooses under uncertainty: value of more data, cost-based thresholds, abstention, when to stop a test and ship, robust choice. Use when deciding, not estimating.
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Designs consensus, quorums, leases, idempotency, and CRDTs. Use when facing split brain, fencing tokens, exactly-once claims, duplicate retries, stale reads, or leader flapping.
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Proves protocols and agent workflows with TLA+, P, Alloy, Z3, Dafny, or Lean. Use when proving protocols correct, writing TLA+ specs, or verifying LLM proofs.
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Game-theory primitives for divergent incentives: Nash equilibrium, auctions, mechanism design, collusion, bargaining, reputation. Use when modeling strategic play.
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Designs streaming platforms for Kafka, Flink, CDC, and lakehouse ingestion. Use when planning event backbones, CDC pipelines, schema governance, or real-time lakehouse delivery.
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Measures AI coding impact and extension robustness. Use when tracking delivery, quality trajectories, cost, experience, pilots, scorecards, or leadership reporting.
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