AI & ML

bilal140202 Skill

Use when the user needs X (Twitter) data through Xquik: tweet search, user lookup, follower export, media download, monitoring, webhooks, MCP, SDK setup, or confirmation-gated publishing workflows. Read-only by default, API-key only, no X login material, and every write, private read, monitor, webhook, or metered bulk job requires explicit approval.

bilal140202 Skill

Knowledge base management, ingestion, sync, and retrieval across multiple storage layers (local files, MCP memory, vector stores, Git repos). Use when the user wants to save, organize, sync, deduplicate, or search across their knowledge systems.

bilal140202 Skill

Skill especializada em criação musical completa e prompts para Suno AI. Cobre: letras, briefs de produção, estrutura de canções, identidade vocal, style tags e análise de artistas. Use sempre que o usuário pedir música, letra, hook, refrão, rap verse, bridge, prompt Suno, style tags ou qualquer composição — mesmo pedidos casuais como "escreve uma música sobre X", "cria um prompt Suno de K-pop", "rap de anime no estilo Anirap". Ativa também para análise de estruturas, desconstrução de gêneros e músicas para RPG. Ativar para: música, letra, lyric, song, track, refrão, hook, verso, bridge, produção musical, vocal, rap, canção, trilha, K-pop, J-pop, J-rock, anime opening, anime ending, OST, metalcore, nu metal, rock, trap, R&B, Suno AI, style tags, rap de anime, anisong, anirap, M4rkim, Enygma, Chrono, Linkin Park, Skillet, grupo musical, prompt musical.

bilal140202 Skill

Cluster hardened instincts (high-confidence feedback_*/discovery_* memories) into a proposed higher-level structure — a Command, Skill, or Agent. Run when many related instincts have accumulated in one domain. Part of the Instinct Engine. Do NOT use for one-off pattern capture (use /patterns) or daily journaling.

bilal140202 Bundle

Dual-AI code analysis pairing z.ai GLM 5.2 with Claude code-searcher — a lightweight two-model second opinion. Use for a quick z.ai-backed check on a code question.

bilal140202 Skill

Walks every daily journal entry in a date range (default this year) and appends a "Body track" section BELOW the original verbatim content. Pulls health-mcp data for each date (HRV, RHR, sleep, cycle phase, lab status, recovery/sleep/strain scores) and weaves a Floor-paired interpretation. Idempotent (skips entries that already have the section). Use when user says /backfill-journal-body-context, asks to enrich journals with body data, says "backfill my journals with health" or wants existing journal entries paired with their Apple Health / Oura / Fitbit data retroactively.

bilal140202 Skill

Backend specialist for APIs, databases, authentication using FastAPI with clean architecture (Repository/Service/Router pattern)

bilal140202 Skill

Use when syncing research documents to Notion. Handles Notion synchronization operations including state management and encoding validation.

bilal140202 Skill

Design, implement, and debug autonomous AI agents and multi-agent systems in Rust using the Google Antigravity (AGY) Rust SDK. ACTIVATE this skill when the user wants to create, configure, or orchestrate Google Antigravity agents in Rust.

bilal140202 Skill

Automated multi-agent orchestrator that spawns CLI subagents in parallel, coordinates via MCP Memory, and monitors progress

bilal140202 Skill

Search 2500+ curated ChatGPT and LLM open-source repositories. Use when the user asks to find tools, libraries, or repos related to ChatGPT, LLMs, RAG, agents, langchain, NLP, AI development, or any open-source AI tooling.

bilal140202 Skill

Use when you are about to declare a task complete. Ensures the fix actually works and prevents premature completion claims.

bilal140202 Skill

Design, implement, and debug autonomous AI agents and multi-agent systems using the Google Antigravity (AGY) SDK. ACTIVATE this skill when the user wants to create, configure, or orchestrate Google Antigravity agents.

bilal140202 Bundle

Given a Japanese NLP GitHub repo (URL / owner/repo / tool name), find repositories that do the same or related processing. Mines the bundled dataset for content-similar items, then expands via web research, then merges and re-ranks.

bilal140202 Bundle

Power BI custom visual (.pbiviz) development with the pbiviz toolchain and its MCP server. Automatically invoke when the user mentions "custom visual", "pbiviz", "develop a Power BI visual", "powerbi-visuals-tools", "IVisual", "capabilities.json", "visual formatting model", "visual certification", "publish a visual to AppSource", or asks to scaffold, build, debug, package, certify, or publish a .pbiviz developer visual.

bilal140202 Skill

当 Agent 需要执行任何命令时，强制将命令封装为 Python subprocess 脚本后执行，彻底绕过 Windows UAC 参数审查，实现无人值守自动化。

bilal140202 Skill

Interactive naming convention standardization for TMDL-based Power BI semantic models. Automatically invoke when the user asks to "standardize naming conventions", "fix naming conventions", "clean up model names", "apply naming standards", "audit naming", "make names human readable", "rename fields", "fix abbreviations in model", or mentions renaming measures, columns, or tables for consistency across a model.

bilal140202 Bundle

Analyze current trends in Japanese NLP for a topic. Surveys the existing awesome-japanese-nlp-resources dataset and augments it with up-to-the-minute web research to produce a digestible trend report.

bilal140202 Skill

One-click initialization of a multi-agent repository from the Antigravity template. Use this skill when users want to scaffold a new project quickly (`quick` mode) or with runtime defaults (`full` mode) including LLM provider profile, MCP toggle, swarm preference context, sandbox type, and optional git init.

jacob-balslev Bundle

Use when reasoning about systems that pass untrusted content to a language model: the data-vs-instruction collapse that makes this attack class a structural property of LLMs rather than a fixable bug, the direct/indirect/exfiltration/action-trigger taxonomy, the role of every untrusted surface (RAG retrievals, tool results, attachments, web content, document parsing, user-provided text), why content filters and improved system prompts do not solve it, and the defense-in-depth measures that do (capability constraint, content origin tracking, separate planning and execution stages, human-in-the-loop gates, principle-of-least-authority for tools). Do NOT use for model refusal policy or jailbreak evals (use `guardrails` or `eval-driven-development`), for general application security (use `owasp-security` or `security-fundamentals`), for runtime input validation patterns (use `type-safety` + `api-design`), or for the protocol cycle of tool calls (use `tool-call-flow`).

jacob-balslev Bundle

Sweller's Cognitive Load Theory (CLT) for agents reviewing skill bodies, prompts, documentation, dashboards, and agent outputs for avoidable cognitive burden. Working memory holds roughly 4 chunks at a time; CLT classifies load into intrinsic (irreducible task difficulty), extraneous (unnecessary load from poor presentation, ELIMINATE), and germane (the schema-building processing applied to intrinsic load, PROTECT). Use when writing a SKILL.md body (does this section add extraneous load?), designing prompts (am I asking the model to hold too much at once?), building dashboards (what is the per-screen cognitive budget?), authoring docs (is intrinsic load segmented?), or checking whether modern features (long context, structured outputs, prompt caching, subagents) actually reduce load or just move it elsewhere. Do NOT use for retrieval and session working-set design (use context-management), token budget math and compaction timing (use context-window), prompt engineering tactics (use prompt-craft), token-effici

jacob-balslev Bundle

Use when reasoning about baseline application-security properties: threat modeling, trust boundaries, Saltzer and Schroeder design principles, input validation, authentication vs authorization, secrets handling, secure-by-default choices, least privilege, defense in depth, and OWASP vulnerability classes as recurring failure modes. Covers cross-cutting decisions about what is trusted, where validation belongs, where authn/authz checks live, and how to bound blast radius. Do NOT use for LLM-specific prompt injection or agent-tool authority (use prompt-injection-defense), OWASP-category deep code review (use owasp-security), vendor webhook mechanics (use webhook-integration), cryptographic primitive implementation or key-management mechanics (use vendor/KMS/library docs), compliance/legal artifacts, or the social/organizational side of security.

jacob-balslev Bundle

Use when designing or evaluating a production AI agent system, choosing a multi-agent coordination pattern (orchestrator/worker, fan-out, consensus, sequential chain, evaluator/optimizer), diagnosing coordination failures (claim races, silent stalls, context contamination, runaway loops), or auditing whether an agent loop is truly production-ready. Covers the four pillars (architecture and lifecycle, task decomposition, coordination patterns, production reliability), the six reliability requirements (observability, cost budgets, idempotency, failure recovery, safety caps, claim locks), the delegation decision framework with overhead crossover, and the most common anti-patterns. Do NOT use for prompt wording (use `prompt-craft`), per-call tool efficiency (use `tool-call-strategy`), context-stack design within a single agent (use `context-engineering`), or runtime debugging of a deployed system (use `debugging`).

jacob-balslev Bundle

Use when serializing merges across multiple agent branches, resolving conflicts between agent outputs, or cleaning stale task branches. Covers atomic locking, idempotency checks, non-fast-forward handling, and worktree cleanup. Do NOT use for ordinary git operations outside an agent merge queue (use `version-control`).

jacob-balslev Bundle

Use when planning multi-step implementations, designing quality gates, establishing verification protocols, or building agent checklists calibrated to known failure modes. Covers methodology/method/process distinctions, Cleanroom, PSP/TSP, hypothesis-driven development, DMAIC, checklist design, V&V frameworks, EDDOps, quality gates, and PDCA. Do NOT use for code-review verdicts (use `code-review`), behavior-preserving implementation work (use `refactor`), or test strategy (use `testing-strategy`).

jacob-balslev Bundle

Use when reasoning about tests that specify universal properties of code rather than specific input-output pairs: the forall(input) → property quantification, the generator/shrinker primitives that produce inputs and minimize failing cases, the four-rules-of-simple-design analog (commutativity, associativity, idempotence, round-trip, oracle, invariant), the difference between example-based tests (one input, one assertion) and property-based tests (many generated inputs, one universal claim), why property tests find bugs example tests don't, the shrinking discipline that produces minimal failing cases, and the trade-off between generator complexity and bug-finding capacity. Do NOT use for specifying one concrete behavior with one input (use example-based tests under testing-strategy), for fuzz-testing focused on crashes (use fuzz-testing), for mutation testing as a test-suite quality signal (use mutation-testing), or for model-based testing of state machines (use state-machine-modeling).

jacob-balslev Bundle

Use when reasoning about code coverage as a strategic measurement rather than a quality target: structural reach versus behavioral verification (covered vs tested), the coverage-criterion hierarchy (function, line, branch, decision, condition, MC/DC, path) and matching the criterion to where a module's failure modes hide, Marick's floor-vs-ceiling distinction, Goodhart's Law applied to coverage and Goodhart-resistant policy (diagnostic use, diff/patch floors, risk-weighting, behavioral panels), denominator hygiene and cross-tool counter semantics, safety-critical MC/DC (DO-178C Level A required; ISO 26262 ASIL-D highly recommended) as requirements-based evidence, and interpreting coverage from AI-generated tests. Do NOT use for choosing test levels (use testing-strategy), mutation testing as a behavioral signal (use mutation-testing), test-double construction (use test-doubles-design), LLM eval iteration (use eval-driven-development), or specific coverage-tooling configuration (tool docs).

jacob-balslev Bundle

Use when reasoning about Test-Driven Development as a design discipline rather than a workflow: the red-green-refactor cycle as a feedback loop, the difference between London-school (outside-in, interaction-heavy, mock-driven) and Detroit-school (inside-out, state-heavy, classicist) TDD, the role of TDD as a design tool (how tests pressure code into more decomposable shapes), the connection between TDD and emergent design, the boundary between TDD and prior-test-suites, why TDD's failure mode is not 'no tests' but 'tests that mirror implementation', and the empirical record of TDD's effects on defect density, design quality, and development velocity. Do NOT use for the strategy of what to test at which level (use testing-strategy), the construction of test doubles (use test-doubles-design), the discipline of LLM eval iteration (use eval-driven-development), or general-software process workflow (use the obra/superpowers test-driven-development workflow skill — this skill is the concept-shape complement).

jacob-balslev Bundle

Use when reasoning about the theory beneath data modeling: Codd's relational model and algebra, normal forms from 1NF through 5NF/BCNF, functional dependencies and closure, Chen ER modeling, principled denormalization, relational-vs-document tradeoffs, and immutable-data alternatives such as event sourcing or append-only tables. Do NOT use for practical persistence design (use entity-relationship-modeling), applying schema changes (use schema-evolution), index choices (use indexing-strategy), or conceptual modeling above the data model (use conceptual-modeling).

jacob-balslev Bundle

Enforces five non-negotiable quality pillars that counter the shortcuts an agent takes under training pressure: complete reporting (show ALL items, never filter unilaterally), verification (no claim of works/done/exists without a tool-call receipt in the same turn), thoroughness (every acceptance criterion individually verified; docs ship with the change), enrichment ('improve' means add capability, never trim), and anti-shortcut (exhaust deterministic lookup before guessing; findings demand action, not just filing). Use when reviewing any enumerated output for completeness, when an agent claims something works without evidence, when marking a task done, when asked to 'improve' or 'clean up' anything, or when findings are filed without being acted on. Do NOT use for the deep explanatory model of WHY completeness fails or the step-level execution mechanics (use `methodical`), for the cross-domain quality-standards catalog like OWASP/WCAG/SOLID (use `best-practice`), for scoring whether a result is good enough

jacob-balslev Bundle

This skill provides decision frameworks for choosing the optimal execution path before starting work: plan-vs-act gates, agent architecture selection (chaining, routing, parallelization, orchestrator-worker, evaluator-optimizer), scope management heuristics, critical-path analysis across task networks, and context budget awareness. Use when deciding how to approach a task (plan first vs act immediately), decomposing complex work into parallelizable subtasks, choosing between subagent patterns, or when a task has failed twice and needs a fresh approach. Do NOT use for executing the chosen plan (use task-execution), debugging failures (use troubleshooting or diagnosis), or tool-level efficiency (use tool-call-strategy).

jacob-balslev Bundle

Use when starting a non-trivial feature, refactor, or agent implementation that needs a written spec, plan, task breakdown, and verification path before code changes. Covers Spec Kit-style SDD phases, requirements-vs-plan separation, task traceability, review gates, and spec-compliance verification. Do NOT use for one-line edits, README-only fixes, post-implementation code review (use `code-review`), or test-level decisions (use `testing-strategy`).

codyswanngt Bundle

Make a brownfield project…

signal-execution-labs Bundle

Skill #85: Model Failover Manager

fabioc-aloha Skill

Intelligent project persona identification using priority chain detection with LLM and heuristic fallback

fabioc-aloha Skill

Debug non-functional M365 Copilot declarative agents.