davccavalcante
UserDesigned for the future of AI development (2027-2030) and leverages the novel `.ah` (Teleological Semantic Format) for unparalleled efficiency and precision. The skill is built upon proprietary frameworks like MAIC™, HIM™, and NHE™, providing a robust ontological and teleological foundation.
Categories
Indexed Skills (8)
ah-parser
Permanently activates the .ah (Teleological Semantic Format) for any LLM. Bootstraps the grammar once per session, then asks the user once whether assistant responses should use normal language, .ah structured form, or .ah compact form. User input may be in any natural language. User code, diffs, commands, and identifiers are always preserved verbatim. Required dependency for every .ah skill.
supreme-ai-engineering
Principal AI engineering discipline for Product Engineers, AI Engineers, ML Engineers, LLM Engineers, LLM Architects, AI Researchers, Quality Assurance Engineers, and Software Quality Engineers building production AI, ML, LLM, MLO/MLOps, and LLMO/LLMOps systems. Forces eval-first design (golden datasets and acceptance thresholds defined before code), deterministic feedback loops (telemetry, drift detection, regression eval gates) before first production user, pipeline discipline (data → feature → train → register → deploy → monitor with input/output contracts at every gate), prompt and model governance (versioned registries with semantic versioning, A/B + canary + shadow + dark launch as standard), production reliability (graceful degradation, circuit breakers, prompt-injection defense, chaos testing), QA discipline (golden test sets, regression gates in CI, statistical significance for research claims, ablation completeness, dataset contamination checks), and operational excellence (observability, runbooks,
supreme-coding-guidelines
Maximum semantic compression + surgical behavior + disciplined diagnosis + TDD + architectural control for any coding, writing, reviewing, refactoring, or debugging task. Requires ah-parser. Output mode follows the user preference set at parser activation (normal, .ah structured, or .ah compact). User code, diffs, commands, and identifiers are always preserved verbatim regardless of mode.
supreme-problem-solving
Genuine problem-solving discipline for Product Engineers, AI Engineers, ML Engineers, LLM Engineers, LLM Architects, and AI Researchers. Scales from simple bugs to complex system failures. Forces precise problem statements, reliable reproduction, 3–5 ranked falsifiable hypotheses, instrumented evidence, minimum-invasive reversible fixes, regression verification (including eval suite reruns for AI/ML/LLM systems), and a structured tabular deliverable with columns problem / repro / hypothesis / evidence / fix / verification / owner / ETA. Requires ah-parser. Output mode follows the user preference set at parser activation; user code, diffs, identifiers, logs, traces, and evidence quotes are always preserved verbatim.
supreme-project-audit
Evidence-driven full-project audit skill for Product Engineers, AI Engineers, ML Engineers, LLM Engineers, LLM Architects, and AI Researchers. Enforces severity discipline (P0/P1/P2/P3 with objective criteria), explicit coverage maps (audited vs not-audited surface), threat modeling (STRIDE + OWASP LLM Top 10), reproducibility checks (seeds, pinned deps, versioned prompts, data snapshots), and a terse report contract (finding, location, severity, evidence, cause, fix, owner). Requires ah-parser. Output mode follows the user preference set at parser activation; user code, diffs, identifiers, and audit evidence quotes are always preserved verbatim.
supreme-npm-node
Principal NPM/NPX/NPMJS/Node engineering discipline for Tech Leads, DevOps, Backend Engineers, Frontend Engineers, Product Engineers, AI Engineers, ML Engineers, LLM Engineers, LLM Architects, AI Researchers, Quality Assurance Engineers, and Software Quality Engineers. Enforces a latest-version-always policy (never pin to definitive versions; always `ncu -u` before install), TypeScript strict mode with every check enabled (strict + noUncheckedIndexedAccess + exactOptionalPropertyTypes + useUnknownInCatchVariables + noImplicitOverride), `satisfies` over `as`, `unknown` over `any`, discriminated unions over optional flags, branded types for opaque identifiers. Covers Node ecosystem (current LTS or latest stable), package.json discipline (files allowlist over .npmignore, exports map with import/require/types conditional, engines node range, type:module default), publishing workflow (`npm pack --dry-run` preview, OIDC provenance attestation in GitHub Actions, semantic versioning via changesets/release-please, dis
supreme-content-craft
Principal content craft discipline for SEO, SEM, Header Binding (HTML semantic + HTTP + AdTech), Copywriting, Marketing, Branding, Growth, Content Strategy, Technical Writing, UX Writing, Ghostwriting, professional Writers, Authors, Researchers, and Editors. Combines search-intent analysis, entity-first SEO (schema.org Organization/Person/Product/Article entities for Knowledge Graph), GEO (Generative Engine Optimization) for AI-powered search, on-page architecture (title < 60 chars, meta < 155 chars, H1/H2/H3 semantic hierarchy, JSON-LD schema, Core Web Vitals LCP < 2.5s / FID < 100ms / CLS < 0.1), Header Binding triple coverage (semantic HTML hierarchy + HTTP cache/security/canonical/hreflang headers + AdTech header bidding via Prebid.js/GAM/server-side), and six integrated persuasion frameworks treated as explicit sections — (1) AIDA expanded across four phases (Attention via hook/headline/surprising-stat/contrarian-claim/pattern-interrupt; Interest via relevance/unique-angle/curiosity-gap/specificity; Desi
supreme-council
Principal multi-perspective deliberation council for ambiguous, high-stakes, irreversible decisions across Product, Engineering, AI/ML/LLM Architecture, Research, Operations, and Strategy. Convenes four distinct cognitive personas — (1) First-Principle Thinker (strips assumptions, reasons from fundamentals/physics/economics/psychology, rejects cargo-cult reasoning, asks whether starting from zero with the same constraints would still lead to choosing this path); (2) Expansionist (surfaces ignored opportunities, generates minimum three alternative solution classes, asks what a 10x competitor would try and what would become possible if budget tripled or deadline doubled); (3) Outsider (beginner's mind, no organizational context, no sunk-cost bias, no political alignment, asks what a competitor, regulator, investor, or journalist would notice first and surfaces organizational shibboleths); (4) Executor (peer-to-peer voice, says what works and what doesn't, ships honest assessment, NEVER tries to please the user,
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