Steven Diamante

Gauntlet for absolute claims (always/never/guaranteed/optimal); pressure-test, then refine with explicit boundaries. Use when users ask to prove or disprove strong certainty claims, request devil's-advocate challenge rounds, or want the /tw-prove-it gauntlet to run all 10 rounds continuously by default.

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Audit over-engineered codebases by factoring layers into live obligations, quotienting redundant distinctions, ablating unearned surface, and normalizing survivors while preserving required behavior. Use when change latency or agent difficulty comes from frameworks, plugins, DI, codegen, task runners, config indirection, ORMs, GraphQL, monorepo/infra tooling, or web stacks, or when asked to remove layers. Produces an evidence-backed Reduction Certificate, cuts, migration phases, proof signals, and rollback. Not for one local readability cleanup (use tw-complexity-mitigator).

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Run one explicit escalation pass using the original glaze prompt words verbatim. Use when prompts say `/tw-glaze`, when the first answer feels merely adequate, or when you want the exact original rhetoric preserved while pushing for a materially stronger direction.

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Perform comprehensive exploratory testing on a website using browser automation, covering user journeys, forms, errors, accessibility, responsiveness, performance, and security basics. Use when asked to QA a site, explore an app, find bugs, or produce a browser testing report.

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Use when modifying, removing, or refactoring code that lacks test coverage. Emphasizes the danger of untested changes and the RGR workflow to add characterization tests before modifications.

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Compile up to five durable insights from the current conversation for possible addition to CLAUDE.md. Use before clearing context, when asked to remember lessons, or when the user wants project conventions and preferences preserved.

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Remove AI writing patterns from prose. Use when drafting, editing, or reviewing text to eliminate predictable AI tells.

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Compile deep repository evidence into artifact-bound correctness doctrine — authority/law/proof maps, owned invariants, failure archaeology, and the strongest durable destination for each piece of knowledge, plus an optional minimal repository-specific skill portfolio. Use when the user wants both deep codebase understanding AND durable doctrine, knowledge routing, or repo-specific skill recommendations. Research discoverable facts before asking. Read-only. Not for quick onboarding, one isolated invariant, ordinary implementation, generic review, or direct skill creation.

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Review+fix protocol with optional pre-core git diff review and safety guardrails (unsoundness, invariants, footguns, incidental complexity). Use when prompts say "/tw-fix this PR", "fix current branch", "fix this diff", "repair CI red", or "apply a minimal patch", and when crash/corruption/invariant-break issues need correction with a validation signal. Stop only after self-review exhausts actionable changes, each full cycle reaches post-self-review rerun plus terminal diff-review closure, and two consecutive zero-edit full cycles are clean.

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Mine a codebase for breakthrough, evidence-backed opportunities — features, additions, refactors, simplifications, DX, UX, reliability, performance, or architecture cleanup. Researches project reality first, then forces two escalation gates (reject the merely-adequate idea; expand ambition 10x then collapse to the smallest proof-bearing artifact). Outputs ranked opportunities, an escalation ledger, and one plan seed. Does not implement or create tickets.

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Turn 'should never happen' into 'cannot happen' by defining owned inductive invariants and enforcing them at parse/construct/API/DB/lock/txn boundaries with a verification signal. Use when prompts mention invariants, impossible states, validation sprawl, cache/index drift, idempotency/versioning, retries/duplicates/out-of-order events, race/linearization bugs, loop correctness, or hardening another workflow (for example /tw-fix) with invariant checks first.

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Comprehensive, measurement-driven performance optimization for latency, throughput, memory/GC, and tail behavior. Use when the user asks to optimize/speed up, reduce latency (p95/p99), increase throughput/QPS, lower CPU/memory/allocations/GC pauses, profile hot paths, or run a benchmarked perf pass (including JSONL/query-heavy code). Requires before/after measurement on a runnable workload (or an explicit `UNMEASURED` plan) plus a correctness gate.

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Rebuilds session context after a clear by reading git history, plans, and beads state. Triggers on 'catch up'.

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Comprehensive code review for quality, security, performance. Triggers on 'review code', 'code review', 'check this PR', 'audit code'.

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Mutation testing patterns for verifying test effectiveness. Use when analyzing branch code to find weak or missing tests.

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