「structural」の検索結果

[Code Intelligence] Use when you need to analyze the blast radius of current code changes using the structural knowledge graph.

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[Code Intelligence] Use when you need to query code relationships and connections using the structural knowledge graph.

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Establish or audit a structural quality contract for any agent-operated engineering repository: app, library, CLI/tool, plugin, harness, or meta repo. Use when a repo needs charter clarity, docs/decision ownership, type-native validation gates, evidence paths, safe action policy, cold-start legibility, maturity proof, repair-loop routing, consumer/producer owner-boundary routing, command surface evolution, evolutionary simplicity (see references/evolutionary-simplicity.md), or a repo ecology pass to decide what should be compressed, merged, removed, updated, created, retired, or moved to checks.

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Master orchestration for repository governance, North Star impact, sub-Star boundaries, and repair-first or evidence-first drift checks. Guides an agent through the complete lifecycle of making architectural decisions, documenting them, writing FAQs, and cleaning up stale plans while adhering strictly to repo ethics and brand tone. Use whenever you need to make a structural change, write an ADR, update the doc lattice, or govern repository architecture.

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Foundational ethics, authority hierarchy, and structural principles for the Predicate agent. Always-on law (composable system-prompt core): Truth>Harmony, Evidence>Authority, Halt>Assumption, Outcomes>Process — four ordered principles that govern every walk. Reference (by-moment): conflict resolution, ethics adjudication, novel situations, precedence walkthrough, entropy diagnostics, principled resistance. Trigger (reference depth): resolving rule conflicts, ethics calls, novel situations. Prompt contains: constitution, principles, precedence, truth over harmony, outcomes over process, evidence over authority, principled resistance.

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Durable categorized record-keeping for any predicate-enabled walk. Trigger when: - Classifying a step's output for recording (decision, finding, debt, feedback). - Promoting a scratch artifact to the ledger, to repo/docs, or to a category home (.ledger/tech-debt/, .ledger/process-feedback/). - Auditing whether a draft is structurally ready for its destination. - Running a zero-context sensibility read before committing to promotion. - Recording a known limitation as a tech-debt entry (not prose in the log). - Recording a friction, miss, improvement, or process amendment. - Prompt contains: /record, record-keeping, promote, ledger note, zettelkasten, scratch-to-ledger, zero-context read, over-strip, classification trigger, tech-debt, process-feedback, amendment.

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Structural simplicity review based on Rich Hickey's "Simple Made Easy" framework. Trigger when: - Reviewing code for complected concerns, analyzing coupling, or simplifying code architecture. - Prompt contains: /hickey, simple made easy, complect, decouple, compose.

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The crouton mental model for someone who has never seen this repo — what "layer" means (two different things), the two-halves rule, the three app.config registries, team-scoped multi-tenancy, manifest-driven field types, the numbered invariants with the incidents behind them, and the known-weak points. Use when orienting in this codebase for the first time, when a question is shaped like "how does crouton work / why is it built this way / where does X live", when deciding whether a change belongs in a package or a generated layer, when confused by an extends chain or by croutonApps/croutonCollections/croutonLayoutBlocks, or before proposing any structural change. Trigger phrases: "what is a crouton layer", "package vs generated layer", "how does multi-tenancy work here", "why is there a stub component", "where do field types come from", "what invariants must hold".

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The crouton-conformance gate — the SECOND acceptance axis of graduation (the first is the experience side-by-side, /graduate stage C1). Checks a graduated app is a REAL crouton app — CLI-scaffolded, real collections, consumes the correct packages without reinventing them — by composing the existing probes (frontend-review, a11y, e2e-smoke, typecheck) plus crouton-structural checks, then renders a required, signed-off checklist on the graduation PR. Use at /graduate stage C2, or when asked "is this app crouton-native", "run the conformance gate", "check graduation conformance".

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Review an author's manuscript, its argument and paragraph structure, analyze reviewer comments, draft rebuttals, and verify revisions. Use for self-review, structural edit plans, simulated reviewer objections, author responses, revision closure, and submission-readiness assessment. Formal assigned peer review is outside this skill; manuscript edits and final figures/tables use their owning skills.

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Draft and revise evidence-grounded academic manuscripts with adaptable author preferences. Use for section writing, implementing structural edits, claim calibration, citation-integrated prose, prose cleanup, standalone review excerpts, and LaTeX template migration. Use paper-review for critique or author responses, and paper-figures-tables for final visual artifacts.

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Default material, information-density hierarchy, clean-rendering, negative-prompt hygiene, and evidence-preserving photo-repair layer for IM2 / GPT Image 2 / gpt-image-2 / image_gen. Use for clean generation/redraws, artifact cleanup, focal-detail control, atmospheric depth, physically coherent light paths, air scattering, volumetric light, light ratio, material realism, architecture/terrain/hard-surface scene mass and structural grounding, and 企业/公司活动/门店/团队合影/商品/餐饮/证件文档照片修复. Preserve identity, headcount, placement, product facts, text, logo, QR, date, price, and scene evidence; prefer residual blur over invented restoration detail.

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Designs the data model, API contracts, and structural foundation of the system.

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Detects prompt injection attacks targeting LLM-based applications using a multi-layered defense combining regex pattern matching for known attack signatures, heuristic scoring for structural anomalies, and transformer-based classification with DeBERTa models. The detector analyzes user inputs before they reach the LLM, flagging direct injections (system prompt overrides, role-play escapes, instruction hijacking) and indirect injections (encoded payloads, multi-language obfuscation, delimiter-based escapes). Based on the OWASP LLM Top 10 (LLM01:2025 Prompt Injection) and Simon Willison's prompt injection taxonomy. Activates for requests involving prompt injection detection, LLM input sanitization, AI security scanning, or prompt attack classification.

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Architecture design and governance protocol for system structure, boundaries, contracts, APIs, schemas, migrations, rewrites, and long-lived codebase health. Use when designing or reviewing architecture, planning structural refactors or migrations, governing tech debt, or containing rewrite risk. Keeps the load-bearing spine small, owned, checked, and deletable over time.

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Two-phase code quality skill: (1) review codebase for structural smells — file LOC, method LOC, accidental complexity, test readability — producing a structured report, then (2) fix all smells via provable refactorings with ACN micro commits and green-bar-at-all-times discipline. Use this skill when the user says 'sweep', 'code review', 'find code smells', 'check method lengths', 'review for complexity', 'tidy up the codebase', 'refactor smells', or asks about long methods, test readability, or accidental complexity. Also trigger when users ask to 'clean up' or 'improve code quality' across multiple files.

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Mitigate incidental code complexity when control flow is tangled, nesting is deep, names are hard to parse, or reasoning requires cross-file hops. Use when a review stalls on readability, you need an analysis-first refactor plan before edits, or you want essential-vs-incidental verdicts, ranked simplification steps, a small structural sketch, and a TRACE assessment (analysis-only; no edits).

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Knowledge base from the NASA Probabilistic Risk Assessment Procedures Guide (NASA/SP-2011-3421, 2nd ed.). Use for quantitative PRA of aerospace and safety-critical systems: the risk triplet and scenario logic stack (MLD, ESD, event trees, fault trees, minimal cut sets), Bayesian data collection and parameter estimation, aleatory/epistemic uncertainty modeling and common-cause failure (Alpha Factor, beta-factor, CCBE), human reliability analysis (THERP, CREAM, NARA, SPAR-H), context-based software risk (CSRM), physics-based and structural/phenomenological models (stress-strength, limit states, FORM/SORM, NASGRO, range safety), uncertainty propagation and importance measures (F-V, RAW, Birnbaum, DIM), and launch-abort modeling with worked PRA examples. This is the QUANTITATIVE engine beneath NASA's risk doctrine — for the qualitative RIDM/CRM decision framework use the nasa-risk pack. Thin on programme management, organisational risk governance, and non-aerospace regulatory contexts; aerospace-focused and anchored to the 2011 second edition.

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Build an Anima LoRA from a single-character or style name, from Danbooru collection through curation, section-ordered WD14 captions, dataset checks, confirmed training, fixed-seed checkpoint validation, and a staged Civitai draft. Use for requests such as make a LoRA of X, collect images and train a character LoRA, train and upload to Civitai, or build a dataset from scratch and publish. Delegate training to lora-trainer and structural checks to dataset-doctor; never train without confirmation and never auto-publish.

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Audit and repair a kohya-style LoRA dataset before training. Use when the user asks to check, validate, inspect, fix, clean, 体检, or 修 a dataset, captions, or tags. Inspect image count, repeats and step budget, resolution and aspect buckets, duplicates, corrupt or non-RGB images, missing or empty captions, trigger consistency, and tag-frequency hygiene. Return a structural PASS, WARN, or FAIL report and apply only confirmed fix_dataset.py actions, dry-run first with displaced originals quarantined.

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Reinterpret a supplied architecture or structural-detail photograph as a bold relief-print poster with carved marks, a restrained ink palette and a tall condensed title. Use for 建筑版画海报、屋檐木刻海报、architectural linocut posters; not for a generic halftone photo filter.

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Rebuild the canon mirror for a second coding agent (AGENTS.md) after a rules-file version bump: show the changelog delta, write in only the new rules, then enforce the size cap, version stamp, structural anchors, a copy into the shared folder and the linter run. Triggers: "/codex-mirror", "update the codex mirror", "AGENTS.md is stale".

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。

Designs the data model, API contracts, and structural foundation of the system.

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 the user asks to "optimize for AI citations"; improves citation readiness for ChatGPT, Perplexity, AI Overviews, Gemini, and Claude. Not for structural on-page SEO — use on-page-seo-checker; not for net-new drafting — use content-writer. AI引用优化/GEO优化/AI搜索

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。