「values」の検索結果

Business-logic flaw hunting - workflow/state bypass, price/quantity tampering, negative/overflow values, coupon/refund abuse, mass assignment, and logic races. The top-paying bug class with no scanner coverage. Wiki-first, FIND schema output.

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Read huge, dense, read-only text as rendered PNG pages instead of raw text — image tokens scale with pixels, not characters, so token-dense bulk (big files, vendored code, diffs, logs) costs ~60–75% less as an image. Use when you must skim or reason over thousands of lines you will NOT edit or byte-copy. Lossy on exact strings: never for files you'll Edit, secrets, hashes, or byte-exact values. Fable-class readers only.

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Create, scaffold, or generate Helm charts, Chart.yaml, values.yaml, templates, helpers.

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Validate, lint, audit, check Helm charts — Chart.yaml, templates, values.yaml, CRDs, schemas.

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Apify integration. Manage Actors, Datasets, KeyValueStores, RequestQueues, Tasks. Use when the user wants to interact with Apify data.

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Fix misleading Flutter/Dart test failures where expected data is empty ([]) or default values instead of the mocked response, caused by stale Mockito generated mocks or missing stubs being silently swallowed by try/catch in provider/repository code. Use when: (1) Test expects non-empty data but gets [], (2) Mock stubs for a method are set up but the code under test returns fallback/empty results, (3) A PR added new methods to a service class and tests that previously passed now fail with misleading "expected X, actual: empty" assertions, (4) "Generated files are out of date" CI error alongside test failures. The root cause is MissingStubError thrown by unstubbed mock methods, caught by production try/catch blocks, causing silent fallback to empty state.

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Optimize slow PostgreSQL inserts in Python using psycopg2. Use when: (1) Row-by-row inserts are taking too long over network, (2) executemany() isn't providing speedup, (3) Migrating large datasets to PostgreSQL, (4) Network latency making individual INSERT statements impractical. The key is using execute_values() from psycopg2.extras instead of executemany() or individual execute() calls.

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Use database cache when external APIs aren't reliably idempotent. Use when: (1) External API claims idempotency but returns different values for same input, (2) Re-running a script creates duplicate resources, (3) Need stable identifiers across runs but external service generates new ones. Pattern: check database cache first, only call external API for genuinely new items, cache the result.

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Fix LRU-bounded Flutter/bloc state classes that silently lose insertion-order updates. Use when: (1) a state class uses `Equatable` with a `LinkedHashMap<K, V>` field to preserve insertion order (LRU, MRU, recent-items caches), (2) `Cubit.emit` appears to drop emissions when an existing key is re-inserted or moved to most-recent, (3) LRU eviction evicts the "wrong" entry after a refresh, (4) a test that reports the same key twice and then overflows the cap sees the refreshed entry evicted instead of the oldest. Root cause: Equatable's default map comparison is structural (unordered), so reordering without changing keys/values produces an "equal" state that `Cubit.emit` suppresses.

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Fix ClickHouse query errors when querying AggregatingMergeTree tables that use state functions. Use when: (1) Query fails with type mismatch on AggregateFunction columns, (2) Using sum/count on columns created with sumState/countState/uniqState, (3) Creating views that JOIN with materialized views using AggregatingMergeTree, (4) Getting unexpected results from aggregate columns that show as AggregateFunction(sum, ...) type. The *State() functions store intermediate aggregate states, not final values - you must use *Merge() functions to finalize them.

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Fix incorrect priority ordering when using denormalized aggregate columns. Use when: (1) Records are processed in wrong order despite ORDER BY on count/sum columns, (2) Top items by some metric aren't being selected first, (3) Aggregate columns show 0 or NULL for records that should have high values, (4) Priority queue processes low-value items before high-value ones. The root cause is often that denormalized columns (vine_count, loop_count, total_orders, etc.) weren't backfilled or maintained properly. Solution: JOIN with source tables to compute actual aggregates at query time.

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Fix ClickHouse query errors in Rust when using clickhouse-rs crate. Use when: (1) "string is not valid utf8" errors - typically FixedString columns need CAST to String, (2) "tag for enum is not valid" errors - typically Option<T> fields receiving non-NULL values, (3) Sum/count aggregations returning Float64 but Rust expects u64, (4) LEFT JOIN results with empty strings where Rust expects Option::None, (5) "not enough data, probably a row type mismatches a database schema" - query SELECT returns fewer columns than the Rust Row struct expects (common when multiple query functions share the same struct but one function is missing columns added later), (6) Garbled/corrupted data when INSERTing to FixedString(N) columns using String Rust type - silently corrupts data (no error!) because String adds a length prefix but FixedString expects exactly N raw bytes. Fix: use [u8; N] with #[serde(with = "BigArray")] from serde-big-array, (7) "the trait bound `[u8; 64]: serde::Serialize` is not satisfied" when using [u8; N] arrays in Row structs - need #[serde(with = "BigArray")] annotation from serde-big-array crate.

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Use when implementing select values from a range.

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Use when implementing enter and format monetary values.

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Generates automated test oracles to verify correct software behavior. Creates assertion-based oracles (expected values), property-based oracles (invariants), differential oracles (comparing implementations), and metamorphic oracles (input transformations). Use when you need to generate assertions for test cases, identify invariants that should always hold, compare new vs legacy implementations, create metamorphic test relationships, validate function correctness, or improve test coverage with better verification strategies. Supports Python (pytest, unittest, hypothesis) and Java (JUnit, property testing).

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Selectively instruments code to capture runtime data for debugging failures and bugs. Use when investigating crashes, exceptions, unexpected behavior, test failures, or performance issues. Analyzes stack traces and error messages to identify suspicious code regions, then adds targeted logging, tracing, and assertions to capture variable values, execution paths, timing, and conditional branches. Supports Python, JavaScript/TypeScript, Java, and C/C++.

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Analyzes code to identify security-critical time intervals and timing vulnerabilities in authentication, authorization, and time-sensitive security operations. Use this skill when reviewing code for proper timeout enforcement, token expiration, session management, rate limiting, password reset validity, or any time-sensitive security mechanism. Detects missing expiration checks, excessive timeout values, lack of rate limiting, client-side only validation, hardcoded timeouts, and timing attack vulnerabilities. Triggers when users ask to check security timeouts, verify token expiration handling, audit session timeout implementation, review rate limiting, or analyze time-based security controls.

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Identify and report code smells indicating poor design or maintainability issues in Python code, including duplicate code, magic numbers, hardcoded values, God classes, feature envy, inappropriate intimacy, data clumps, primitive obsession, and long parameter lists. Use when conducting code quality audits, preparing for refactoring, improving codebase maintainability, or performing design reviews. Produces markdown reports with severity ratings, locations, descriptions, and specific refactoring recommendations with before/after examples. Triggers when users ask to find code smells, identify design issues, suggest refactorings, improve code quality, or detect maintainability problems.

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Generate concrete counterexamples when formal verification, assertions, or specifications fail. Use this skill when debugging failed proofs, understanding why verification fails, creating minimal reproducing examples, analyzing assertion violations, investigating invariant breaks, or diagnosing specification mismatches. Produces concrete input values, execution traces, and state information that demonstrate the failure.

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Automatically analyzes configuration files to detect inconsistencies, conflicts, missing keys, and divergent values across environments, versions, or modules. Use when managing multi-environment configurations, detecting config drift, validating configuration changes, or ensuring consistency across microservices. Supports JSON, YAML, TOML, INI, XML, .env, and properties files. Identifies security issues like hardcoded secrets and provides actionable resolution guidance.

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ACTIVATE when drafting all-hands speeches, investor updates, quarterly letters to stakeholders, press statements, CEO announcements, crisis communications, customer updates, or any mission-critical communication to employees, investors, customers, or media. Executive communications require persuasion, clarity, emotional intelligence, and alignment with company values and strategy.

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Authoritative SwiftUI best practices from Apple: @Observable data flow, identity in ForEach/List, animations, environment values, and performance.

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Apple Human Interface Guidelines as structured design values — system colors (incl. increased-contrast variants), the full Dynamic Type ramp, Liquid Glass rules and budgets, layout and hit-target metrics, SF Symbols, and HIG-to-SwiftUI component mappings. Use when styling or reviewing any Apple-platform UI: picking a color, size, spacing, corner radius, or hit target; writing .glassEffect / material code; choosing an SF Symbol; or answering 'what does the HIG say about X'.

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Clean and preprocess datasets by handling missing values, removing duplicates, correcting types, resolving outliers, and enforcing validation schemas. Use when the user requests data cleaning or provides relevant inputs for this workflow.

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