「report」の検索結果

Systematically explore and test a web application to find bugs, UX issues, and other problems. Use when asked to "dogfood", "QA", "exploratory test", "find issues", "bug hunt", "test this app/site/platform", or review the quality of a web application. Produces a structured report with full reproduction evidence -- step-by-step screenshots, repro videos, and detailed repro steps for every issue -- so findings can be handed directly to the responsible teams.

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 direct Python execution for reliable document creation including spreadsheets, PDFs, and structured reports

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。

Generate documents with write_file when retrieval tools fail, with explicit guardrails against task context drift

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。

Create and modify Excel workbooks with mandatory deterministic Python verification of workbook structure, required sheets, populated columns, and sample counts before reporting completion.

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。

Generate professional documents using write_file when primary data sources fail, leveraging embedded domain knowledge instead of external retrieval

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Complete PDF lifecycle: download, extract, and generate structured documents with reportlab

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。

Generate spreadsheets, diagrams, and PDF reports with iteration budgeting and error recovery

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。

Systematic fallback workflow for PDF generation through pandoc, reportlab, and fpdf2 with installation verification

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。

Generate structured PDF checklists and reports with tables, sections, and scoring criteria using Python libraries

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。

Generate spreadsheets, diagrams, and PDF reports in a phased workflow with explicit iteration budgets and error recovery

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。

Generate structured PDF checklists and reports using Python libraries in sandbox

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。

Report tool failures transparently and attempt minimal viable output with disclaimers

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。

Document generation with direct pandoc/ReportLab execution (lightweight default) and optional shell_agent fallback for complex scenarios

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。

Audit generated Excel reports by enumerating populated output rows, exposing formulas, and checking that each subtotal range includes all visible detail rows.

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。

Verify delegated file-edit results directly in the target workspace before accepting success, and rerun with absolute paths if the delegate's report conflicts with reality.

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。

Runs full Trailmark structural analysis by building a graph, running `preanalysis()`, and reporting hotspots, taint, blast radius, privilege boundaries, attack surface, and version-gated Trailmark 0.4+/0.5+ data such as proxy counts, subgraph edges, type/reference summaries, and entrypoint attributes. Use when vivisect needs detailed structural data for a target. Triggers: structural analysis, blast radius, taint analysis, complexity hotspots, proxy nodes, type references.

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Designs and improves fuzzing harnesses for C/C++ and Rust. Covers mapping raw bytes onto a target API, generating structured inputs, avoiding non-determinism and false crashes, and deciding what to fuzz together. Use when writing a first LLVMFuzzerTestOneInput or fuzz_target! harness, when a campaign finds nothing or reports crashes that will not reproduce, or when the target API needs structured rather than raw input.

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Performs graph-assisted triage of a single security finding, SARIF result, weAudit annotation, suspicious function, or report excerpt using Trailmark reachability, entrypoint paths, taint, privilege-boundary, blast-radius, caller/callee, and neighborhood evidence. Use when deciding whether one candidate issue is reachable, prioritizing a finding before PoC work, preparing evidence for exploit validation, or checking whether a static-analysis result is actionable.

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Measures and interprets what a fuzzing campaign actually reaches, using llvm-cov, lcov, or a fuzzer's own coverage output. Covers baselining a new campaign, reading coverage reports, and turning uncovered regions into harness, seed, or dictionary work. Use when a fuzzer plateaus, when judging whether a harness is effective, after changing a harness, or when asking why some code is never reached.

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Guides through Trail of Bits' 5-step secure development workflow. Runs Slither scans, checks special features (upgradeability/ERC conformance/token integration), generates visual security diagrams, helps document security properties for fuzzing/verification, and reviews manual security areas. Use when securing a smart contract end to end rather than hunting one bug, checking a project on every check-in or before deployment, triaging a Slither report, or asking where to start on smart contract security.

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Performs security-focused differential review of code changes. Adapts analysis depth to codebase size, uses git blame for context, calculates blast radius by counting callers, checks test coverage of modified code, and generates a markdown report. Use when reviewing a PR, commit, or diff for security vulnerabilities, checking whether a change re-introduces a previously fixed bug, asking what else a change could break, or finding which modified code has no test covering it.

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Analyzes smart contract codebases to identify state-changing entry points for security auditing. Detects externally callable functions that modify state, categorizes them by access level (public, admin, role-restricted, contract-only), and generates structured audit reports. Excludes view/pure/read-only functions. Use when auditing smart contracts (Solidity, Vyper, Solana/Rust, Move, TON, CosmWasm) or when asked to find entry points, audit flows, external functions, access control patterns, or privileged operations.

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。

Validates security patches with reproducible baseline-versus-patched evidence, including original exploits, root-cause variants, behavior preservation, regressions, and newly introduced security failures. Use after a patch exists and before accepting, merging, or reporting it as fixed; also use when an AI-generated patch, remediation commit, pull request, or proposed upstream fix needs adversarial post-patch validation across any language.

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Builds and runs code under AddressSanitizer to catch buffer overflows, use-after-free, and other memory errors during fuzzing or tests. Covers -fsanitize=address builds, ASAN_OPTIONS, reading the crash report, LeakSanitizer, and the overhead and platform trade-offs. Use when fuzzing C/C++ or Rust that has unsafe blocks or FFI, when debugging a memory corruption crash, or when reading an ASan stack trace.

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。