Results for “integrity”

Systematic peer review toolkit. Evaluate methodology, statistics, design, reproducibility, ethics, figure integrity, reporting standards, for manuscript and grant review across disciplines.

Plan structure validation, phase completeness checks, reference integrity verification, and artifact existence confirmation. Provides the structured verification layer ensuring GSD artifacts are well-formed and complete.

Consolidates 89 high-integrity SOPs for the Full department of the Galyarder Framework, covering adapter creation, execution protocols, and multi-agent hygiene.

Deploys canary files, honeypot shares, and decoy systems to detect ransomware activity at the earliest possible stage.

Use when designing, creating, or modifying database schemas, data structures, or data models. This skill provides standardized data modeling practices ensuring consistency, integrity, and maintainability across all data stores.

Validate ransomware recovery plans by testing backup restore operations, measuring RTO/RPO targets, verifying data integrity, and documenting recovery gaps in an isolated lab environment.

Humanization Quality Verifier - Ensures transformation integrity and quality Validates that humanization preserves meaning, citations, and academic standards Use when: after G6 transformation, before final export, for quality assurance Triggers: verify humanization, check transformation, validate changes

Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.

Sign build artifacts (binaries, packages, containers) with GPG, Sigstore, and platform-specific tools to ensure integrity and authenticity throughout the software supply chain.

Enforces a strict input boundary protocol (detect, classify, filter, verify) to ensure untrusted data never reaches business logic raw.

Use when coding interviews, focus groups, or open-ended responses with AI assistance, when a codebook needs building or auditing, when intercoder reliability must be computed, or when qualitative quotes need integrity checks before publication.

Enforces a strict input boundary protocol (detect, classify, filter, verify) to ensure untrusted data never reaches business logic raw.

Verify TPM measured boot integrity and remote attestation using tpm2-tools, including PCR reading, event log replay, quote generation and verification, and golden baseline comparison.

Enforces principal-engineer-level Python code quality: formatting, type safety, idiomatic patterns, class and function design, and anti-pattern prevention.

Resolves legacy references to the cyber-intel capability by routing to the current runtime agent or skill.

Integrate an exact reviewed PR under repository policy, choose truthful history semantics, and verify cleanup

Generate comprehensive checklists to validate requirements quality (Unit Tests for Specs). Critically examine the spec for clarity, completeness, and consistency before implementation starts.

Test quality inspection framework for reviewing test coverage, identifying gaps, and ensuring comprehensive validation

Use to finalize Cell Press display items — column-width sizing, minimum fonts, RGB, show-the-data with defined error bars/n/replicate type, scale bars, colorblind-safe palettes, multi-panel discipline, stand-alone legends, and image integrity.

Reviews cohesion, naming, responsibility, complexity, and maintainability. Do not use as a default companion to every code change.

Analyzes code to separate essential complexity from incidental noise, then produces a ranked refactoring plan with a structural sketch and TRACE assessment, without making edits.

Post-implementation quality gate that reviews changes, fixes small issues (scout rule), creates tasks for medium issues, and generates analysis for large issues.

Detect and analyze Linux persistence mechanisms including crontab entries, systemd service units, LD_PRELOAD hijacking, bashrc modifications, and authorized_keys backdoors using auditd and file integrity monitoring.

Detect sophisticated cyber-physical attacks that modify PLC logic while spoofing sensor readings, covering PLC integrity monitoring, process anomaly detection, and multi-stage attack chain detection.

Analyzes UEFI bootkit persistence mechanisms including firmware implants, ESP modifications, Secure Boot bypass techniques, and UEFI variable manipulation. Covers detection of known bootkit families, forensic inspection, and integrity verification.

Monitors a deployed URL for regressions after releases by checking HTTP status, console errors, network failures, performance metrics, content integrity, API health, static assets, and SSE streams.

Use to finalize PNAS display items — column-width sizing (~9 / 11.4 / 18 cm), minimum font sizes, show-the-data with n and defined error bars, scale bars, colorblind-safe palettes, legend structure, image integrity, and color-mode notes. Enforces figure rigor before submission.

Detect cyber attacks targeting OT historian servers (OSIsoft PI, Ignition, Wonderware) that sit at the IT/OT boundary and serve as pivot points for lateral movement between enterprise and control networks, including data manipulation, unauthorized queries, and exploitation of historian-specific vulnerabilities.

Use this skill when the user asks about Electronic Logging Device (ELD) compliance under 49 CFR 395.20-38 — ELD mandate, AOBRD vs ELD transition, ELD exemptions, ELD malfunctions, 8-day paper backup, ELD certification on FMCSA registered list, data file integrity, edits + annotations, driver portability, and how to handle ELD failures at roadside. Cite 49 CFR 395.20-395.38.

Analyse experiment results, run validity checks (SRM, exposure parity, data integrity, novelty/primacy), interpret causally, make a ship/iterate/kill decision against the pre-declared rule, and append to cumulative learnings. Forces honest readouts — strips significance claims from underpowered or peek-violating tests; never lets directional results masquerade as causal wins. Load when results exist, or when the user says "read out this experiment", "analyse the test", "did the test win", "interpret the results", "what did we learn", "ship or kill", or when the experimentation orchestrator routes here.