Results for “trustscore”

Detects fake reviews, Sybil rings, rating manipulation, and reputation laundering in ERC-8004 agent ratings across 20+ blockchains using the TrustLayer API.

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.

Evidence-based health research using tiered trusted sources with GRADE-inspired evidence ratings. Integrates Apple Health data for personalized context. Use when user asks health, nutrition, exercise, sleep, or wellness questions.

Checks on-chain agent trustworthiness, discovers agents for tasks, profiles agents, looks up wallet/identity records, contacts or dispatches agents, and reads or writes hosted metadata via the 8K4 Protocol (ERC-8004).

Score the experience a new customer actually has, not just whether they activated. Grades the first 90 days on the moments that make someone feel taken care of, finds where the welcome goes cold, and hands back a scored audit with the specific fixes. Built for any post-sale team, customizable to your product and lifecycle. Trigger on "audit my onboarding", "score our onboarding experience", "why do new customers churn early", "grade our first 90 days", "where does onboarding go cold", "is our welcome any good", or any onboarding-quality question.

Calculates a per-ticker panic index (0-100) from six weighted signals including VIX, IV Rank, RSI-14, volume anomaly, put/call ratio, and consecutive down days, triggering Bull Put Spread entry signals at scores ≥60.

Compute the F1Score metric using torchmetrics when predictions and ground-truth labels are available.

Triages and enriches indicators of compromise (IPs, domains, file hashes, URLs, email artifacts) from phishing emails, security alerts, or threat feeds, assigning confidence scores and dispositions using VirusTotal, AbuseIPDB, MalwareBazaar, and MISP.

TLS/SSL configuration audit. Tests protocol versions (SSLv2/3, TLS 1.0/1.1/1.2/1.3), cipher strength and ordering, certificate chain validation (intermediates, CT logs, OCSP stapling, pinning), known vulnerabilities (POODLE, BEAST, CRIME, Heartbleed, ROBOT, DROWN, Ticketbleed, GOLDENDOODLE), session management (ticket reuse, resumption, fixation), renegotiation attacks, TLS 1.3-specific issues (0-RTT replay, PSK modes, downgrade), HSTS deep analysis (preload, subdomains, bypass), and certificate revocation (CRL, OCSP responder, stapled response freshness). Compliance-mapped to PCI DSS 4.0 (full Section 4), NIST SP 800-52r2, and FedRAMP. Uses testssl.sh, sslscan, sslyze, openssl, nmap NSE scripts, and nuclei SSL templates. Multi-port scanning across 20+ TLS-bearing services.

Assigns behavioral trust grades (A–F) to MCP servers by running probes for prompt injection, permission overreach, data leaks, and adversarial-input handling, and publishes reproducible onchain attestations.

Review an exact PR head in isolation using trusted-base instructions and execution evidence

Use this when the system needs clear analysis of trusted versus untrusted execution contexts, external inputs, client/server boundaries, provider callbacks, or admin-level code paths. Trigger on requests about trust boundaries, attack surfaces, or where verification must happen.

Assesses codebase maturity using Trail of Bits' 9-category framework, producing a professional scorecard with evidence-based ratings and actionable recommendations.

Computes the R2Score metric using torchmetrics, handling single and multi-output predictions with options for adjusted and variance-weighted scores.

Track investment theses across their lifecycle — from screening idea to closed position with postmortem. Register theses from screener outputs, manage state transitions, attach position sizing, review due dates, and generate postmortem reports with P&L and MAE/MFE analysis.

Scan codebases for technical debt, score severity, track trends, and generate prioritized remediation plans.

Evaluates Base ERC-20 tokens by contract address, returning a 0-100 score across five criteria, launch protocol classification, security flags, and a narrative interpretation.

Turn a pile of accounts into a stack-ranked priority list with a reason on every row. A layered score (gates first, then an evidence-weighted base rank over the signals you actually have, then bounded boosts for product usage and buyer intent) that stays fair across channels and never scores a blank field as a zero. Built for B2B GTM teams, customizable to your signals and your ICP. Trigger on "score these accounts", "rank by fit", "composite ICP score", "stack-rank my list", "who should I work first", "prioritize these leads", or any multi-signal account qualification.

Assess SSL/TLS server configurations using the sslyze Python library to evaluate cipher suites, certificate chains, protocol versions, HSTS headers, and known vulnerabilities like Heartbleed and ROBOT.

Guides organizations through implementing zero trust architecture in cloud environments following NIST SP 800-207 and Google BeyondCorp principles, covering identity-centric access controls, micro-segmentation, continuous verification, device trust assessment, and deploying Identity-Aware Proxy in AWS, Azure, and GCP.

Vendor privacy risk tiering methodology for processor management. Covers scoring factors including data volume, sensitivity, transfer locations, certifications, breach history, and control maturity with weighted risk calculation and tier assignment.

Security audit orchestrator for agent skills — scans for prompt injection, data exfiltration, credential theft, supply chain risks, and instruction hierarchy violations before any skill is installed, created, improved, or read from a GitHub repo. Load when creating skills from external sources, when improve-skills reads from GitHub repos, when research-skill fetches community SKILL.md files, when a user installs a third-party skill, or when the user asks to audit skill security, scan for injection, check if a skill is safe, scan all skills, or run a security sweep. Orchestrates all secure-* skills in sequence. Content is SAFE only if ALL secure-* skills return SAFE. 36% of community skills contain flaws (Snyk ToxicSkills 2026). This skill is the first line of defense.

Use to design or audit the support contribution to a customer health score, so the score predicts something instead of averaging weakly-related signals into a colour. Trigger for "build a customer health score", "add support signal to health scoring", "our health scores don't predict churn", red/amber/green account scoring, or a health score nobody trusts.

Prioritize assumptions using an Impact × Risk matrix and suggest targeted experiments for each.

Provides a comprehensive security checklist and code patterns for authentication, input validation, secrets management, SQL injection prevention, XSS, CSRF, rate limiting, and sensitive data exposure.

Lightweight ContentShake AI self-check the /draft stage can call before saving. Returns just SEO + Quality scores (no full optimization) so the writer knows whether the draft is in winning territory before /quality-check runs. Fails soft when SEMRUSH_API_KEY is unset.

Scores real estate leads based on engagement, budget, timeline, and property preference matching

Provides a single 0-100 cycle score blending VIX, SPY IV Rank, Put/Call ratio, and valuation percentile to determine offense vs. defense posture, based on Howard Marks' market cycle framework.

Score a UI file's design quality 0-100 against StyleSeed's design language with per-category breakdown, worst offenders, and prioritized fix list.

Audit authentication, token handling, PII protection, and anti-cheat in QuizVerse.

Use to measure customer effort from behavioural signals instead of CES surveys, and to audit whether a composite effort score is honest. Trigger for "customer effort score", "behavioural CES", effort without survey, repeat contacts and channel switches, transfers and reopens, or "our CES doesn't match operational data".

Meta's 86M prompt injection and jailbreak detector. Filters malicious prompts and third-party data for LLM apps. 99%+ TPR, <1% FPR. Fast (<2ms GPU). Multilingual (8 languages). Deploy with HuggingFace or batch processing for RAG security.

Validates, tests, and scores the quality of skills within a claude-skills ecosystem using Python scripts that check structure, syntax, runtime, and multi-dimensional quality metrics.

Security Craft

Benchmarks a text-to-text transformer across GLUE, SuperGLUE, CNN/Daily Mail, SQuAD, and WMT, reporting GLUE average, BLEU, ROUGE-2-F, and Exact Match scores.