Results for “credibility-scoring”

Multi-dimensional lead qualification scoring. Evaluates leads against BANT criteria, firmographic fit, behavioral signals, and intent indicators. Outputs qualified/disqualified verdict with detailed reasoning.

Rank an existing set of context chunks by relevance, diversity, freshness, and utility. Use when retrieval has already produced candidates that must be scored or reranked; use context-retrieval when the source corpus still needs to be searched.

Give a decisive, project-grounded point of view in the subject's own shape: a graded verdict on an external-adoption question, a holistic take on a document, or a position on a user-supplied approach set. Use for a solo POV, a mid-session second opinion, a named-peer cross-check, any request to consult other models or reconcile their opinions, an `oracle` panel, or a correction-cost-gated proactive cross-check offer. Not for findings review (use ce-doc-review), neutral explainers, or generating options (use ce-ideate or ce-brainstorm).

Generate high-CTR titles and opening hooks for a piece of content and score them against clarity and curiosity.

Decide whether an article deserves publication through hard checks and skeptical comparison.

Score raw leads as HOT/WARM/COOL based on config-driven weights from agency.config.json

Deterministically score, route, rank, and select at most one fully vetted Pleasur.ai Stage 01 blog-keyword candidate using product-fit-dominant business value, traffic opportunity, brand fit, DR-relative winnability, and a free-seeker penalty. Use only after BID and AIO evaluation are complete.

Turn "is this account a real fit?" into a stacked verdict. A composite 0-100 fit score built from every signal you have, a breakdown by source, override flags for when a scoring tool is wrong, a channel classification, and a written rationale. Built for reps, marketers, and RevOps, customizable to your CRM and whatever scoring you use. Trigger on "is {account} a real fit?", "score this list against our profile", "find lookalikes to our best customers", "why is the score wrong on {account}?", "validate this prospect list", "what's our coverage in {segment}?", or any account or list qualification.

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.

Prioritize product work with explicit criteria, scoring, tradeoffs, and decision rationale.

Run an AI-assisted PR code review using multi-layer lenses with confidence scoring.

Use when making a forward-looking claim with a checkable outcome (reply within 24h, error rate will drop, this skill will see more use) — record to state/predictions.jsonl with a review horizon so reflection can grade you later. Closes the in-the-moment double-loop.

Evaluate scientific claims and evidence quality by assessing experimental design, identifying biases and confounders, and applying evidence grading frameworks like GRADE and Cochrane Risk of Bias.

Score Analyzer

Reviews secrets, config defaults, environment variables, logs, credentials, CI secrets, and unsafe settings.

Readability Review