Results for “reputation-scoring”

Handle social media crises with strategic response and reputation management. Use when the user needs to manage a PR crisis, handle negative feedback storms, or protect brand reputation.

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.

Scores generated image captions against reference captions and source images using the Polos metric, which is trained to align with human judgments and probes hallucination robustness and open-vocabulary evaluation.

Load this skill when the user says "resolve comments", "resolve-comments", or "/resolve-comments". Checks open PR review comments, addresses valid ones in a single batched commit, and resolves all threads with appropriate replies.

Build and run the owner's self-branding system in a structured way across LinkedIn, Substack, dev.to, X, profile rewrites, recurring series, and comment strategy. Use when the owner asks for positioning, a branding plan, profile updates, content drafts, a publishing sequence, comment ideas, or help maintaining a consistent public voice. Optimize for operator credibility: specific experience, defensible numbers, concrete artifacts, and category ownership.

Scores deal margin and risk, routes discount approval to the right human approver, and redlines terms against commercial policy for per-deal review.

Score and prioritize leads based on firmographic fit and behavioral engagement signals, producing ranked tiers for sales team focus. Use when the user requests lead scoring or provides relevant inputs for this workflow.

Defines ideal customer profile filters, scores inbound and outbound leads, and builds a lightweight qualification rubric to sharpen pipeline focus for founder-led sales.

Build quiz and assessment funnels that generate qualified leads through interactive scorecards, covering concept hooks, question design, dynamic results, and automated follow-up sequences.

Score and iteratively improve marketing content with an expert panel until 90+. Use for content quality gates, expert panel reviews, editorial scoring, or when another skill needs a content QA loop.

Score, evaluate, and iteratively improve any content or strategy using an auto-assembled panel of domain experts. Handles copy, sequences, landing pages, strategy docs, titles, charts, recruiting evaluations, or anything else that needs a quality gate. Recursively iterates until all scores hit 90+ (max 3 rounds). Use when asked to: "expert panel this", "score this", "rate these variants", "quality check this", "panel review", "which version is better", "expert score", "evaluate this copy/strategy/page", or when another skill needs a quality gate on its output. Also triggers on: "score this landing page", "expert panel these email variants", "rate this headline", "panel these charts".

Ranks mutual connections by bridge value for warm introductions, using a weighted graph model with decay and engagement bonuses.

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.

Benchmark-relative quality gate. Scores the draft against the research dossier's beat spec (depth, consensus coverage, evidence) plus AI-tell and voice signals, runs an adversarial read armed with the SERP benchmark, and emits the verdict that gates the pipeline.

`task-agent`/`review-agent`: use when CPU, memory, I/O, database, network, rendering, or cost needs measured bottleneck evidence; skip without a profiling need.

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.

Daily customer health scoring across the active book of business. Pulls usage signals from PostHog and subscription state from Stripe, scores each active customer per the brand's customer-success playbook, updates the Notion customer DB, and Slack-alerts on at-risk transitions. Runs daily on cron schedule.

Create irresistible offers using the Value Equation, bonus stacking, risk-reversing guarantees, and ethical scarcity.

Scores competitors across nine weighted dimensions with explicit 1–5 rubrics and a tension plot, producing comparable profile cards for competitive analysis.

Earn press coverage and media mentions through PR outreach, newsjacking, and journalist pitching.

Apply six decades of persuasion science—Cialdini's principles of reciprocity, commitment, social proof, authority, liking, scarcity, and unity—to product design, copy, and sales, ethically.

Monitor brand mentions, analyze sentiment, and identify trends across social platforms. Use when the user needs to analyze social data, track brand health, or understand audience sentiment.

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.

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

Reviews resumes for formatting, design, content, and job-specific fit; scores job postings for fit and interview odds; develops rubrics; extracts content from conversations; and audits the resume generation pipeline.

Analyzes state merit review for non-covered securities offerings, applying NASAA Statements of Policy to cheap stock, promoter equity investment, voting rights, and promoter compensation. Produces examiner-ready comment responses with cap table analysis and negotiation strategy. Use when filing Reg A, Rule 504, intrastate, or direct public offerings in merit review states, responding to Blue Sky examiner comments, structuring offerings to avoid conditioning, or analyzing NASAA SOPs. Also trigger on cheap stock analysis, promoter equity tests, unequal voting rights review, state examiner correspondence, or phrases like "merit review issues" or "the state examiner sent comments."

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

Run a trademark clearance search before adopting a new brand name, mark, or logo and risk-rate it clear, caution, or high-risk with a proceed/modify/abandon recommendation.

Decide which pull-request review comments are actionable. Use when addressing review feedback, monitoring a PR, or filtering already-handled comments.

Improve existing content and close the learning loop without cannibalizing new-content work.

Use to compare BPO sites, vendors or partner teams fairly, adjusting for the work mix each is given before concluding anything about performance. Trigger for "compare our BPO sites", "which vendor is performing best", "site A scores lower than site B", outsourcer QBR packs, partner MI reporting, or setting contractual quality targets with a vendor.

Classify reply sentiment and determine next action

Naming Craft

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

Parse RFP/RFI/RFQ documents, score fit per requirement with verifiable proof points, generate win-themes, estimate winrate, and produce a bid/no-bid recommendation using the Shipley method.