Results for “ratings”

Extracts structured product data from Amazon search results, including prices, ratings, sales estimates, and shipping info, using the BrowserAct API.

Extract Amazon product reviews by ASIN using the BrowserAct API, returning structured data including ratings, text, reviewer info, and verified purchase status.

Evaluate and rank agent results by metric or LLM judge for an AgentHub session.

Evaluate and rank agent results by metric or LLM judge for an AgentHub session.

Evaluate and rank agent results by metric or LLM judge for an AgentHub session.

Grades individual test methods and produces a compact markdown table with a letter grade, score band, and one-line note for each test.

Evaluate and rank agent results by metric or LLM judge for an AgentHub session. Use when the user runs /hub:eval or asks to score, compare, or pick a winner among completed AgentHub agents.

Evaluates LLM performance using BLEU, ROUGE metrics and LLM-as-judge. Use for model testing.

Score LLM and agent outputs using LLM-as-judge techniques — direct scoring against rubrics or pairwise comparison between two outputs. Includes built-in bias mitigation for position bias, length bias, and self-enhancement bias. Load when the user asks to score an output, judge a response, evaluate against a rubric, compare two outputs, do direct scoring, run pairwise comparison, or says "rate this", "which response is better", "score this against the rubric", "judge this output", "LLM as judge this". Sub-skill of eval-output orchestrator.

Evaluates the correlation between automatic text generation scores and human quality ratings, including robustness to domain and quality drift, using metrics like Kendall's Tau and Pearson correlation.

Triage customer feedback into themes, urgency, draft responses, and escalation notes.

Evaluate LLM outputs systematically — benchmarks, automated metrics, human preference, and regression tracking

Evaluate and rank agent results by metric or LLM judge for an AgentHub session. Use when the user runs /hub:eval or asks to score, compare, or pick a winner among completed AgentHub agents.

Evaluates execution transcripts and output files against a list of expectations, assigning pass/fail verdicts with cited evidence and critiquing the assertions themselves.

Extract structured best-selling product data from Amazon, including titles, prices, ratings, reviews, sales volume, and promotions, using the BrowserAct API.

Capture confidence ratings before and after a learning attempt to identify overconfidence and underconfidence patterns. Use when a student wants to understand how well they actually know something versus how well they think they know it.