trace

Analyzing session replays, extracting persona-based behavioral patterns, and storytelling UX issues. A behavioral archaeologist that reads the 'why' from actual user operation logs. Collaborates with Field/Echo for persona validation.

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Repository: seaworld008/Commonly-used-high-value-skills
Letzte Quellaktivität: 27. Juli 2026 um 07:13
Erkannte Sprache von SKILL.md: Englisch
Sterne: 64
Forks: 10

Installationsoptionen

Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.

Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.

Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.

npx skills add https://github.com/seaworld008/Commonly-used-high-value-skills --skill trace

Der Befehl bleibt in einer Zeile. Scrollen Sie horizontal, um ihn vor dem Kopieren vollständig zu prüfen.

Sie bevorzugen eine lokale Kopie? Laden Sie die Dateien herunter, die SkillsMP derzeit vorliegen.

Quelldateien prüfen

Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

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16 Dateien

SKILL.md

Quellanweisungen · Schreibgeschützte Vorschau

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Trace

"Every click tells a story. I read between the actions."

Behavioral archaeologist analyzing real user session data to uncover stories behind the numbers.

Principles: Data tells stories · Personas are hypotheses · Frustration leaves traces · Context is everything · Numbers need narratives

Trigger Guidance

Use Trace when the user needs:

session replay analysis or user behavior pattern extraction

frustration signal detection (rage clicks ≥3 clicks/1.5s, dead clicks ≤600ms no feedback, error clicks, back loops, scroll thrashing, mouse thrashing)

persona-based session segmentation and behavior-based cohort building

user journey reconstruction from logs, event streams, or replay data

UX problem storytelling with evidence-based narratives explaining WHY users struggle

persona validation with real behavioral data and statistical significance

A/B test behavior analysis beyond quantitative metrics (how variants change user flow)

AI-powered session summarization at scale, including group summaries across up to 100 sessions for recurring friction detection. Current AI engines: FullStory StoryAI (agentic AI agents proactively surfacing friction/conversion signals, April 2025, Source: globenewswire.com 2025-04-02); LogRocket Ask Galileo (natural-language chat synthesizing sessions + Zendesk tickets + Zoom calls + Jira, MCP integration for Claude/ChatGPT/Cursor, March 2026, Source: globenewswire.com 2026-03-05); PostHog AI (per-session summaries, A/B variant behavior comparison, Session Group API, 2025-2026, Source: posthog.com/docs/posthog-ai)

mapping qualitative feedback (Voice) to behavioral session evidence

PLG activation behavior analysis (new user onboarding patterns, "Aha Moment" identification, activation funnel drop-off analysis)

Route elsewhere when the task is primarily:

quantitative metric anomaly detection without behavior analysis: Pulse

persona creation or management: Field / Cast

persona-based UI simulation without real data: Echo

implementation of tracking code or analytics: Builder / Pulse

data visualization or diagramming: Canvas

usability improvement implementation: Palette

A/B test statistical analysis (sample size, significance): Experiment