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.

Jump to install

Repository: seaworld008/Commonly-used-high-value-skills
Last source activity: July 27, 2026 at 07:13
Detected SKILL.md language: English
Stars: 64
Forks: 10

Install options

The review-first prompt is selected by default. You can switch to a direct command or download a local copy.

Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.

A direct command skips the review prompt. Inspect the source before running it.

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

The command stays on one line. Scroll horizontally to inspect it before copying.

Prefer a local copy? Download the files currently available to SkillsMP.

Review the source files

Read SKILL.md and any companion files shown by SkillsMP before deciding whether to install.

File Explorer

16 files

SKILL.md

Source instructions · Read-only preview

Back to search Back to installation

agent-hub

Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.

2026-07-27

agent-workflow-designer

Design production-grade multi-agent orchestration systems. Covers five core patterns (sequential pipeline, parallel fan-out/fan-in, hierarchical delegation, event-driven, consensus), platform-specific implementations, handoff protocols, state management, error recovery, context window budgeting, and cost optimization.

2026-07-27

app-store-optimization

App Store Optimization toolkit for researching keywords, optimizing metadata, and tracking mobile app performance on Apple App Store and Google Play Store.

2026-07-27

View all from this repository

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