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Event performance classifier

Classifies events into performance tiers (Elite, Strong, Steady, Underperforming, At-Risk) using weighted KPI scoring against defined benchmarks, with per-event rationale, tier-specific action recommendations, and a…

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Purpose

Convert raw event metrics into a consistent, defensible performance tier for every event — using a weighted KPI rubric with explicit benchmarks — so analysts can triage a portfolio at a glance, flag at-risk events early, and apply the same scoring logic across batch exports and real-time evaluation.

When to Use

Invoke this skill when the user asks to:

  • Classify or tier a set of events by performance
  • Score events against KPI benchmarks or targets
  • Flag underperforming or at-risk events in a portfolio
  • Build a reusable rubric for evaluating event health
  • Segment events for prioritization, resourcing, or intervention
  • Rank events and explain why each landed where it did

Inputs Required

Before starting, collect:

  • Event dataset: One or more events with their metrics (table, list, or single record — batch or one-off)
  • KPIs to evaluate: Which metrics define performance (e.g., attendance rate, GMV, conversion, NPS, refund rate)
  • Benchmarks or targets: Per-KPI target values, tier thresholds, or historical baselines to score against
  • KPI weights: Relative importance of each KPI (if omitted, apply equal weighting and note it)
  • Event type/segment: Category, region, or size class — so events are scored against comparable peers
  • Direction of each KPI: Whether higher is better (GMV) or lower is better (refund rate, no-show rate)
  • Tier count and names: If the user has a preferred scheme; otherwise default to 5 tiers below

If benchmarks are missing, derive relative thresholds from the dataset's own distribution (percentiles) and clearly state that classification is relative, not absolute.

Instructions

Step 1: Normalize and Validate Inputs

Prepare the data before scoring:

  • Confirm every event has values for the required KPIs; flag events with missing fields and score them on available KPIs only (mark as "partial data")
  • Normalize each KPI to a 0–100 subscore against its benchmark:
  • Higher-is-better: subscore = min(100, actual / target × 100)
  • Lower-is-better: subscore = min(100, target / actual × 100)
  • Percentile-based (no benchmark): rank the value within the dataset and map to 0–100
  • Cap subscores at 100 so a single overperforming KPI can't mask weakness elsewhere

Step 2: Apply the Weighted Scoring Rubric

Compute a composite score per event:

  • Composite = Σ (KPI subscore × KPI weight) / Σ weights
  • Round to one decimal; retain the KPI-level subscores for the rationale
  • Ensure weights sum to 1.0 (or normalize them); state the weighting used

Step 3: Assign Performance Tier

Map composite scores to tiers. Default 5-tier scheme (override if the user supplied one):

  • Elite (90–100): Top performers exceeding targets across most KPIs — replicate and protect
  • Strong (75–89.9): Consistently above benchmark with minor gaps — optimize the margins
  • Steady (60–74.9): Meeting core expectations, mixed KPI profile — monitor and nudge
  • Underperforming (40–59.9): Missing benchmarks on key weighted KPIs — intervene
  • At-Risk (0–39.9): Failing across weighted KPIs — escalate or evaluate for retirement

Apply an override rule: if any single KPI weighted above 0.30 scores below 30, cap the tier at Underperforming regardless of composite — a critical failure shouldn't be averaged away.

Step 4: Write Per-Event Rationale

For each event, produce:

  • Composite score and assigned tier
  • The 1–2 KPIs pulling the score up and the 1–2 pulling it down (with subscores)
  • Any override or partial-data flag triggered

Step 5: Attach Tier-Specific Actions

Recommend the standard next move for each event based on its tier:

  • Elite: Document what's working; use as a benchmark template for peers
  • Strong: Identify the single lagging KPI and set a targeted improvement goal
  • Steady: Watch the weakest KPI; re-evaluate next cycle
  • Underperforming: Diagnose the failing weighted KPI and assign an intervention owner
  • At-Risk: Escalate immediately — decide within the cycle whether to fix or sunset

Step 6: Summarize the Portfolio

Provide a distribution rollup: count and percentage of events per tier, the highest and lowest composite scores, and any systemic pattern (e.g., "all At-Risk events share low conversion").

Output Format

  • Structure: Classification table (one row per event) followed by per-event rationale, a tier distribution summary, and the reusable rubric definition
  • Length: Scales with dataset — ~40–80 words of rationale per event, plus a 100–150 word portfolio summary
  • Format: Table for the classification results; short structured bullets for rationale and actions; a compact rubric block at the end
  • Sections: Classification Table → Per-Event Rationale & Actions → Portfolio Distribution → Reusable Scoring Rubric
  • Tone: Analyst-precise and decisive — every tier assignment is justified by numbers, no hedging

Quality Checklist

Before delivering output, verify:

  • [ ] Every event in the input received a composite score and a tier (count matches input)
  • [ ] Weights used are stated and sum to 1.0 (or were normalized)
  • [ ] Each rationale names the specific KPIs driving the score up and down — not "performed well overall"
  • [ ] The single-KPI override rule was checked and applied where triggered
  • [ ] Partial-data or missing-KPI events are flagged, not silently scored as zero
  • [ ] The portfolio summary reports exact per-tier counts and the score range

Notes

  • The rubric is the reusable asset — deliver it in a form the analyst can re-apply to next month's export or wire into a real-time evaluation without re-deriving thresholds
  • Always score events against comparable peers; a small regional event and a flagship shouldn't share the same absolute GMV benchmark unless the user insists
  • When benchmarks are absent, percentile-based scoring is fine for triage but tells you rank, not health — label it clearly so tiers aren't misread as absolute performance
  • Recommend re-running the classifier on a fixed cadence so tier movement (an event slipping from Steady to Underperforming) becomes an early warning signal

How to use this skill

This file is a Claude Code skill in Anthropic's standard format — markdown with YAML frontmatter at the top.

Installing in Claude Code:

  • ~/.claude/skills/<folder-name>/SKILL.md — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)
  • <your-project>/.claude/skills/<folder-name>/SKILL.md — auto-loads only when working in that project (recommended for project-specific skills)

Restart Claude Code or open a new session to register.

Using in Claude.ai, ChatGPT, Gemini, or other AI tools:

  1. Drag this file into a new chat, or copy and paste its contents
  2. Send your task or request as your next message — the AI will apply the skill where it fits

For Claude Code skill details: https://docs.anthropic.com/en/docs/claude-code/skills