What makes Performance Analyzer notable?

1.2K downloads. Trust evidence available

How should Performance Analyzer be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Performance Analyzer

Use when the user asks to "analyze influencer campaign performance", "compare influencers", or "find what content worked"; produces metric scorecards vs targ... Skill: Performance Analyzer Owner: aaron-he-zhu Summary: Use when the user asks to "analyze influencer campaign performance", "compare influencers", or "find what content worked"; produces metric scorecards vs targ... Tags: latest:19.0.0 Version history: v19.0.0 | 2026-07-24T14:58:51.029Z | auto - Summary: Introduces compatibility and metadata updates, minor documentation revision, and reorganizes skill manifest file

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.2k

Updated

Oct 11, 2026

Version

19.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.2K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1.2K downloadsadoption · observed Oct 11, 2026
Latest release: 19.0.0release · observed Jul 24, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17e1tg8pjra8dn1dvtq21sahx83hrxj:performance-analyzer

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-aaron-he-zhu-performance-analyzer/snapshot"

Documentation

CLAWHUB

144,842 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: performance-analyzer
slug: performance-analyzer
displayName: "Performance Analyzer · 效果分析"
summary: "活动效果分析:达成 vs 目标、平台与创作者维度拆解、优化建议"
description: 'Use when the user asks to "analyze influencer campaign performance", "compare influencers", or "find what content worked"; produces metric scorecards vs target and benchmark, platform/influencer/content rankings, engagement-quality and sentiment reads, conversion-attribution breakdowns, and ranked learnings. Not for dollar-level return math — use roi-calculator. 达人营销效果分析/投放复盘'
version: "19.0.0"
license: Apache-2.0
compatibility: "Claude Code and compatible agent-skill hosts"
homepage: "https://github.com/aaron-he-zhu/aaron-marketing-skills"
when_to_use: "Use mid-flight or post-campaign when a user wants to evaluate influencer results, compare creators against each other, find top-performing content or formats, judge engagement quality and comment sentiment, connect influencer activity to conversions, or build performance benchmarks for future planning."
argument-hint: "<campaign name> [platform or influencer handles]"
metadata: {"author": "aaron-he-zhu", "version": "19.0.0", "discipline": "influencer", "phase": "report", "geo-relevance": "low", "hermes": {"tags": ["marketing", "influencer", "report"], "category": "influencer"}, "openclaw": {"emoji": "📣", "homepage": "https://github.com/aaron-he-zhu/aaron-marketing-skills"}}
---

# Performance Analyzer

Analyze influencer campaign performance past surface metrics — score results vs target/benchmark, rank platforms/creators/content, read engagement quality and sentiment, attribute conversions, and write ranked learnings.

> **Cross-discipline (paid ads):** this is also the cross-channel **paid-ads** scorecard/anomaly lens — account-wide metric rollups vs target/benchmark that feed [ad-test-designer](../../../ad/orchestrate/ad-test-designer/SKILL.md) (what to test) and [paid-measurement-loop](../../../ad/scale/paid-measurement-loop/SKILL.md) (what to read back). Save paid runs under `memory/ad/performance-analyzer/`.

## Quick Start

```
Analyze performance of [campaign name] influencer campaign
```

Compare creators within one campaign:

```
Compare performance of these influencers from [campaign]: @handle1, @handle2, @handle3
```

## Skill Contract

- **Reads**: campaign name and date range; native platform analytics (reach, views, engagement); influencer-supplied reports or screenshots; website/GA traffic and conversion data; sales and promo-code redemption data; targets and benchmarks if the user has them; per-creator performance baselines from `memory/creators/<handle-slug>.md` ([creator-registry](../../../protocol/creator-registry/SKILL.md) roster records) when present.
- **Writes**: a performance analysis to `memory/influencer/performance-analyzer/YYYY-MM-DD-<campaign>.md` covering core-metric scorecards, platform/influencer/content rankings, engagement-quality and sentiment reads, conversion attribution, and ranked learnings.
- **Promo

_meta.json

{
  "ownerId": "kn73qjxwmbna25qq8q051epqt980sys5",
  "slug": "performance-analyzer",
  "version": "19.0.0",
  "publishedAt": 1784905131029
}

references/analysis-templates.md

# Performance Analyzer — Analysis Templates

Fill-in templates for each step of the influencer performance analysis. Each maps to a numbered step in [../SKILL.md](../SKILL.md) Instructions.

Repo-root links from this file use `../../../`.

- [skill-contract.md](../../../../references/skill-contract.md)
- [state-model.md](../../../../references/state-model.md)
- [CONNECTORS.md](../../../../CONNECTORS.md)

---

## Step 1 — Gather Performance Data

```markdown
### Performance Data Collection

**Campaign**: [name]
**Period**: [start] - [end]
**Influencers**: [count]
**Platforms**: [platforms]

### Data Sources

| Source | Metrics Available | Collection Method |
|--------|-------------------|-------------------|
| Native analytics | Reach, views, engagement | Platform export |
| Influencer reports | Screenshots/exports | From creators |
| Website analytics | Traffic, conversions | GA/tracking |
| Sales data | Revenue, orders | E-commerce platform |
| Promo code data | Redemptions | Sales system |
```

---

## Step 2 — Analyze Core Metrics

```markdown
## Campaign Performance Overview

### Summary Metrics

| Metric | Result | Target | vs. Target | vs. Benchmark |
|--------|--------|--------|------------|---------------|
| Total Reach | [X] | [X] | [+/-X%] | [+/-X%] |
| Total Impressions | [X] | [X] | [+/-X%] | [+/-X%] |
| Total Engagements | [X] | [X] | [+/-X%] | [+/-X%] |
| Engagement Rate | [X%] | [X%] | [+/-X%] | [+/-X%] |
| Total Video Views | [X] | [X] | [+/-X%] | [+/-X%] |
| Link Clicks | [X] | [X] | [+/-X%] | [+/-X%] |
| Promo Code Uses | [X] | [X] | [+/-X%] | N/A |
| Conversions | [X] | [X] | [+/-X%] | [+/-X%] |
| Revenue | $[X] | $[X] | [+/-X%] | N/A |

### Performance Score: [X/10]

**Assessment**: [Excellent/Good/Average/Below Average/Poor]

### Key Highlights

✅ **What Exceeded Expectations**:
- [Highlight 1]
- [Highlight 2]

⚠️ **What Underperformed**:
- [Issue 1]
- [Issue 2]
```

---

## Step 3 — Analyze by Platform

```markdown
## Platform Performance

### Platform Comparison

| Platform | Reach | Engagements | ER | Clicks | Conversions | CPA |
|----------|-------|-------------|-------|--------|-------------|-----|
| Instagram | [X] | [X] | [%] | [X] | [X] | $[X] |
| TikTok | [X] | [X] | [%] | [X] | [X] | $[X] |
| YouTube | [X] | [X] | [%] | [X] | [X] | $[X] |
| **Total** | **[X]** | **[X]** | **[%]** | **[X]** | **[X]** | **$[X]** |

### Platform Insights

**Best Performing Platform**: [Platform]
- Why: [analysis]
- Key content: [what worked]

**Underperforming Platform**: [Platform]
- Why: [analysis]
- Improvement opportunity: [suggestion]

### Platform-Specific Metrics

#### Instagram

| Metric | Feed Posts | Reels | Stories |
|--------|------------|-------|---------|
| Reach | [X] | [X] | [X] |
| Engagements | [X] | [X] | [X] |
| ER | [%] | [%] | [%] |
| Saves | [X] | [X] | N/A |
| Shares | [X] | [X] | [X] |

#### TikTok

| Metric | Result | Benchmark |
|--------|--------|-----------|
| Views | [X] | |
| Likes | [X] | |
| Comments |

skill-card.md

## Description:

Analyzes influencer campaign performance by comparing results against targets and benchmarks, ranking platforms, creators, and content, reading engagement quality and sentiment, and summarizing conversion attribution and learnings.

This skill is ready for commercial/non-commercial use.

## Publisher:

[aaron-he-zhu](https://clawhub.ai/user/aaron-he-zhu)

### License/Terms of Use:

MIT-0

## Use Case:

Marketing teams, analysts, and campaign operators use this skill during or after influencer campaigns to evaluate results, compare creators and platforms, identify effective content patterns, and prepare recommendations for future planning.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Campaign analytics and business metrics may be summarized into memory notes.

Mitigation: Review the campaign data before use and avoid providing sensitive analytics unless persistent workspace notes are permitted.

Risk: ROI, revenue, CPA, and budget recommendations may be misleading if treated as final financial decisions.

Mitigation: Treat outputs as draft business analysis and validate dollar-level conclusions with a dedicated ROI calculator or finance workflow before acting.

## Reference(s):

- [Performance Analyzer on ClawHub](https://clawhub.ai/aaron-he-zhu/skills/performance-analyzer)
- [Source homepage](https://github.com/aaron-he-zhu/aaron-marketing-skills)
- [Analysis templates](references/analysis-templates.md)

## Skill Output:

**Output Type(s):** [text, markdown, guidance]

**Output Format:** [Markdown analysis report with metric tables, rankings, attribution summaries, and recommendations]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [May include memory write paths for campaign analysis and durable findings when supported by the host environment.]

## Skill Version(s):

19.0.0 (source: server release metadata and frontmatter)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

distribution-manifest.json

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    "repository": "aaron-he-zhu/aaron-marketing-skills"
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