What makes Dark Social Attributor notable?

1K downloads. Trust evidence available

How should Dark Social Attributor 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

Dark Social Attributor

Use when the user asks to "figure out where our direct traffic really comes from", "measure dark social", "add a how-did-you-hear-about-us field", or "show s... Skill: Dark Social Attributor Owner: aaron-he-zhu Summary: Use when the user asks to "figure out where our direct traffic really comes from", "measure dark social", "add a how-did-you-hear-about-us field", or "show s... Tags: latest:19.0.0 Version history: v19.0.0 | 2026-07-24T14:30:48.140Z | auto Version 19.0.0 - Updated skill metadata to version 19.0.0 across documentation. - Added new file: distribution-manifest.j

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.0k

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. 1K 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: 1K 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:dark-social-attributor

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
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-dark-social-attributor/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

83,785 characters of source documentation, loaded on request.

Extracted files

4 files captured from the source.

SKILL.md

---
name: dark-social-attributor
slug: aaron-dark-social-attributor
displayName: "Dark Social Attributor · 暗社交归因"
summary: "暗社交归因/直接流量分解/自报来源字段/分享链路UTM"
description: 'Use when the user asks to "figure out where our direct traffic really comes from", "measure dark social", "add a how-did-you-hear-about-us field", or "show social drives signups without click data"; produces a share-link/UTM hygiene spec for owned share surfaces, a self-reported attribution field design that replaces an existing form field (free-text first, coded later), a GA4 direct-traffic decomposition read (deep-URL directs, mobile-app skew, private-push correlation) with every derived number hard-labeled Estimated/proxy, and a branded-search-lift proxy from GSC plus Wikipedia pageviews — the declared dark-social method behind ECHO O2. Not for paid-channel attribution reconciliation (platform-claimed vs analytics conversions) — use attribution-reconciler. 暗社交归因/直接流量分解/自报来源字段/分享链路UTM'
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 when direct traffic is unexplained, social ROI is questioned without click evidence, share buttons carry naked URLs, or a how-did-you-hear field is being designed: declares the dark-social estimation method (ECHO O2) and specs the instrumentation — share-link/UTM hygiene, a self-reported attribution field that replaces an existing form field, GA4 direct-traffic decomposition heuristics, and a branded-search-lift proxy via GSC + pageviews.py. Every derived number is Estimated/proxy by construction. Not for reconciling paid-platform conversion claims (attribution-reconciler) and not the metric dictionary or write-back loop (social-measurement-loop)."
argument-hint: "<GA4/GSC exports or site> [share-surface inventory] [existing form fields]"
metadata: {"author": "aaron-he-zhu", "version": "19.0.0", "discipline": "social", "phase": "observe", "geo-relevance": "low", "hermes": {"tags": ["marketing", "social", "observe"], "category": "social"}, "openclaw": {"emoji": "📣", "homepage": "https://github.com/aaron-he-zhu/aaron-marketing-skills"}}
---

# Dark Social Attributor

Makes the unmeasurable share loop estimable — honestly. Dark social is the traffic that arrives with no referrer because the link traveled through a DM, a group chat (微信群 / WhatsApp / Slack / Discord), a newsletter forward, or an address-bar copy. This skill declares the estimation method and specs the instrumentation; it never turns an estimate into a Measured number. It is the Observe-phase upstream of the ECHO **O** dark-social sub-items (see [echo-benchmark.md](../../../references/echo-benchmark.md)): *dark-social method declared and Estimated-labeled before any social-ROI claim* (ECHO O2) and the *dark-social instrumentation coverage* rows (ECHO O6–O7 — share-link/UTM hygiene live plus a self-reported attribution field running). Its labels are also wh

_meta.json

{
  "ownerId": "kn73qjxwmbna25qq8q051epqt980sys5",
  "slug": "dark-social-attributor",
  "version": "19.0.0",
  "publishedAt": 1784903448140
}

skill-card.md

## Description:

Helps marketers and growth teams estimate dark social contribution by specifying share-link UTM hygiene, self-reported attribution capture, GA4 direct-traffic decomposition, and branded-search-lift proxy analysis while labeling derived numbers as Estimated or proxy.

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, growth, and analytics practitioners use this skill to explain otherwise unattributed direct traffic, design share-link and self-reported attribution instrumentation, and present dark-social estimates without treating proxies as measured conversions.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: GA4/GSC exports, form inventories, and marketing-memory records may contain sensitive customer, traffic, or business data.

Mitigation: Use only approved, minimized exports in the agent environment and avoid supplying unnecessary personal or confidential fields.

Risk: Optional saved results or proposed channel facts could preserve inaccurate attribution claims.

Mitigation: Approve save or propose steps only after reviewing that generated facts are accurate, source-labeled, and appropriate to retain.

Risk: Dark-social decomposition and branded-search lift can be misread as measured attribution.

Mitigation: Keep heuristic outputs labeled Estimated or proxy and report self-reported counts alongside, not merged into, click-based attribution.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/aaron-he-zhu/skills/dark-social-attributor)
- [Project homepage](https://github.com/aaron-he-zhu/aaron-marketing-skills)

## Skill Output:

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

**Output Format:** [Markdown attribution pack with instrumentation specs, labeled estimate tables, and a handoff summary]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Derived attribution numbers are labeled Estimated or proxy; optional save and propose steps require user approval.]

## Skill Version(s):

19.0.0 (source: server release metadata and SKILL.md 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

{
  "capabilities": [
    "inline-delivery",
    "canonical-state-read"
  ],
  "capability_ceiling": "lite",
  "catalog_sha256": "6f0256cf52710f2916ecebaea0f3110c9313099ec4a69a11cac72ba9b2f3b940",
  "files": [
    {
      "bytes": 14115,
      "mode": "0644",
      "path": "SKILL.md",
      "sha256": "6a26de3bc65ca966084c49220ebc0d79f6d15f1f7c5aebddc5ec49d64329c65e"
    }
  ],
  "files_sha256": "86851c45614abe56e300dfc636c98d26e9b6aac414a7e8edd2ec18339d23b371",
  "hash_algorithm": "sha256",
  "kind": "standalone-skill",
  "manifest_excludes": [
    "distribution-manifest.json"
  ],
  "manifest_path": "distribution-manifest.json",
  "package_ceiling": {
    "max_bytes": 1000000,
    "max_files": 64
  },
  "profile": "lite",
  "profile_definition_sha256": "4598e1f7bba667ef928ea2a60a6252ad9348086e9eecab29437db442df2a568e",
  "schema_version": "1.1",
  "source": {
    "commit": "f552620c278afddcb25d09637a0cfcc1ce48faf4",
    "repository": "aaron-he-zhu/aaron-marketing-skills"
  }
}

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