agentleFS
Infinite-Labs-AIInfinite-Labs-AI/infinite-skills/skills/analytics-tracking/SKILL.md

analytics-tracking

Skill43 starsChanged 4 months agoMITView on GitHub

Use when auditing, planning, or debugging marketing measurement, conversion tracking, GA4, GTM, UTMs, pixels, CRM attribution, funnel reporting, or dashboard trust.

61 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use operational-enterprise-ai, product-proof-saas and 9 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • boraoztuncSkill

    operational-enterprise-ai

    Create or redesign enterprise AI, automation, security, and operations product pages that explain system boundaries, approvals, auditability, exceptions, and rollback. Use for dark cinematic heroes, hairline grids, metric pauses, expandable solution rows, case-study evidence, security proof, and qualified demo or waitlist handoffs.

    Also in 3 projects that use this

  • boraoztuncSkill

    product-proof-saas

    Create or redesign SaaS and AI product landing pages where a real workflow, interface, or deterministic demo is the central proof. Use for pale atmospheric shells, product UI in the hero, prompt-to-output stories, audience tabs, compact feature modules, honest pricing comparisons, and FAQ handoffs.

    Also in 3 projects that use this

  • xfstudioSkill

    product-marketing-context

    When the user wants to create or update their product marketing context document. Also use when the user mentions 'product context,' 'marketing context,' 'set up context,' 'positioning,' or wants to avoid repeating foundational information across marketing tasks. Creates `.claude/product-marketing-context.md` that other marketing skills reference.

    Also in 22 projects that use this

  • andremoreira73Skill

    linkedin-posts

    Draft and refine LinkedIn posts. Use when the user wants to write, improve, or iterate on LinkedIn content.

    Also in 3 projects that use this

  • ai-vitaSkill

    aso-audit

    When the user wants to audit or optimize an App Store or Google Play listing. Also use when the user mentions 'ASO audit,' 'app store optimization,' 'optimize my app listing,' 'improve app visibility,' 'app store ranking,' 'audit my listing,' 'why aren't people downloading my app,' 'improve my app conversion,' 'keyword optimization for app,' or 'compare my app to competitors.' Use when the user shares an App Store or Google Play URL and wants to improve it.

    Also in 5 projects that use this

  • ukunal91appSkill

    revops

    When the user wants help with revenue operations, lead lifecycle management, or marketing-to-sales handoff processes. Also use when the user mentions 'RevOps,' 'revenue operations,' 'lead scoring,' 'lead routing,' 'MQL,' 'SQL,' 'pipeline stages,' 'deal desk,' 'CRM automation,' 'marketing-to-sales handoff,' 'data hygiene,' 'leads aren't getting to sales,' 'pipeline management,' 'lead qualification,' or 'when should marketing hand off to sales.' Use this for anything involving the systems and processes that connect marketing to revenue. For cold outreach emails, see cold-email. For email drip campaigns, see email-sequence. For pricing decisions, see pricing-strategy.

    Also in 17 projects that use this

  • +1 more from boraoztunc

The file

We split this skill into the rules it gives an agent

3 rules. 0 are common, 3 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 60 edited versions we read keep these.

  • 3

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Check whether marketing tracking is reliable enough to make decisions about budget, funnel performance, and growth.

    line 8
  2. Distinctive:

    Ask what decisions the data is supposed to support:

    line 12
  3. Distinctive:

    Test traffic pollutes reports.

    line 40

“Common” is measured: kept by at least 6 in 10 of the 60 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Analytics Tracking

Check whether marketing tracking is reliable enough to make decisions about budget, funnel performance, and growth.

Start With Decisions

Ask what decisions the data is supposed to support:

  • Which channel gets more budget.
  • Which campaigns convert.
  • Which pages or steps leak users.
  • Which leads become revenue.
  • Which lifecycle messages work.
  • Which experiments win.

Tracking is not trustworthy because tags fire; it is trustworthy when the decision chain is complete.

Inspect The Chain

Trace:

  • Source capture: UTMs, referrer, click IDs, campaign naming.
  • Event capture: page views, leads, signups, purchases, activation, qualified lead, revenue.
  • Identity: anonymous to known user, lead to account, account to deal.
  • Destination: analytics, ad platforms, CRM, warehouse, dashboards.
  • Definitions: what counts as a conversion, lead, MQL, opportunity, customer.
  • Consent and privacy boundaries.
  • Reconciliation: totals across systems and expected gaps.

Flag Trust Breaks

  • Events fire but are not tied to the business outcome.
  • Multiple systems define the same metric differently.
  • UTMs overwrite or disappear.
  • Test traffic pollutes reports.
  • Duplicate conversions feed paid bidding.
  • PII leaks into analytics or ad tools.
  • Dashboards hide uncertainty.
  • Offline revenue never connects back to campaigns.

Judge Data Confidence

For each decision, state:

  • Trust level: high, usable with caveats, directional only, not usable.
  • Known gaps.
  • Acceptable uncertainty.
  • Reconciliation owner.
  • Next check that would raise trust.

Output

Tracking trust read:
[one paragraph]

Decision chain:
Question -> Required signal -> Source -> Destination -> Owner

Decision trust:
| Decision | Trust level | Known gaps | Acceptable uncertainty | Reconciliation owner |

Trust breaks:
| Break | Evidence | Decision affected | Fix | Priority |

Measurement plan:
| Event | Trigger | Parameters | Destination | Success criteria |

Naming rules:
- [UTM or event rule]

Reconciliation checks:
1. [check]
2. [check]

Privacy risks:
- [risk]

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.