analytics-funnel
Skill12 starsChanged 23 days agoMITView on GitHub
Use when product or gameplay telemetry must be grouped into a versioned funnel with explicit steps, cohort, denominator and observation window so drop-off claims are auditable rather than inferred from screenshots or raw event counts.
In this repository194 other files it gives its agents.
- llms.txtllms.txt
- hd-sprite-sequenceSkill
- pixel-animationSkill
- skin-weightsSkill
- spine-skeletalSkill
- sprite-atlasSkill
- sprite-catalogSkill
- sprite-skinSkill
- art-bibleSkill
- asset-pack-intakeSkill
- character-rigSkill
- materialsSkill
- model-pipelineSkill
- open-asset-fixtureSkill
- vfx-generateSkill
- a11y-controlsSkill
- ability-gateSkill
- action-feelSkill
- ad-breakSkill
- adventure-toolSkill
- aggro-tableSkill
- aim-assistSkill
- ammo-reloadSkill
- animation-blendSkill
- animation-graphSkill
- asset-runtimeSkill
- attack-tellSkill
- audio-busesSkill
- audio-feelSkill
- avatar-createSkill
- balance-designSkill
- boss-designSkill
- browser-inputSkill
- camera-anti-clipSkill
- camera-modestySkill
- camera-shotsSkill
- cert-handoffSkill
- chemistry-verbsSkill
- climb-vaultSkill
- cloud-saveSkill
- collision-layersSkill
- combo-designSkill
- cover-shooterSkill
- cover-spaceSkill
- crafting-loopSkill
- currency-dualSkill
- cutscene-handoffSkill
- day-nightSkill
- death-carrySkill
- debug-slateSkill
- deck-buildSkill
- dialogue-beatSkill
- dialogue-flagsSkill
- difficulty-designSkill
- dodge-iframeSkill
- durability-economySkill
- encounter-designSkill
- enemy-aiSkill
- enemy-fireSkill
- enemy-kit-balanceSkill
- The first 60 shown.
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 8
Ask the analysis intent: first-session, progression, encounter, commerce, retention, or existing.
- Very specific: line 28
Do not require events to occur on one universal logic tick.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Analytics funnel
Ask the analysis intent: first-session, progression, encounter, commerce, retention, or existing.
Contract
Publish:
- stable
funnel_idand revision - ordered step IDs and the
telemetry-eventsschema/version each step consumes - cohort / eligibility predicate
- denominator definition
- observation window and timezone/session boundary policy
- deduplication / late-event policy
- segment dimensions allowed for comparison
- excluded or unknown population
Ownership
analytics-funnel owns funnel definition and aggregation semantics. telemetry-events owns event schema/emission, gameplay systems own state truth, and gameplay-validation owns qualitative/playability claims. Funnel correlation is not causal proof.
Rules
- Do not require events to occur on one universal logic tick; consume the authoritative event timestamp/session identity defined by
telemetry-events. - Event names, step order and denominator are versioned. A schema or eligibility change starts a new comparable revision or is explicitly migrated.
- Duplicate/retried events are deduplicated by stable event/session identity according to policy.
- A drop-off rate always names numerator, denominator, cohort, build/ruleset and time window.
- Privacy/data-minimization rules are project/platform constraints; do not add person-identifying inputs merely to make a funnel easier to join.
- Heat maps and funnels suggest where to investigate; they do not prove why a player failed, quit or converted.
Acceptance
Given the same event set, funnel revision, cohort and window, aggregation produces the same step counts and denominator independent of ingestion order. A reviewer can reproduce the reported drop-off and identify excluded/late/duplicate events without treating the result as causal gameplay proof.
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
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.

