Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add ali-demirbas/claude-lifecycle/plugin install claude-lifecycleWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ali-demirbas/claude-lifecycle/lifecycle-audience)<a href="https://agentmods.dev/skills/ali-demirbas/claude-lifecycle/lifecycle-audience"><img src="https://agentmods.dev/badge/skills/ali-demirbas/claude-lifecycle/lifecycle-audience/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ali-demirbas/claude-lifecycle/lifecycle-audience"><img src="https://agentmods.dev/badge/skills/ali-demirbas/claude-lifecycle/lifecycle-audience.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00081 | $0.01576 |
| Opus 5 | $0.00041 | $0.00788 |
| Sonnet 5 | $0.00016 | $0.00315 |
| Haiku 4.5 | $0.00008 | $0.00158 |
Grade A, and why
lifecycle-audience scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lifecycle Audience — From Definition to Query
A journey doc's §3 says "users with ≥ 2 view_item in 30 days and no purchase" — and then a human translates that into a data-team ticket. The engine already knows the events, the params, and the windows; this skill writes the query itself. It is the bridge over the "designed but not activatable" gap: the portfolio's audiences become artifacts a data engineer can run today.
When NOT to use this
- No journeys or portfolio exist yet — there are no audience definitions to translate; run
lifecycle-journeysfirst. - Neither a BigQuery export nor a CDP substrate exists — this skill is explicitly blocked in that case (see Inputs below); don't attempt a query against a guessed schema instead of saying so.
- The need is the event→stage mapping itself, not a query — that's
lifecycle-map. This skill consumes an already-defined audience; it doesn't classify events.
Inputs (gate)
portfolio.json+ journey docs (the audience include/exclude definitions).- Data substrate — this decides everything:
- GA4 BigQuery export available → generate BigQuery SQL against the standard
events_*export schema (public, documented, stable). This is the primary mode. - Composable / warehouse-native CDP (reads audiences directly from the same BigQuery project via reverse-ETL) → this is not a third format, it's BigQuery mode: the audience is already one SQL model away from activation, and a reverse-ETL sync consumes a query result directly, so a separate trait translation would just be a redundant hop. Emit the same labeled SQL as the primary mode, and note which sync key it's meant to feed (e.g. "model query, sync key = user_pseudo_id").
- CDP (Segment-class, ingests its own copy of the data) → generate a tool-agnostic trait definition (JSON: conditions, windows, event references) plus prose mapping notes — never a specific vendor's API body without documentation in hand.
- Neither → this skill is blocked; say so and point at the tracking plan's identity item. No substrate, no query — pretending otherwise is the exact dishonesty the engine exists to prevent.
- GA4 BigQuery export available → generate BigQuery SQL against the standard
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 63 lines · 81 tokens per session scan A 3be183ad4ab8
lifecycle-audience is a skill published in the GitHub repository ali-demirbas/claude-lifecycle (2 stars, last pushed 22d ago), licensed MIT. It adds 81 tokens to every session and 1,576 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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