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-connect)<a href="https://agentmods.dev/skills/ali-demirbas/claude-lifecycle/lifecycle-connect"><img src="https://agentmods.dev/badge/skills/ali-demirbas/claude-lifecycle/lifecycle-connect.svg" alt="Measured on agentmods" 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 | $0.03499 |
| Opus 5 | $0.00041 | $0.01750 |
| Sonnet 5 | $0.00016 | $0.00700 |
| Haiku 4.5 | $0.00008 | $0.00350 |
Grade A, and why
lifecycle-connect 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 7d 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lifecycle Connect — Data Source Assessment & DQS
Establish what data exists and how much journey sophistication it can support. Output: a Data Assessment Report ending in a DQS. Full scoring table: ${CLAUDE_PLUGIN_ROOT}/docs/data-quality-score.md.
When NOT to use this
- A fresh DQS + event inventory already exists this session and nothing changed — re-running repeats a full data pull for the same answer; go straight to
lifecycle-maporlifecycle-journeys. - The question is what an event means or how the funnel looks, not how much the data is worth — that's
lifecycle-map, which consumes this skill's output rather than producing it. - The user has performance or holdout results from a journey that already launched — that's
lifecycle-results. This skill scores input data quality; it has nothing to do with campaign outcomes.
Step 1 — Identify the source (tier)
| Tier | Detection | Action |
|---|---|---|
| T1 | Live analytics connection: GA4 via MCP, BigQuery/Google Cloud, or another connected analytics tool (Mixpanel, Amplitude…) | List properties/datasets, confirm which one; then pull events, conversions, funnel |
| T2 | User provides a row-level CSV/export (GA4, BigQuery, Mixpanel, Amplitude, CRM) with one row per event/user | Read the file; extract event names, counts, date range |
| T2-aggregate | User provides pre-aggregated reports instead of row-level data: monthly/period totals per channel, funnel step, event, page, or device — from any tool's dashboard export (GA4 UI report, Mixpanel/Amplitude summary export, a hand-built spreadsheet), not just GA4 | Read every sheet/table provided; treat each as one input source (see below) |
| T3 | No data at all | Record industry; DQS is scored 0 for data components — journeys will be playbook-based |
T2-aggregate is a distinct shape, not a lesser T2. It can have excellent event-type and funnel-step visibility (often clearer than a raw per-user export, since the aggregation is already done) but zero per-user rows, ever — no user_id, no individual identity, no cross-device stitching. Score it honestly: User attributes / segments caps near 0 regardless of how rich the rest of the data is (there is no row-level identity to score), while Event diversity and Funnel completeness can score normally off the aggregate tables. State this cap explicitly in the DQS breakdown so the user understands why value-based branching or individual targeting isn't available even though the numbers look strong.
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.
- 7d ago First seen · 115 lines · 81 tokens per session scan A 23b4a52cb9f5
lifecycle-connect is a skill published in the GitHub repository ali-demirbas/claude-lifecycle (2 stars, last pushed 20d ago), licensed MIT. It adds 81 tokens to every session and 3,499 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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