Lifesight channel deep dive

Skill lifesight/lifesight/skills/lifesight-channel-deep-dive

Official agent skills for the Lifesight MCP — causal marketing measurement inside Claude and Claude Code. Claude Code plugin + Claude.ai bundle.

Install

npx -y skills add lifesight/lifesight --skill lifesight-channel-deep-dive

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What its author says it does

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Use when the user wants to go deep on ONE channel — saturation, marginal ROI, response curve, headroom, or a what-if: "should I increase TikTok", "where's the headroom on Meta", "is Linear TV saturated", "what happens if I cut Google by 20%", "how efficient is this channel really". The analyst's home spoke — tightest data guardrails. Routed to from the `lifesight` router after the workspace is calibrated.

SKILL.md

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Lifesight Channel Deep-Dive

Everything that matters about one channel: where it sits on its response curve, what the next dollar returns, and what a change would do. The whole game is marginal, not average — a channel with a great average ROAS can be a terrible place to add the next dollar.

Prerequisites (router handles): workspace calibrated, profile loaded. Operate under lifesight-core; present under lifesight-rendering. Load both.

Analyst guardrail. This spoke serves the heaviest-data persona, who is most exposed to the query_ad_data flood. Apply lifesight-core Rule 3 strictly: prefer ask_mia (it summarizes); if you must pull rows, scope hard (one channel, bounded window, aggregated). Never widen a deep-dive into a full-table dump.

Flow

Confirm the one channel (and the metric/question — saturation? marginal ROI? a specific what-if?). One channel at a time; if they name several, do the most important first and offer the rest.
One heavy call. Pull the channel's curve / marginal economics / what-if via ask_mia. Walk its gates if it asks (lifesight-core Rule 4).
Interpret against the curve, not in isolation.

Judgment checks (mandatory)

Marginal, not average. Lead with the return on the next dollar and where the channel sits vs its saturation knee — not the blended ROAS.
Platform vs causal. State whether the efficiency figure is platform-reported or causal iROAS. A "scale it" call on a sub-1.0 platform number needs the causal read.
Saturation ≠ headroom for a weak channel. "Room in the curve" only matters if the channel is efficient there. A channel below the iroas_floor is not a growth opportunity just because it isn't saturated.
What-ifs ride the curve. A "+20% spend" answer must reflect diminishing returns, not linear extrapolation. Respect profile guardrails (floor, caps, locked channels).

Output shape

Where it sits — current spend, saturation %, and the marginal return on the next dollar, in plain language.
The read — efficient with headroom / efficient but saturating / inefficient — and what that means.
The move — scale, hold, or cut, with the specific what-if if asked ("+20% → ~X incremental revenue at Y marginal ROAS").
Validation option — if it's a big bet, suggest a geo-lift test to confirm causally.

Clean channel name, causal language, signed/formatted numbers.

Next steps to offer

"Optimize across all channels" (→ budget-optimization) · "Compare to [another channel]" · "Run a geo-lift test to validate" · "Check what moved here recently" (→ anomaly-watch).

Red flags — STOP

Leading with average ROAS instead of marginal return
Calling a sub-floor channel "headroom / scale it"
A what-if that extrapolates linearly instead of along the response curve
Widening the deep-dive into a multi-channel raw data pull → scope it (Rule 3)
Quoting a platform number as causal without naming the basis