# Lifesight Calibration

> Use the FIRST time you operate a Lifesight workspace, when no lifesight-workspace-profile exists yet, or when the user asks to "set up", "calibrate", "onboard", "configure for my account", or "recalibrate" Lifesight. Also use when the active workspace changed, when channel names or models look unfamiliar, or when the user wants to set guardrails (iROAS floor, max per-channel change, locked channels). Produces the per-workspace profile that every other Lifesight skill reads.

- Skill: `lifesight/lifesight-calibration` (Agent Skill)
- Install (CLI): `npx skillmds@latest add lifesight/lifesight-calibration`
- Raw SKILL.md: https://api.skillmd.com/api/skills/lifesight/lifesight-calibration/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: lifesight (https://skillmd.com/u/lifesight)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/lifesight/lifesight-calibration

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# Lifesight Calibration — Learn the Workspace Once

The Lifesight skills are **universal** — the same files ship to every customer.
Nothing about a specific account is baked into them. This skill is how a universal
skill set adapts to one account: it inspects the live workspace **once** and writes
a small `lifesight-workspace-profile` that the router, core, rendering, and every
persona spoke then read. Skills stay universal; the profile is the only per-customer
artifact.

**Follow `lifesight-core` operating rules throughout** (one heavy call per turn,
clean errors, no leaks).

## When to run

- No `lifesight-workspace-profile` exists yet for the active workspace.
- The user explicitly asks to set up / calibrate / recalibrate.
- The active workspace just changed (the profile is per-workspace).
- Channels or models in a response don't match the profile (drift — refresh it).

If a current profile already exists and matches the active workspace, **don't
re-run** — just use it.

## What to inspect (cheap calls only)

All of these are light tools — no flood risk. Run them in this order:

1. **Confirm the workspace.** `list_workspaces` → note the active workspace name/id.
2. **Discover models.** `list_models` → capture every model (name, KPI) and which
   is the default/recommended. If zero models, stop and tell the user to set a
   default scenario in the platform UI — calibration can't complete without one.
3. **Learn the channels + spend scale in one call.** `get_current_budget(default
   model)` returns the per-channel current spend. This gives you (a) the real raw
   channel columns for this account and (b) the rough total spend scale. This is
   the cheapest way to learn the channel set — do NOT run a heavy optimization just
   to discover channels.

That's it for inspection. Don't pull historical row data or run optimizations
during calibration — you only need the structure, not the analysis.

## Build the channel map

For every raw channel column returned, derive a clean display name using the
universal rule in `lifesight-rendering` (drop the metric affix, Title-Case, expand
known abbreviations, preserve funnel qualifiers). Record both the raw token and the
display name so other skills never have to guess.

## Write the profile

Write `lifesight-workspace-profile.md` (in the user's project for Claude Code;
see Persistence below for other surfaces). Use this format:

```markdown
# Lifesight Workspace Profile
workspace: <name> (<id>)
calibrated: <date>
default_model: <model name> — <KPI>
models:
  - <model name> — <KPI> [default]
  - <model name> — <KPI>

## Channel map (raw → display)
<raw_column> = <Display Name>
<raw_column> = <Display Name>
...

## Scale
approx_total_spend: <rough magnitude, e.g. ~$2.1M / quarter>   # for formatting + sanity checks
currency: <USD/…>

## Guardrails  (customer-defined — confirm with the user; leave blank if unset)
iroas_floor: <e.g. 1.0>            # don't recommend scaling channels below this
max_channel_change: <e.g. ±50%>    # cap per-channel reallocation
locked_channels: <none / list>     # never recommend changing these
default_objective: <e.g. fixed-budget reallocation, not uncapped max-revenue>

## Persona default
primary_user: <CMO / Growth / CFO / Analyst — optional, shapes default framing>
```

Keep it short — it's a config, not a report. Confirm the guardrails with the user
rather than inventing them; an empty guardrails block is fine and means "ask before
applying constraints."

## Persistence by surface

- **Claude Code / filesystem present** → write the file as above; it persists.
- **Claude.ai desktop (MCP only, no filesystem)** → you can't write a file. Instead,
  present the profile to the user and offer to save it to their project memory /
  custom instructions, or simply re-derive it at the start of each session (the
  inspection above is cheap). Never block on persistence — runtime re-derivation is
  always an acceptable fallback.

## Hand back

Once written, summarize in one or two lines and route on:

> "Calibrated to <workspace>: <N> channels, default model <model> (<KPI>),
> ~<scale> total spend. Guardrails are unset — want to set an iROAS floor or
> per-channel caps before we optimize?"

Then continue to whatever the user actually came to do (via the router).

## What calibration never does

- It never runs a heavy optimization just to learn the workspace — `get_current_budget`
  is enough.
- It never invents guardrails or numbers — it reads real data and asks for the rest.
- It never writes account-specific values into the universal skill files — only into
  the profile artifact.