setup-runneth-classic

name	setup-runneth-classic
description	First-run setup for the runneth-classic pack. Does a deep silent pre-discovery across Motion brand-context, workspace-goal, saved reports, recent ad performance, custom conversions, and the public web, then runs a short conversational onboarding that confirms what was discovered and enriches the gaps. Hands off to brand-audit to deepen the brand foundation. Triggered automatically by runneth-classic's post-install step. Can be re-invoked any time with "set up runneth", "reconfigure runneth", or "rerun runneth setup".
user-invocable	true

Purpose

Configure runneth-classic for the workspace. The pack's behavioral rules read the files this setup writes — surface preference tunes inline-vs-artifact defaults, winner-definition tunes "what's working" answers, watched brands feed competitor research, customer voice intent feeds brand-audit and downstream creative work.

This setup is built on one design principle: Runneth should walk into the conversation already knowing as much as possible. A senior strategist showing up to onboard a new account does the homework first — they read the brand site, look at recent ads, check what reports the team has, talk to people who've already worked with them. The conversation that follows is confirmation and enrichment, not blank-slate discovery.

Phase 0 does that homework silently. Phase 1 confirms and enriches. The customer feels like Runneth gets them from the first sentence.

Execution

Workspace identification first

Before anything: resolve the active workspace. Use the default workspace from Motion context. Save per-workspace files under /agent/brain/runneth-classic/workspaces/<workspace-slug>/.

---

Phase 0 — Pre-discovery (silent, before any user-facing message)

No user-facing message in this phase. Run the gathering, build the internal model, then transition into Phase 1 with the synthesis.

What to gather

Run these in parallel where possible. Be efficient — don't let pre-discovery take more than 90 seconds of wall clock.

From Motion (always):

1. motion brand-context --data-query "brand foundations, fundamentals, product information, competitors, customer voice analysis" — the workspace's existing brand model. This is the single most important call.
2. motion workspace-goal — preferred conversion metric and attribution windows for Facebook and TikTok entries.
3. motion reports — list of saved reports with their tableKpis, sortBy, filters.
4. motion meta custom-conversion-metrics — custom conversion registry (Meta only; skip if no Meta connection).
5. motion spend-threshold — workspace significance threshold.
6. motion meta insights --date-range last_30d --sort topSpend --limit 50 --include-metrics --include-summaries — what creatives are actually running on Meta right now, including format mix and messaging themes from summaries.
7. motion tiktok insights --date-range last_30d --sort-by spend --sort-direction desc --grain ads --limit 30 --include-metrics — what's running on TikTok (skip if no TikTok connection).
8. Historical creative library for graveyard inference — motion meta insights --date-range last_365d --sort topSpend --limit 500 --include-metrics --include-summaries --group-by creative --include-glossary — the full year of creative work. This is what we use to infer what was tested and abandoned vs. what's still running.

From the brain (full inventory, always run):

The brain is not assumed empty. Real orgs install Runneth Classic on top of existing context — prior brand-audit runs, dossiers built by other use cases, files the team saved manually, content from a previous Runneth setup. Phase 0 explicitly audits what's there and incorporates it into the model. The pack has an opinion on what to use, preserve, update, and archive.

9. Run a brain inventory. ls -la /agent/brain/ at the top level. For each subtree relevant to creative strategy, record what exists, when it was last modified, what it appears to cover, and whether the modification date suggests it may be stale.

Specifically check these locations in order:

• /agent/brain/runneth-classic/workspaces/<slug>/ — prior runneth-classic state for this workspace (re-invocation case)
• /agent/brain/runneth-classic/integrations-intent.md — org-level integrations the team has already named
• /agent/brain/brand-audit/<workspace>/ — prior brand-audit Foundation bundle (strategy.md, brand-context.md, review-audit.md, keywords.md, competitor-analysis.md, product-catalog.md)
• /agent/brain/brand-kit/<workspace>/ — visual identity content if brand-kit ran
• /agent/brain/paid-strategy-audit/<channel>/<workspace>/ — strategy briefs per channel
• /agent/brain/competitor-intel/ — prior weekly competitor scans
• /agent/brain/customers/<workspace-or-slug>/ — CSM dossier content if it exists
• /agent/brain/customer-voice/ or similar VOC stores
• Any other top-level brain subtree modified in the last 90 days

For each find, classify it as:

• Fresh (modified within the last 60 days) — default to using as is
• Aging (60–180 days) — use but flag for confirmation during setup
• Stale (>180 days) — likely needs refresh; surface to the user explicitly

Save the inventory to /agent/brain/runneth-classic/workspaces/<slug>/brain-inventory.md for the orchestrator's Step 0 re-anchor to read on every future turn.

10. Read the high-value files in full. If brand-audit's strategy.md exists, read it cover to cover — the persona × angle × stage matrix is the most important piece of inherited context. Same for any prior winner-definition.md, standing-decisions.md, graveyard-inference.md, customer-voice-intent.md, watched-brands/*.md. These are the files Phase 1 will lead with.

From the public web (when useful):

12. If brandUrl is in brand-context, WebFetch the URL → confirm category, positioning, product type. Pull whatever About / How-it-works copy is visible.
13. WebSearch "<brand name> reviews" — check whether public review surfaces exist (Trustpilot, G2, Capterra, Amazon, Reddit). Don't read them in depth; just identify their existence so customer-voice questions can be framed correctly.
14. Social listening / cultural context — WebSearch for category conversations on Reddit, TikTok, X. Use the brand's brandCategory and productCategory plus the implied persona's pain to seed 2–3 broad queries. Example queries for a SaaS performance-marketing tool: site:reddit.com performance marketers complaining attribution, tiktok performance marketing inside jokes 2026, r/marketing biggest frustrations this year. Capture: emerging language patterns, current frustrations, cultural moments the brand could credibly speak to, what's getting roasted in the category. Save the synthesis to /agent/brain/runneth-classic/workspaces/<slug>/cultural-context.md for downstream creative-strategy reads.

Build the internal model

Synthesize what you have into a working picture. Inherited brain content has priority over fresh Motion data for subjective claims (persona definitions, voice, brand positioning, standing decisions, graveyard markers); fresh Motion data has priority for factual claims (current active ads, current saved reports, current workspace goal). Cover at minimum:

• What they sell. Physical product / SaaS / service / marketplace / agency / other. From productType and productCategory in brand-context, confirmed by the website if fetched.
• Brand category. From brandCategory in brand-context.
• Primary persona implied. From brand-context's customer voice analysis or competitor positioning. May need confirmation but pick the strongest candidate.
• Customer voice sources likely. SaaS / B2B → internal sources (Gong calls, support tickets, sales notes). Physical / consumer → public reviews (Trustpilot, Amazon, Reddit). Cross-check with the WebSearch result.
• Competitors already tracked. Brand IDs from brand-context's competitorBrands array.
• Workspace goal and attribution. From motion workspace-goal.
• Inferred winner-definition. From workspace-goal plus the patterns in saved reports.
• What creative is currently running. Format mix, messaging themes, persona tells from the top 30-day creatives.
• What's been tested and abandoned (the inferred graveyard). From the 365-day creative library: identify patterns that were tested with meaningful spend then dropped to zero, OR formats/tactics that appeared once or twice and never returned. Group by visual format, asset type, hook tactic, and messaging angle using glossary tags. Distinguish two cases: (a) patterns that peaked then died ("tried at scale, killed") and (b) patterns that were tested once and never iterated on ("tried once, abandoned"). Save the synthesis to /agent/brain/runneth-classic/workspaces/<slug>/graveyard-inference.md so Turn 5b can present specific findings rather than asking blank-slate.
• What's been iterated on heavily (the validated patterns). From the same 365-day pull: patterns that appear repeatedly across variants, that maintain or grow spend over time, or that show up in multiple campaigns. These are the brand's bench — what the team has signed off on as good.
• Whether brand-audit has already run. Yes / no based on /agent/brain/brand-audit/<workspace>/ existence, plus when it last ran (fresh / aging / stale).
• Whether prior runneth-classic state exists. If workspaces/<slug>/ has prior files, this is a re-invocation. Treat existing files as the starting point and only update what the user changes in Phase 1.
• What other use cases have contributed brain content. Other use cases (brand-kit, paid-strategy-audit, competitor-intel, etc.) may have written useful content. Note their existence; the orchestrator reads them via corpus-search on relevant future turns.
• What looks stale. Anything aging or stale gets surfaced for confirmation rather than silently used.

The graveyard inference is not a confident claim. Patterns get paused for many reasons — seasonal rotation, creator contract ending, fatigue, a strategist's preference shift. Phase 1 surfaces specific findings as questions, not assertions, and the customer corrects what we got wrong.

---

Brain handling policy

The pack has an explicit opinion on how existing brain content gets used, where it lives, what gets updated, and what gets archived. This policy applies to every Phase 0 inventory and every Phase 1 write.

Use

• Inherited content is the default starting point. If a prior brand-audit strategy file exists, the workspace persona is the one in that file — not whatever Phase 0 infers from brand-context. The user has to actively change it for the inferred version to win.
• Subjective inheritance. Persona names, voice descriptions, standing decisions, graveyard markers, watched-brand reasons — if a prior file says it, trust it.
• Factual freshness. Current active ads, current workspace goal, current attribution windows — always pull the live Motion data; the prior file may be stale.
• Read but don't claim ownership of other use cases' content. Brain content written by brand-kit, paid-strategy-audit, competitor-intel, and others is theirs. The orchestrator reads it for context but the runneth-classic setup does not write to those paths.

Conflict resolution

When Phase 0 inference contradicts inherited brain content, resolve in this priority order:

1. What the user says in this conversation (Phase 1 explicit confirmation) wins everything.
2. Inherited per-workspace state under runneth-classic/workspaces/<slug>/ wins over fresh inference for subjective content.
3. Inherited brand-audit/<workspace>/ content wins over fresh inference for subjective content.
4. Fresh Motion data wins for factual claims (current campaigns, current goal, current creative).
5. WebSearch / WebFetch results are the lowest-priority signal — supporting context only.

If the user's Phase 1 answer contradicts inherited content, treat that as a deliberate update. Archive the old version (see below) and write the new version. Do not silently merge.

Where state lives

• Current canonical state for this workspace — /agent/brain/runneth-classic/workspaces/<slug>/<filename>.md
• Org-level state shared across workspaces — /agent/brain/runneth-classic/<filename>.md (currently only integrations-intent.md)
• Brain inventory snapshot — /agent/brain/runneth-classic/workspaces/<slug>/brain-inventory.md (written by Phase 0, read on every chain run)
• Archived prior versions — /agent/brain/runneth-classic/workspaces/<slug>/_archive/<ISO-date>-<filename>.md
• Pre-discovery corrections — /agent/brain/runneth-classic/workspaces/<slug>/pre-discovery-corrections.md

Updating vs. archiving

When Phase 1 captures a change to durable state (winner definition, persona context, graveyard entry, watched brand, etc.):

1. If the file does not exist yet: write it. Done.
2. If the file exists and the change is additive (new entry to a list, new theme to track, new tool added): merge — append the new content, preserve everything that was there.
3. If the file exists and the change overrides existing content (winner definition changes, persona gets renamed, graveyard pattern marked as not-actually-dead): archive the old version first.

Archival procedure:

mv /agent/brain/runneth-classic/workspaces/<slug>/<filename>.md \
   /agent/brain/runneth-classic/workspaces/<slug>/_archive/<ISO-date>-<filename>.md

Then write the new version to the original path. Add a header to the new version recording what changed and why, with a pointer to the archived original.

Never destructive overwrites for content the user has been working with. Even if the change is small, preserve the prior version.

Stale content handling

• Fresh (<60 days): use as is, no surface.
• Aging (60–180 days): use but mention in the closing handoff: "Your brand-audit foundation is from — if anything feels off in the next few weeks, say refresh brand-audit."
• Stale (>180 days): surface during Phase 1. "Brand-audit hasn't run since . Want me to refresh it as part of this setup, or trust what's there?"

Provenance markers

Every per-workspace file written by setup includes a header recording:

• Captured: ISO date
• Source: Phase 0 inference / Turn N confirmation / Inherited from prior file / User upload
• Confidence: high (user-confirmed) / medium (inferred and reasonable) / low (best guess, flag for confirmation)
• Supersedes: path to archived prior version, if any

After Phase 0

You now have an internal model. Phase 1 starts with the synthesis as the opener.

---

Phase 1 — Confirmation and enrichment

Critical rule for every turn in Phase 1:

Ask ONE question per turn. Wait for the user's response. Save what they said. Then ask the next question.

Do not list multiple questions in a single message. Do not show the user a roadmap. Do not say "Step 2," "Step 3," or "Step N" anywhere in chat. The customer should never see numbered steps. Internal turn numbers in this skill exist for your reference only.

Anti-pattern (the failing pattern from real test installs — do not do this):

Setting up Runneth. I'll ask a few questions then build your brand foundation. Step 2 — Surface preference: Slack, web, or both? Step 3 — Winner definition: Your workspace goal is set to ROAS... Step 4 — Connection check: Meta is connected...

Step 9 — Brand foundation. Last thing before I hand off to brand-audit: what's the brand website URL?

Correct pattern: one question, one turn, save, move on. Use "Step N" or numbered headings never. Use ordinary conversational flow.

Tone for the whole onboarding

• Warm, direct, like a senior strategist who already did the homework.
• Show what you already know. Don't make the customer repeat things Motion can already see.
• When you do ask, ask sharply and use your context. Examples: "Since you're SaaS, my guess is your customer voice is internal — Gong calls, support tickets — right?" not "Where do reviews live?"
• Acknowledge corrections immediately and update the internal model before continuing.

Turn 1 — Pre-discovery synthesis + first confirm

Open with what you found. This is the message that should make the customer feel like Runneth gets them. The synthesis branches based on what's already in the brain.

Case A: This is a fresh install (no prior runneth-classic state, no prior brand-audit, or stale).

"Setting up your Runneth. Before asking anything, I dug through what Motion already has on :

• What you sell: <SaaS workflow tool for performance marketers | physical wellness products | etc.>, built for .
• How you measure: Workspace goal is (). Your saved reports lean on .
• What's running: Your top creatives over the last 30 days lean on <format + messaging theme synthesized from the top 50>.
• Who you're already watching: .

Sound right at a glance, or did I miss something obvious?"

Case B: brand-audit foundation already exists and is fresh (<60 days).

"Setting up your Runneth. I see brand-audit ran on — I'll lean on that as your foundation. Quick read on what I'm carrying forward:

• Who you're building for: facing , at .
• How you measure: Workspace goal is , with . Your saved reports lean on .
• What's running right now: .
• Watched brands: <existing inspo + competitor names>.

Sound right at a glance, or has anything shifted since brand-audit last ran?"

Case C: brand-audit exists but is aging (60–180 days).

"Setting up your Runneth. Brand-audit was last refreshed on , so I'll work with what's there but flag anything that looks like it may have shifted. Quick read:

[same four bullets as Case B]

Sound right at a glance, or has anything material changed since ?"

Case D: brand-audit exists but is stale (>180 days).

"Setting up your Runneth. Brand-audit hasn't run since — that's old enough that I'd suggest refreshing it as part of this setup. For now, here's the read I have:

[same four bullets as Case B, plus note on what's likely shifted given Motion data]

Want me to refresh brand-audit at the end of this configuration, or trust what's there?"

Case E: This is a re-invocation (prior runneth-classic state exists).

"Re-running your Runneth setup. I'm carrying forward what was captured on :

• Persona:
• Winner definition:
• Watched brands:
• Customer voice:

Anything to update, or jump straight to refreshing the brand-audit foundation?"

Wait for the user's response.

• "Yes / sounds right / 👍" → proceed to Turn 2.
• Correction ("we're not selling physical products," "actually our primary persona is X") → acknowledge the correction in one short phrase, update the internal model, then proceed to Turn 2. Save the correction note to /agent/brain/runneth-classic/workspaces/<slug>/pre-discovery-corrections.md so future turns inherit it.
• "What else?" → briefly add 2-3 more observations from the internal model if useful (e.g., "I also saw you're tracking N custom conversions, and your spend threshold is set to $X"), then ask again if it sounds right.

Turn 2 — Surface preference

Cannot be inferred from any signal. Ask directly.

"Where will you mostly use me — Slack, the web app, or both?"

Save to /agent/brain/runneth-classic/workspaces/<slug>/surface-preference.md:

# Surface preference
Captured: <ISO date>
Primary surface: <slack | web | both>

Turn 3 — Winner definition + the why

Lead with the inference you already built in Phase 0. Confirm in one sentence, then ask the why in the same turn.

"Looks like ROAS is your headline metric — your workspace goal is set to it and your saved reports lean on it. Spend shows up as the scale signal. So for winners I'd default to: ROAS as the efficiency call, spend as the scale check. Sound right, or do you grade differently? And the why behind it — growth mode, profitability squeeze, or holding steady?"

(Adjust the lead based on what Phase 0 actually surfaced — if workspace-goal points to a custom conversion, frame around that. If saved reports are all spend-sorted, lead with spend.)

The why answer changes how downstream creative bets get prioritized. Growth mode tolerates riskier upper-funnel bets. Profitability mode favors proven bottom-funnel formats. Maintenance mode wants iterations on what already works.

When the user responds, save to /agent/brain/runneth-classic/workspaces/<slug>/winner-definition.md:

# Winner definition for <workspace>
Captured: <ISO date>

Primary signal: <spend | conversion value | ROAS | custom conversion | other>
Secondary signal: <if applicable>
Custom conversion ID: <if applicable>
Business mode: <growth | profitability | maintenance | launch | other>
User's exact words on the metric: "<verbatim>"
User's exact words on the why: "<verbatim>"

Turn 4 — Creative workflow tools

Can be partially inferred. If Phase 0 surfaced any connected integrations or saved tool references, lead with those:

"I see you have Slack connected. For everything else — where do briefs live? Where do creative assets and final cuts go? Where do performance updates and weekly decisions get shared? Anything else worth knowing — name it and how your team uses it."

If nothing connected yet:

"A few quick questions about how creative work flows for your team:

• Where do briefs live? Notion, Google Drive, Frame.io, somewhere else?
• Where do creative assets and final cuts go?
• Where do performance updates and weekly decisions get shared?
• Anything else worth knowing — name it and how your team uses it."

Parse each named tool from any of the four sub-questions. Save to /agent/brain/runneth-classic/integrations-intent.md (ORG-LEVEL, not workspace-scoped):

# Integrations Intent
Captured: <ISO date>

## <tool name>
Connected: <yes | no>
Used for: <brief delivery | asset storage | performance updates | other — what the user said>
How we use it: <user's description, verbatim if short>
Specific surfaces / IDs / channels: <if mentioned>

If the file already exists, merge — don't overwrite. New entries append; existing entries update only if the user explicitly contradicts the prior entry.

Acknowledge briefly. Examples:

• "Got it — briefs in Notion, assets in Frame.io, performance in #performance Slack channel."
• "Saved. I'll know where to put briefs and where to look for assets when those integrations get connected."

Turn 4b — The graveyard (confirm what Phase 0 inferred)

Only run this turn if Phase 0's graveyard-inference.md has at least one specific pattern worth surfacing. Skip silently if the 365-day pull was too thin to identify anything.

Surface 2–3 specific findings as questions. Don't list everything Phase 0 inferred — just the strongest 2–3. The customer corrects what we got wrong.

"Looking at the last year of your ad library, a few patterns stood out:

• You tested heavily on greenscreen / talking-head format between October and December, peaked at $40K spend, then dropped to zero in January. Was that intentional — bad performance, or just seasonal rotation?
• Founder-led testimonials appeared in a small test in March and never came back. Tried once and not for you, or just not gotten back to?
• You haven't tested any pain-agitation hooks in the past year. Deliberate, or just not a direction yet?

Any of those reads wrong, and is there anything else you've sworn off I should avoid proposing?"

Compose dynamically from graveyard-inference.md. Use specific time ranges, specific spend numbers, and specific glossary categories the customer will recognize. Don't be vague.

Update graveyard-inference.md based on the response. Save customer corrections verbatim so the orchestrator's Step 0 re-anchor reads them on every future creative-strategy turn:

# Graveyard for <workspace>
Last updated: <ISO date>

## Confirmed dead (do not propose)
- <Pattern>: <why — user's words>
- <Pattern>: <why — user's words>

## Not actually dead (revisit possible)
- <Pattern>: <correction — e.g., "we paused those for fall, want to test again Q1">

## Never tried, deliberately
- <Pattern>: <why — user's words>

## Inferred by Phase 0 but not confirmed
- <Pattern>: <observation — customer didn't address>

When this turn fires, the orchestrator's creative-strategy chains read this file before generating concepts or hooks. The graveyard becomes binding — do not propose patterns marked "confirmed dead."

Turn 4c — Current pressure (what's coming up)

Can't be inferred. Single direct question.

"Anything coming up in the next 60 days I should know about — launches, sales, big creative bets you're trying to make work?"

Save to /agent/brain/runneth-classic/workspaces/<slug>/current-pressure.md:

# Current pressure for <workspace>
Captured: <ISO date>
Refresh cadence: every 60 days, or on request

## Coming up
- <event / launch / sale / push>: <when>, <what we're trying to make work>

## Active right now
- <campaign or focus the team is invested in>

## Recent changes
- <platform shifts, attribution drift, account issues the team has been navigating>

This file should refresh roughly every 60 days. The orchestrator surfaces it for any creative-strategy turn so concepts respect upcoming pressure.

Turn 5 — Watched brands

Lead with what Phase 0 already saw. The workspace likely has competitors tracked in brand-context. Enrich:

"You already have and in your tracked brands. Anything else you watch for inspo specifically — brands you admire and want to learn from but might not be direct competitors? Or other competitors I should add?"

If nothing tracked yet:

"Any brands worth keeping an eye on? Two buckets — inspo brands (you admire and want to learn from) and direct competitors (you're actively differentiating against). Drop names or skip."

For each new name given, call motion search-brands --search-term "<name>" to resolve the brand ID. If multiple matches, surface candidates and ask which one (extra conversational turn).

Save to:

• /agent/brain/runneth-classic/workspaces/<slug>/watched-brands/inspo.md
• /agent/brain/runneth-classic/workspaces/<slug>/watched-brands/competitors.md

Per file:

# Watched brands — <Inspo | Competitors>
Captured: <ISO date>

## <Brand Name>
Brand ID: <motion brand ID, or "not resolved" if search-brands failed>
Domain: <if returned>
Why we're watching: <user's reason if given>
Added: <ISO date>

If a name couldn't be resolved (search-brands returned nothing or low confidence), flag it in the file as not resolved and tell the user. Don't fail silently.

Pass the resolved competitor list forward to brand-audit's setup so it pre-fills the competitor shortlist.

Acknowledge: "Saved. Tracking inspo and competitor brands."

Turn 6 — Customer voice

Use what Phase 0 inferred about product type to frame the question. Don't ask generically.

If product type is SaaS / B2B / service:

"Since you're <SaaS / B2B / service>, my guess is your customer voice mostly lives internal — Gong calls, support tickets, sales call notes — rather than public reviews. Where's the strongest source for you?"

If product type is physical / consumer:

"Where do customer reviews live for ? Trustpilot, Amazon, Reddit, your own site? Plus anything internal I should also use — interviews, support tickets, recorded customer calls."

Always also ask:

"Any themes you specifically watch — shipping complaints, comparison to , gift-purchase signals, churn reasons?"

Save to /agent/brain/runneth-classic/workspaces/<slug>/customer-voice-intent.md:

# Customer voice intent
Captured: <ISO date>

## Primary sources
- <source 1, with connection status>
- <source 2>

## Themes to watch
- <theme 1>
- <theme 2>

## Non-public sources
- <Gong calls, internal docs, etc.>

Turn 7 — Optional uploads (conditional)

Check ./uploads/ for files dropped during setup using ls ./uploads/.

If files exist:

"I see files in uploads. Want me to classify them? Each can be:

• Brand context (voice, positioning, audience docs)
• Legal guidelines (claims rules, compliance docs)
• Competitor creative (competitor ads, references)
• Reviews / VOC (review CSVs, interview transcripts, Gong quotes)
• Other (tell me what)"

For each file, classify and save to the right path:

• BRAND_CONTEXT → /agent/brain/runneth-classic/workspaces/<slug>/brand-context-upload.md
• LEGAL_GUIDELINES → /agent/brain/runneth-classic/workspaces/<slug>/compliance-notes.md
• COMPETITOR_CREATIVE → /agent/brain/runneth-classic/workspaces/<slug>/competitor-references/<filename>
• REVIEWS → /agent/brain/runneth-classic/workspaces/<slug>/customer-voice/reviews-upload.md
• Other → ask once where it should live, save accordingly

If no files in uploads: skip this turn entirely — do not say "no files in uploads" out loud. Just move on.

Turn 8 — Hand off to brand-audit

If brand-audit hasn't run for this workspace yet, hand off:

"Now I'll build your brand foundation — products, customer voice, keywords, the competitor work, and the persona × angle × stage matrix. Takes about 10-15 minutes. I'll come back with your first prompts when it's done."

If brand-audit already ran (Phase 0 saw /agent/brain/brand-audit/<workspace>/strategy.md from a prior session), skip the heavy run and only refresh deltas:

"Brand foundation is already built from earlier. I'll refresh anything that's stale based on what you just told me, then we're done. Give me a minute."

Invoke the setup-brand-audit skill. Pre-populate with:

• Brand site URL (brand-audit will ask if not in brand-context)
• Review sources from Turn 6
• Competitor shortlist from Turn 5
• Slack channels for refresh ping (brand-audit will ask)

Brand-audit runs its Foundation layer and writes to /agent/brain/brand-audit/<workspace-slug>/.

Turn 9 — Closing handoff (automatic, after brand-audit completes)

When brand-audit reports completion, post into the same conversation thread. The closing is not a status list — it's a narrative summary that paints a picture of what Runneth now knows and how it will use that to work differently. The customer should read this and feel like Runneth genuinely gets them.

Required reads before composing the close:

1. /agent/brain/brand-audit/<workspace>/strategy.md — primary persona, pain, angle, stage, lens fit
2. /agent/brain/brand-audit/<workspace>/brand-context.md — brand name, positioning
3. /agent/brain/runneth-classic/workspaces/<slug>/winner-definition.md
4. /agent/brain/runneth-classic/workspaces/<slug>/surface-preference.md
5. /agent/brain/runneth-classic/workspaces/<slug>/watched-brands/inspo.md
6. /agent/brain/runneth-classic/workspaces/<slug>/watched-brands/competitors.md
7. /agent/brain/runneth-classic/workspaces/<slug>/customer-voice-intent.md
8. /agent/brain/runneth-classic/integrations-intent.md
9. /agent/brain/runneth-classic/workspaces/<slug>/pre-discovery-corrections.md (if exists)

Synthesize into a five-section close. Each section is specific to what was captured. Compose dynamically; do not just paste a template.

Structure:

You're set up. Here's what I've got on you for <Brand Name>:

**Who you're building for.**
<2 sentences synthesizing the strongest persona × pain × stage × lens intersection
from strategy.md. Name the persona. Name the specific tension. Note the awareness
stage and the messaging lens that fits.>

**How you grade winners.**
<1-2 sentences from winner-definition.md, in plain language. Include the spend floor
and the secondary signal. Frame it as "so when you ask me 'what's working' I'll lead
with X and re-rank by Y.">

**Who you're watching.**
<List inspo brands by name with a one-line note on each ("admire their <observed
pattern>"). List competitor brands by name with a one-line note ("actively
differentiating against"). If any brand wasn't resolvable in Motion's ad library,
flag it: "flagged but not in the library yet — say the word and I'll search".>

**Where work flows.**
<Specific bullets from integrations-intent.md: briefs → <tool>, assets → <tool>,
performance updates → <tool>. Include the customer voice source from
customer-voice-intent.md with connection status.>

**How this changes how I work with you.**
<A short narrative paragraph (3-4 sentences) tying the above into concrete behavior.
This is the part that paints the picture. Example shape: "When you ask what's working,
I'll lead with <winner signal>, surface <persona>'s ads first, and re-rank by your
<secondary signal> check. When you want hooks, I'll condition them on <persona>
facing <pain> and run them through the <lens> lens. When you want a competitor read,
I know to start with <competitor>. When you have a brief to write, I know it lives
in <brief tool>.">

**Try one:**
1. <Performance-grounded prompt tied to their winner-definition, e.g., "Pull this
   week's top performers, re-ranked by spend with a $200 floor.">
2. <Persona-grounded prompt tied to their strategy.md, e.g., "Give me 5 hooks for
   <specific persona> facing <specific pain>.">
3. <Watched-brand-grounded prompt tied to Turn 5, e.g., "Show me what <inspo brand>
   is running right now.">

Do you want me to <single concrete next move tied to the strongest opportunity
in what was captured>?

Composition rules for the close:

• Be specific to this workspace. If the persona is "performance marketer who can't tell which creative element is moving the needle," use those exact words from strategy.md — don't paraphrase to "a marketer struggling with attribution."
• Use names, not categories. "Viktor and Foreplay" not "two competitors." "Notion" not "a brief management tool."
• Acknowledge gaps honestly. If Parker AI couldn't be resolved in the ad library, say so. If Gong isn't connected yet, frame it as "connect when you're ready and I'll mine calls directly."
• Tie the narrative paragraph to actual user choices. The "How this changes how I work" section should reference specific values from the captures, not generic capabilities.
• End with one specific yes/no. Not three options. Not "any of these sound good?" One concrete question tied to the strongest opportunity visible in the captured data.

What this close does NOT include:

• A list of installed use cases. Customers don't care about plan-mode and corpus-search as line items — they care about what those capabilities feel like in practice. The narrative paragraph captures that implicitly.
• A description of recurring routines. Those are opt-in and explicit — mentioning them sets a false expectation.
• Generic example prompts. Every "Try one" prompt must reference a specific captured value (persona name, watched brand name, integration tool name).

---

Handling complications mid-onboarding

Multi-answer turns

If the user answers multiple questions in one message ("Slack, and winners are spend"), parse all of them and save each. Don't make them re-answer. Then move to the next unanswered question without re-asking.

"Skip" or "next"

If the user says "skip" / "next" / "pass" / "n/a" on any question, save an explicit "skipped" marker and move on. Don't re-ask. They can always come back via "update my setup."

Off-topic asks during setup

If the user asks something unrelated mid-setup ("what's your favorite hook tactic"), answer briefly then return to the turn they were on. Don't lose state.

Re-invocation

If the user says "set up runneth," "reconfigure runneth," or "rerun runneth setup," re-run Phase 0 from scratch (so the model is fresh) and re-run Phase 1. Each turn's save updates the existing file rather than creating a duplicate.

For partial updates ("update my watched brands," "change my winner definition"), jump directly to the relevant turn and update only that file.

Constraints

• Do not invoke this skill on every conversation. One-time setup, re-runnable only on explicit request.
• Do not auto-activate any routines. Opt-in per the pack's design.
• Do not assume a Meta account is connected. Check workspace-goal first.
• Do not save any file in ./uploads/ itself. Uploads are read-only.
• Update /agent/INDEX.md after Phase 1 completes with entries for all files written.
• Do not list multiple questions in a single message. One question per turn.
• Never say "Step N" or use numbered step headings in any user-facing message. Internal turn numbers in this skill exist for the skill author's reference only. The customer should never see them.
• Phase 0 must complete before Turn 1 starts. The opener must include synthesis from Phase 0; it cannot be a generic welcome.