Pavilion AI in GTM School Session 5 · 201 Companion

Power Prompting 201

The moves you reach for once the Six-Layer Stack is second nature.

Scott Wueschinski · scott@gtmify.io · May 2026

The Session 5 hour was deliberately 101: name the six layers, install the toolkit, customize one skill end-to-end. If you walked out of that room and the framework already feels obvious, this page is for you. Five moves that the live hour didn't have time for — each one is what separates a team that uses skills from a team that compounds them.

1.Chaining skills into pipelines

The 101 move: one trigger phrase fires one skill. The 201 move: one trigger phrase fires a sequence of skills, where each skill's output is the next skill's input.

Concrete example from the toolkit: a single trigger — "ramp the Acme account" — chains four skills that all ship in GTMify/aigtm: prospect-researchabmmeeting-prepdeal-strategy. The first skill's company profile feeds the second skill's account plan and buying-committee map. The second skill's stakeholder list becomes the third skill's "who do I prep for" input. The third skill's briefing becomes the fourth skill's strategy starting point. Forty-five minutes of work for one prompt.

Pattern What it does When to reach for it
Sequential chain Skill A's output is Skill B's input Multi-step research, account warm-up, end-to-end deal review
Fan-out / fan-in One input → N skills run in parallel → one synthesizer skill merges results Portfolio-wide audits, multi-account briefings, batch enrichment
Conditional dispatch A router skill picks which skill to run next based on its own output "Triage this lead" workflows where the next move depends on tier
Operational rule. Chains break the same way every time — when one skill's output spec drifts from the next skill's expected input. Lock the contract. The Output Spec section explains how.

2.Custom Layer 1 patterns

In the live hour we edited a single skill's Layer 1 (Context) with your company, ICP, and competitors. That's the universal pattern. There are four customization patterns underneath it that compound differently:

Pattern A — Global Context (one source of truth)

Put your company, ICP, top 3 competitors, value prop, and typical deal size into ~/.claude/CLAUDE.md (or the platform equivalent). Every skill reads it automatically. Highest leverage. Lowest maintenance.

Pattern B — Per-account Context (CRM-as-context)

For named accounts, drop an accounts/<account-slug>.md file with the account's own ICP variant, internal stakeholders, deal history, and competitive dynamics. Skills that touch named accounts reach into it first. Especially powerful for the meeting-prep and deal-strategy families.

Pattern C — Per-persona Context

For roles that come up repeatedly in your motion (CRO, VP RevOps, FP&A lead), keep a personas/<role>.md file with the role's typical priorities, vocabulary, and decision-making patterns. Skills that draft for these roles inherit the voice automatically.

Pattern D — Quarterly Context refresh

Layer 1 isn't write-once. Put a recurring 15-minute calendar block on the first Monday of every quarter to refresh: new competitor, repositioned value prop, revised ICP. Skills inherit the new world automatically — no per-skill edits required.

When NOT to use a skill

This is the question the live hour didn't have time for. Three cases where reaching for a skill is the wrong move:

3.Anti-hallucination constraints at scale

Layer 4 (Constraints) in the 101 hour was a single sentence: "No more than 250 words. No 'it depends.' If you don't have enough info, ask one clarifying question instead of guessing." Good. Now scale it across a team and a portfolio.

Constraint type Example What it prevents
Source-bound "Only use information from the pasted context. If a fact is not present, say 'unknown' rather than inferring." Confident-sounding fabrications about specific accounts, deals, or people
Quantitative bounds "Do not include any percentage or dollar amount unless it appears verbatim in the source." Plausible-but-invented stats that survive into customer-facing artifacts
Tone bounds "Hedge any claim about competitor pricing. Never say 'they charge X' — say 'public list price suggests X'." Legal exposure on competitive collateral
Refusal bounds "If the input does not contain a stage, value, or close date, refuse the task and request the missing fields." Skills running on bad input and producing confident garbage
Field rule. Hallucination doesn't get fixed by a smarter model — it gets fixed by tighter constraints. Constraints are the only layer that removes capability, which is exactly why they raise output quality.

4.Example libraries that grow with your team

The 101 move: one in-line example in Layer 5 to anchor the output. The 201 move: treat examples as an asset. When a teammate produces a great output, add it to the skill's example library. The skill gets sharper every week without anyone editing the prompt itself.

Mechanics:

What this unlocks: a team writing about a skill's output, not fighting it. The library is the artifact that turns a one-person prompt into a team-owned asset.

5.JSON output for chained pipelines

If you're going to chain skills (Section 1), the Output Spec stops being about readability and starts being about contract. Two skills compose cleanly when the first emits a structured object the second can parse.

Concrete pattern: a research skill that emits plain prose looks like:

Acme is a $50M ARR healthcare-payments company...
Their CFO recently mentioned compliance as a top priority...
The CTO has a strong preference for open-source tooling...

The same skill with a 201 Output Spec:

{
  "company": "Acme Health",
  "arr_band": "$25M-$75M",
  "industry": "healthcare-payments",
  "stated_priorities": ["compliance", "cost-takeout"],
  "stakeholders": [
    {"name": "CFO", "stated_concern": "compliance"},
    {"name": "CTO", "stated_preference": "open-source"}
  ],
  "confidence": "medium",
  "missing_fields": ["renewal_date", "current_stack"]
}

The first version is readable. The second is composable. A downstream deal-strategy skill can consume it without re-parsing prose. Every chained pipeline I run depends on this discipline.

Adoption order. Don't JSON-ify everything on day one. Start with the 2–3 skills you actually chain. Leave the human-facing skills (board updates, post-call summaries) in prose — JSON output is for machine consumers, not humans.

6.Five 201 moves you can apply next week

  1. Pick one chain. Identify the two skills you use in sequence most often. Make the first one emit structured output the second one can consume. One Monday afternoon's work.
  2. Promote your Layer 1 to global. Move company/ICP/competitors/value-prop out of any single skill and into ~/.claude/CLAUDE.md. Every skill instantly inherits.
  3. Add a refusal constraint to one customer-facing skill. Pick the skill whose hallucinations would cost you most. Write the refusal rule. Test it once with deliberately incomplete input.
  4. Start an examples library on your highest-stakes skill. Drop two great outputs from the past quarter into examples/. Reference them in Layer 5. Watch quality compound.
  5. Calendar a quarterly Context refresh. First Monday of next quarter, 15 minutes. Update Layer 1 once; every skill in the toolkit inherits the new world.
"The 101 move is to write a better prompt. The 201 move is to write a prompt you never have to write again — and that your teammates can extend without asking you first."