# Gtm Decision

> Close a running GTM experiment — collect the results defined in gtm/exp-NNN/TASKS.md from real data only, judge them against the criteria locked at start, and write DECISION.md with an honest Continue / Iterate / Kill verdict. Use when the user runs /gtm-decision or says an experiment has ended.

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

---


# GTM Decision

An experiment ended. Your job: gather honest results, judge them against the rules that
were locked **before** the experiment started, and record the human's final call.

**Mode:** with `USECASEIFY_API_KEY` set, report the outcome to the registry (connected
mode — this closes the experiment's public page and unlocks your next enrichment).
Without a key, everything below works fully offline.

## Step 1 — Locate the experiment

Find `gtm/exp-*/criteria.lock.json` for the running experiment (if several, ask which).
Read the locked rules, primary metric, minimum evidence, and duration from it — **the
locked file, not from memory, not from BRIEF.md edits made after the start.**

## Step 2 — Collect results (real data only)

Open the experiment's `TASKS.md` → **"Results to report"** — the exact quantitative fields
this experiment committed to measure.

For each field: read it from the real data source the implementation set up (saved SQL
query, export script, analytics view, database). **If a value cannot be read from real
data, set it to `"unknown"`. Never estimate, extrapolate, or fill in a plausible number** —
a fabricated number poisons the verdict.

Then ask the founder: the qualitative questions from TASKS.md, whether anything unexpected
or any implementation issue could distort the data, and a one-paragraph summary in their
own words.

## Step 3 — Confirm before judging

Show a table of every value and where it came from. Get an explicit yes.
(Connected mode: this is also what gets published — aggregate numbers only, never
customer-identifying data, never secrets.)

## Step 4 — Judge against the locked rules

First run the **data-quality checklist** (deterministic — do this before any judgment):

- [ ] `no_quantitative_data` — no numeric results at all
- [ ] `missing_primary_metric` — fields needed for the primary metric are missing/unknown
- [ ] `insufficient_sample` — largest relevant count below the locked minimum sample
- [ ] `ended_too_early` — ran less than half the locked duration
- [ ] `self_reported_only` — only qualitative, self-reported evidence

Then decide — exactly one of:

- **continue** — evidence supports the assumption; the next investment is justified
- **iterate** — the underlying problem/demand looks real, but the approach, message,
  price, or implementation needs to change
- **kill** — evidence does not support continued investment in this assumption

Rules of judgment:

- Judge against the LOCKED rules first. If results are ambiguous between two verdicts,
  pick the one the locked rules support and say why.
- **Confidence** is `low` / `medium` / `high` — qualitative only. Any flag above caps it
  at `medium`; a severe flag (`missing_primary_metric`, `insufficient_sample`,
  `ended_too_early`) forces `low`. No exceptions, including for exciting results.
- Use ONLY numbers present in the collected results, quoted verbatim. Never compute
  figures that were not reported. Never say "market validated".
- List evidence **for and against** the verdict, and at least one limitation.
- Recommend exactly ONE next experiment (2–3 sentences).

## Step 5 — Deliver and record the final call

1. Write `gtm/exp-NNN/DECISION.md` per `docs/format.md`: the verdict large and unsoftened,
   confidence, rationale against the locked rules, results table, evidence both ways,
   limitations + data-quality flags, what it means in plain business language, the one
   next experiment, and next actions for human and agent.
2. **The founder decides, not you.** Ask: accept the recommendation, or override it?
   An override requires a one-line reason, recorded in DECISION.md next to the AI
   recommendation — both stay on the record.
3. *Connected mode:* `POST {base}/api/v1/report` with the results, verdict, final call,
   and override reason if any — this closes the public page honestly (an abandoned
   experiment can also be closed as `abandoned`; that too is honest data). *(If the server is unreachable, skip the report
   and note it — the local DECISION.md remains the record.)*
4. Update `experiment.json` status to `completed`, suggest committing `gtm/`.
5. If CONTINUE or ITERATE: offer `/gtm-next-experiment` for the follow-up. If KILL:
   acknowledge plainly — a clean kill is a successful experiment; it just saved weeks of
   misdirected work.