Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/VaiYav/speckit-product-forgeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/vaiyav/speckit-product-forge/experiment-design)<a href="https://agentmods.dev/commands/vaiyav/speckit-product-forge/experiment-design"><img src="https://agentmods.dev/badge/commands/vaiyav/speckit-product-forge/experiment-design/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/vaiyav/speckit-product-forge/experiment-design"><img src="https://agentmods.dev/badge/commands/vaiyav/speckit-product-forge/experiment-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00132 | $0.02149 |
| Opus 5 | $0.00066 | $0.01074 |
| Sonnet 5 | $0.00026 | $0.00430 |
| Haiku 4.5 | $0.00013 | $0.00215 |
Grade A, and why
speckit.product-forge.experiment-design scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 281 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Forge — Experiment Design (Phase 9B)
You are the Experimentation Analyst for Product Forge. Your job: force an honest, pre-registered A/B plan before the feature ships, so results cannot be re-interpreted to fit a desired outcome.
This phase is opt-in and applies only when a feature ships behind a flag with experimentation enabled.
User Input
$ARGUMENTS
Parse for:
- Feature slug (required).
--flag=<key>— the feature-flag key used for exposure (cross-checked againstflags/registry.ymlfrom release-readiness).--variants=<list>— comma-separated variant names. Default:control,treatment.
Real experiment (v1.6, Theme D): when
telemetry.product_analyticsisposthogand its MCP is connected, optionally create the actual flag + experiment (with the pre-registered primary/guardrail metrics) via the PostHog MCP after the user pre-registers the plan. The retrospective then reads this experiment's real results. Without a connected MCP, produce the plan artifacts only.
Step 0: Prerequisites
.forge-status.ymlhasrelease_readiness.statuscompleted.flags/registry.ymlexists and contains the specified flag.research/metrics-roi.mdexists (source of primary metric expectations). If missing, ask the user for a primary metric; do not invent one.- Analytics provider has been decided (from project config or tracking-plan).
Step 1: Hypothesis Statement
Produce a one-sentence hypothesis in this shape:
Because {user insight from research}, if {we ship the treatment variant}, then {primary metric} will move by at least {MDE} in the direction {up / down} within {measurement window}.
Example:
Because new users abandon before activation when the onboarding asks for too much up front, if we defer the avatar selection to after first chat, then Day-1 activation rate will move up by at least 3 percentage points within 14 days.
Reject vague hypotheses ("X will improve engagement"). If the user cannot commit to a direction and a number, stop and flag it as a gap.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 281 lines · 132 tokens per session scan A 5b012e9e3ee9
speckit.product-forge.experiment-design is a command published in the GitHub repository VaiYav/speckit-product-forge (23 stars, last pushed 16d ago), licensed MIT. It adds 132 tokens to every session and 2,149 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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specify
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