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.
npx agentmods add commands/mixpanel/ai-plugins/designgit clone --depth 1 https://github.com/mixpanel/ai-pluginsWhat 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 | $0.00000 | $0.03104 |
| Opus 5 | $0.00000 | $0.01552 |
| Sonnet 5 | $0.00000 | $0.00621 |
| Haiku 4.5 | $0.00000 | $0.00310 |
Grade B, and why
design scanned grade B with 1 finding 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 2d 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.
Strips warnings and disclaimersmediumAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
- Don't moralise about peeking — switch them to sequential. How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Command: design
Design a Mixpanel experiment before launch. A well-designed experiment starts from the hypothesis and works backward: the hypothesis dictates the metrics that test it, the metrics dictate the sample size, the sample size + traffic dictate duration and testing model. This command stops at DRAFT — the irreversible launch happens in the separate launch command. Don't save the draft until the user explicitly confirms the configuration.
The umbrella SKILL.md defines the shared glossary (Variant, Primary/Guardrail/Secondary metric, Direction, Lift, MDE, CUPED, Winsorization, Multiple-testing correction). Phase-specific terms below.
Glossary (design-specific)
- Hypothesis. A falsifiable, directional claim with a stated mechanism, bounded in time. Shape: "If
<change>, then<metric>will<direction>by ≥<MDE>, because<mechanism>." Every other decision flows from this. - Power. The probability the experiment detects a true effect of size MDE. Default 80%.
- Underpowered. Achievable MDE on available traffic exceeds the user's expected lift. Most likely outcome is "inconclusive"; reachable significance is biased upward (winner's curse).
- Sequential vs Frequentist testing. Sequential makes peeking safe (boundary-based stopping); Frequentist requires a fixed sample committed up front. Most users should default to Sequential.
Components (design-specific)
Sizing formulas
Required sample per variant (two-sample, two-sided, 95% confidence, 80% power):
n = 16 × σ² / d²
Inverted for traffic-bound teams — the smallest effect detectable on available traffic (Kohavi's inversion):
MDE = 4σ / √n
The 16 is (z_{α/2} + z_β)² × 2 rounded. Variance σ² depends on metric type: Bernoulli p(1−p); Poisson ≈ mean; Gaussian computed from data. The full derivation, worked examples, lookup table, and the five remediations for underpowered experiments live in ../references/sizing.md.
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.
- 2d ago First seen · 172 lines · 0 tokens per session scan B a9110681b271
design is a command published in the GitHub repository mixpanel/ai-plugins (15 stars, last pushed 8d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 3,104 tokens. A static security scan graded it B with 1 finding (strips warnings and disclaimers). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.