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/interpretgit 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 | $0.02074 |
| Opus 5 | $0.00000 | $0.01037 |
| Sonnet 5 | $0.00000 | $0.00415 |
| Haiku 4.5 | $0.00000 | $0.00207 |
Grade A, and why
interpret 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 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.
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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Command: interpret
Interpret a Mixpanel experiment's results and health checks. This command consumes the verdicts the platform already returns. Never recompute thresholds (SRM, significance, sufficient-exposures, etc.). If a verdict field is missing, say so — do not synthesize one from raw values.
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 (interpret-specific)
- Polarity. Whether a movement is good for the business. Combines sign of lift with the metric's
direction("up" = bigger is better; "down" = smaller is better). See the Polarity recipe in Components. - Significance. The platform's per-row classification: significant-positive, significant-negative, or not-significant. Read it from the result — do not recompute.
- SRM (Sample Ratio Mismatch). Variants received traffic in proportions that disagree with the configured split. Kohavi's #1 trustworthiness check — when SRM fails, downstream lift, p-values, and CIs cannot be trusted.
- Retro A/A (pre-experiment bias). Re-runs the comparison on the pre-exposure period. A failure means cohorts already differed before treatment started.
- Twyman's Law. "Any unusually clean or unusually large result is more likely a bug than a discovery." Apply on lifts > ~30% — usually a changed-denominator artifact.
- Trustworthiness gate. The pre-flight check that runs before any results interpretation: SRM ok, Retro A/A clean, exposures sufficient, ≥3-day window, no misconfig. Failing any of these means do not interpret results yet — route to the health-check reference.
Components (interpret-specific)
Polarity recipe (load-bearing — apply on every metric row)
This is the canonical polarity recipe for the skill — the interpret references point back here instead of restating it.
The platform's result buckets (positive / negative / no-effect) classify by sign of lift, NOT by business value. Translate each row through the recipe before drawing any conclusion.
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 · 117 lines · 0 tokens per session scan A 0b78b459e7d5
interpret 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 2,074 tokens. 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.
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.