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 skills add PostHog/posthog-foss --skill diagnosing-experiment-resultsgit clone --depth 1 https://github.com/PostHog/posthog-fossWrote 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/skills/posthog/posthog-foss/diagnosing-experiment-results)<a href="https://agentmods.dev/skills/posthog/posthog-foss/diagnosing-experiment-results"><img src="https://agentmods.dev/badge/skills/posthog/posthog-foss/diagnosing-experiment-results/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/skills/posthog/posthog-foss/diagnosing-experiment-results"><img src="https://agentmods.dev/badge/skills/posthog/posthog-foss/diagnosing-experiment-results.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00208 | $0.03197 |
| Opus 5 | $0.00104 | $0.01598 |
| Sonnet 5 | $0.00042 | $0.00639 |
| Haiku 4.5 | $0.00021 | $0.00320 |
Grade A, and why
diagnosing-experiment-results 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- diagnosing-experiment-results — 91% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Diagnosing experiment results
This skill answers: My PostHog experiment results look wrong, biased, or empty — what's going on?
Match the user's complaint in the dispatch table, then read the matching reference file for the diagnostic.
Each diagnostic in the reference files is tagged [HIGH], [MEDIUM], or [LOW] based on how
strongly it's verified — [HIGH] is verified directly in PostHog code, [MEDIUM] is partially or
team-source verified, [LOW] describes SDK/external behavior that wasn't verified here. Treat [LOW]
items as hypotheses to test, not facts to assert.
Step 1 — Resolve the experiment
If the user refers to an experiment by name or description, load the finding-experiments skill first to
resolve it to a concrete ID.
Call experiment-get and pull these fields. They are inputs for almost every diagnostic:
parameters.feature_flag_variants[].rollout_percentage— the variant splitparameters.rollout_percentage— the overall rollout (% of users entering the experiment)exposure_criteria.multiple_variant_handling— defaults to"exclude"if absentexposure_criteria.exposure_config.event— unset means the default exposure event; read which one fromresolved_exposure_event($feature_flag_calledor$experiment_exposure— resolved server-side, same properties either way)exposure_criteria.filterTestAccounts— defaults totruefeature_flag.active, status (draft/running/paused/exposure_frozen/stopped),start_date,end_datefeature_flag.filters.groups[]— for each group readvariant,properties, androllout_percentage. Any non-nullvariantis a forced-variant override on the matched cohort (release-condition assignment, not randomized) — surfaces A7. Watch for the severe shape (A7b): a variant-pinned group with broad/emptypropertiesat high rollout, or no group left randomized (variant: null) / no release path to one arm — that starves the other variant (one arm gets ~0 analyzable exposures). Seereferences/bias-and-skew.md.stats_config— Bayesian (default) or Frequentist
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 202 lines · 208 tokens per session scan A c5ae4b7bfe92
diagnosing-experiment-results is a skill published in the GitHub repository PostHog/posthog-foss (715 stars, last pushed today), licensed MIT. It adds 208 tokens to every session and 3,197 once invoked, about $0.0010 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 2026-09-03.
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