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 scanning-experiments-with-replay-visiongit 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/scanning-experiments-with-replay-vision)<a href="https://agentmods.dev/skills/posthog/posthog-foss/scanning-experiments-with-replay-vision"><img src="https://agentmods.dev/badge/skills/posthog/posthog-foss/scanning-experiments-with-replay-vision/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/scanning-experiments-with-replay-vision"><img src="https://agentmods.dev/badge/skills/posthog/posthog-foss/scanning-experiments-with-replay-vision.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 | $0.00194 | $0.05106 |
| Opus 5 | $0.00097 | $0.02553 |
| Sonnet 5 | $0.00039 | $0.01021 |
| Haiku 4.5 | $0.00019 | $0.00511 |
Grade A, and why
scanning-experiments-with-replay-vision 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- scanning-experiments-with-replay-vision — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scanning experiments with Replay Vision
The job: "I'm running an experiment. Watch the recordings and tell me what's actually happening in each variant."
A Replay Vision scanner is a standing LLM probe over session recordings (see [[creating-replay-vision-scanners]] for the general mechanics). Scoping one to an experiment fixes the classic ways scanners go wrong, all at once: the exposure filter is derived server-side from the experiment_targeting field instead of hand-authored, the prompt is templated from the hypothesis and variants instead of vague, the population is bounded by enrollment, and the experiment's end date gives the scanner a natural end. This skill covers what is experiment-specific; the generic create/size mechanics stay in the parent skill.
The flow: resolve the experiment → set experiment_targeting so the API derives the exposure filter → pick a template → size it → create disabled → preview the prompt on a few real sessions → let the user enable it.
Step 1: Resolve the experiment
experiment-get returns everything needed: feature_flag_key, the linked feature_flag (its filters.multivariate.variants list is the source of truth for variant keys — parameters.feature_flag_variants can be stale), exposure_criteria, resolved_exposure_event, start_date, end_date, and status. If the user didn't identify the experiment, resolve it via [[finding-experiments]] rather than guessing.
You no longer derive the exposure event to build the scan query — the API does that from experiment_targeting (Step 2). You still need the event name for the per-variant readout join at the end; the readout section covers that derivation where it is used.
Guards before doing anything else:
- Draft (no
start_date): there are no exposures and nothing to scan. Say so and stop. - Stopped/complete: a new scanner only sees sessions from creation time onward, and historical backfill is not automatable over MCP (see Limits). A concluded experiment has nothing left to watch — offer the UI backfill path or a handful of
vision-scanners-scan-sessioncalls instead. - Running or exposure-frozen: proceed. A frozen experiment stops enrolling but already-exposed users keep producing sessions, so scanning stays useful.
- Already half over: the scanner watches only the remaining run. Say so, so a per-variant readout isn't mistaken for full-run coverage.
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 Changed ec22eff065db
- 9d ago First seen · 169 lines · 194 tokens per session scan A e227f07fd9e8
scanning-experiments-with-replay-vision is a skill published in the GitHub repository PostHog/posthog-foss (715 stars, last pushed today), licensed MIT. It adds 194 tokens to every session and 5,106 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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