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 skills/tarekkharsa/agentstack/experimentnpx skills add Tarekkharsa/agentstack --skill experimentgit clone --depth 1 https://github.com/Tarekkharsa/agentstackWhat 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 | $0.00035 | $0.00470 |
| Opus 5 | $0.00017 | $0.00235 |
| Sonnet 5 | $0.00007 | $0.00094 |
| Haiku 4.5 | $0.00003 | $0.00047 |
Grade A, and why
posthog_experiment 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 yesterday.
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.
What it actually says
PostHog Experiment
Unofficial, agentstack-authored. Not affiliated with or endorsed by PostHog.
Use this skill when a team wants to test a change with a PostHog experiment or roll a feature out behind a flag, and needs the setup to actually yield a trustworthy result.
Workflow
- Write the hypothesis as one falsifiable sentence: "Changing X will move [primary metric] by roughly Y for [population]." If you cannot state it this way, the experiment is not ready.
- Choose one primary metric that maps directly to the goal (e.g. signup conversion), plus a small set of guardrail metrics that must not regress.
- Define the exposure point — the feature flag and the event that marks a user as enrolled. Users must be counted from the moment they could see the change, not from when they convert.
- Estimate the needed sample size and runtime from the baseline rate and the minimum effect worth detecting. State how long the test must run; resist calling it early.
- Set the rollout: start the flag at a safe percentage, confirm assignment is stable per user, and verify both variants render before ramping.
- Pre-commit to the decision rule (ship / kill / iterate) and the metrics that decide it, in writing, before launch.
Conventions
- One primary metric per experiment. Multiple primaries invite cherry-picking.
- Do not peek-and-stop: respect the planned runtime and sample size.
- Keep the feature flag key descriptive and consistent with the experiment name.
- Roll out gradually (e.g. 5% → 25% → 100%) and watch guardrails at each step.
Boundaries
- Never declare a winner before the experiment reaches its planned sample size or runtime — early results are noise.
- Do not change the primary metric or population mid-flight to chase significance.
- Do not flip a flag to 100% for all users without an explicit go-ahead.
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.
- yesterday First seen · 45 lines · 35 tokens per session scan A 0fcb96a1f9fd
posthog_experiment is a skill published in the GitHub repository Tarekkharsa/agentstack (3 stars, last pushed 18d ago), licensed Apache-2.0. It adds 35 tokens to every session and 470 once invoked, about $0.0002 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 2026-08-31.
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