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 debugging-experimentsgit 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/debugging-experiments)<a href="https://agentmods.dev/skills/posthog/posthog-foss/debugging-experiments"><img src="https://agentmods.dev/badge/skills/posthog/posthog-foss/debugging-experiments/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/debugging-experiments"><img src="https://agentmods.dev/badge/skills/posthog/posthog-foss/debugging-experiments.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Prompt Injection · line 217 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
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 | $0.00209 | $0.04934 |
| Opus 5 | $0.00105 | $0.02467 |
| Sonnet 5 | $0.00042 | $0.00987 |
| Haiku 4.5 | $0.00021 | $0.00493 |
Grade A, and why
debugging-experiments 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:
- debugging-experiments — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 287 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugging experiments
PostHog Experiments are A/B tests: a feature flag randomizes users into variants, the SDK records an exposure when the flag is read, and PostHog computes per-variant metrics and significance. A customer looks at that results page and asks why it looks wrong.
Most experiment-results tickets are config or exposure-collection problems, not statistics bugs. The randomization is fine; something upstream is skewing which users get exposed, or stopping exposures from being recorded. The job is to find which, prove it with the customer's own data, and hand back a plain-language explanation plus the fix.
This skill is the customer-support front door. It carries the two most common complaints
inline (uneven exposures, missing exposures) and loads
diagnosing-experiment-results as a diagnostic
library for the deeper long tail (interpretation traps, numbers-vs-SQL, mid-run surprises).
Debugging workflow
- Parse the ticket. Extract project ID, instance (US vs EU — the URLs and data live in
different places), experiment ID or name, the
lib/platform if relevant, the exact complaint in the customer's words, and what they already tried. Aged or multi-reply tickets are dirty: the config may have been edited mid-thread, so re-pull current state and treat earlier claims as stale. - Resolve the experiment. If the ticket names it rather than giving an ID, load
finding-experimentsto resolve it, then callposthog:experiment-get. - Pull the data read-only. Run the fixed data-pull sequence in
references/pulling-the-data.md. This produces the
"pertinent numbers" you will show the customer: per-variant exposed-person counts,
$multipleshare, thedistinct_id/personfragmentation ratio, the SRM chi-squared result, the exposure trajectory, and the flag/experiment activity log. Verify from data before asking the customer anything. - Match the complaint to the known-cause catalog below. Confirm the single leading cause
with one targeted number from step 3 before writing. Treat the customer's own conclusion
("it's just noise", "a measurement bug") as a hypothesis to disconfirm, not confirm —
pull the data independently rather than re-deriving their answer. Quantify a suspected cause
before asserting its impact (count the contaminating cohort, don't eyeball it). One trap in
particular: never run the SRM chi-square against an assumed even split — read the configured
rollout_percentagefirst, since an intended 34/33/33 reads as a ~2% SRM under an equal-split assumption. - Scope the fix to the experiment's state before recommending it. On a draft, config
changes are free — recommend freely. On a running experiment every change has a mid-run
tradeoff (changing the split is an anti-pattern — prefer reset or end+restart; see
configuring-experiment-rolloutandmanaging-experiment-lifecycle). On a stopped/shipped experiment the flag and results are the documented outcome, so recommend interpretation or a next experiment, not a mid-run edit. Don't propose reversing a state change unless the customer asks how to undo it. - Write the reply using references/customer-reply.md: cause → fix → the numbers that prove it, in the customer's UI language.
What ships with it
4 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 · 287 lines · 209 tokens per session scan A 7dfac1f23eae
debugging-experiments is a skill published in the GitHub repository PostHog/posthog-foss (715 stars, last pushed today), licensed MIT. It adds 209 tokens to every session and 4,934 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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