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-endpoint-performancegit 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-endpoint-performance)<a href="https://agentmods.dev/skills/posthog/posthog-foss/diagnosing-endpoint-performance"><img src="https://agentmods.dev/badge/skills/posthog/posthog-foss/diagnosing-endpoint-performance/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-endpoint-performance"><img src="https://agentmods.dev/badge/skills/posthog/posthog-foss/diagnosing-endpoint-performance.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 28 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 | $0.00092 | $0.02913 |
| Opus 5 | $0.00046 | $0.01456 |
| Sonnet 5 | $0.00018 | $0.00583 |
| Haiku 4.5 | $0.00009 | $0.00291 |
Grade A, and why
diagnosing-endpoint-performance 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 7d 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-endpoint-performance — 89% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Diagnosing endpoint performance
This skill walks through a specific endpoint that is slow, expensive, or unreliable, and produces
a concrete recommendation. It is the deep-dive counterpart to auditing-endpoints (which finds
candidates).
When to use this skill
- "This endpoint is slow / timing out"
- "Why is my endpoint hitting the cost cap?"
- "Should I materialise X?"
- An endpoint surfaced from
auditing-endpointsas a failing materialisation or expensive caller - The user has a specific endpoint in mind and wants advice
If the question is project-wide ("what should I clean up?"), use auditing-endpoints first.
Available tools
| Tool | Purpose |
|---|---|
endpoint-get |
Full endpoint config: query, current version, data_freshness_seconds, materialisation status |
endpoint-versions |
History of every version (query + materialisation state); which version is current |
endpoint-materialization-status |
Whether materialisation is eligible, current state, last run, last error |
endpoints-materialization-preview |
What the materialised query would look like, plus the rejection reason if ineligible |
endpoints-last-execution-times |
When was it last called (endpoint-level sanity-check that it is in active use) |
execute-sql |
Query query_log for endpoint-level call frequency and per-call duration/bytes |
The two AI rewrite tools below are gated behind the endpoints-ai-materialization-fix
feature. When that feature is off, they do not appear in your tool catalog. Step 3 works
without them — treat them as an accelerator, not a requirement.
| Tool (feature-gated) | Purpose |
|---|---|
endpoint-materialization-suggestion |
Server-side AI rewrite of an ineligible SQL query, validated against the live checks |
endpoint-materialization-conditions |
Source code of the live eligibility checks + the rewrite contract, for DIY rewriting |
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.
- 7d ago First seen · 216 lines · 92 tokens per session scan A bac7ba1c2dd1
diagnosing-endpoint-performance is a skill published in the GitHub repository PostHog/posthog-foss (715 stars, last pushed today), licensed MIT. It adds 92 tokens to every session and 2,913 once invoked, about $0.0005 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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