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 investigating-error-issuegit 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/investigating-error-issue)<a href="https://agentmods.dev/skills/posthog/posthog-foss/investigating-error-issue"><img src="https://agentmods.dev/badge/skills/posthog/posthog-foss/investigating-error-issue/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/investigating-error-issue"><img src="https://agentmods.dev/badge/skills/posthog/posthog-foss/investigating-error-issue.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 22 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 | $0.00114 | $0.04032 |
| Opus 5 | $0.00057 | $0.02016 |
| Sonnet 5 | $0.00023 | $0.00806 |
| Haiku 4.5 | $0.00011 | $0.00403 |
Grade A, and why
investigating-error-issue 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:
- investigating-error-issue — 86% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 375 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Investigating an error tracking issue
When a user asks "what's going on with this error?" or pastes an issue URL, gather the context they would otherwise have to assemble manually: who is hitting it, what changed, where it happens, and whether a replay shows the cause.
Available tools
| Tool | Purpose |
|---|---|
posthog:query-error-tracking-issue |
Compact issue details (status, assignee, top frame, release, aggregates) |
posthog:query-error-tracking-issue-events |
Sampled $exception events with stack, URL, browser, $session_id |
posthog:execute-sql |
Breakdowns, release / flag correlations, surrounding events + console logs around the error |
posthog:query-logs |
OTEL log entries around the error timestamp for server-side issues |
posthog:query-session-recordings-list |
Linked replays (delegate ranking to finding-replay-for-issue) |
posthog:read-data-schema |
Confirm property keys before filtering on them |
Workflow
Step 1 — Establish the issue baseline
Fetch the issue record with its compact aggregates and a sparkline:
posthog:query-error-tracking-issue
{
"issueId": "<issue_id>",
"dateRange": { "date_from": "-30d" },
"includeSparkline": true,
"volumeResolution": 12
}
Capture: name, description, status, first_seen, last_seen, assignee,
total occurrences / users / sessions, top in-app frame, latest release
metadata, and the volume buckets.
The sparkline tells you the shape — flat, spike, ramp, or recurring — and that
shape drives the rest of the investigation. If the user only asked a status
question, skip includeSparkline to save tokens.
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 · 375 lines · 114 tokens per session scan A d821dfcbae26
investigating-error-issue is a skill published in the GitHub repository PostHog/posthog-foss (715 stars, last pushed today), licensed MIT. It adds 114 tokens to every session and 4,032 once invoked, about $0.0006 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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