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 formatting-insight-axesgit 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/formatting-insight-axes)<a href="https://agentmods.dev/skills/posthog/posthog-foss/formatting-insight-axes"><img src="https://agentmods.dev/badge/skills/posthog/posthog-foss/formatting-insight-axes/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/formatting-insight-axes"><img src="https://agentmods.dev/badge/skills/posthog/posthog-foss/formatting-insight-axes.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 | $0.00177 | $0.02210 |
| Opus 5 | $0.00088 | $0.01105 |
| Sonnet 5 | $0.00035 | $0.00442 |
| Haiku 4.5 | $0.00018 | $0.00221 |
Grade A, and why
formatting-insight-axes 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.
How it starts
The opening of the file, as written. The whole thing — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Formatting insight axes
PostHog renders insights with a built-in axis formatter. Use it instead of contorting the query or a literal prefix/postfix to fake units.
The two insight kinds configure it in different places:
- TrendsQuery —
trendsFilter.aggregationAxisFormat(this page, below) - SQL insights (
DataVisualizationNode) — per-columnsettings.formatting(see SQL insights)
The anti-pattern
If you are reaching for any of these, stop and pick a format below first:
formula: "A / 60"withaggregationAxisPostfix: " mins"— manual seconds -> minutesformula: "A / 1000"withaggregationAxisPostfix: " s"— manual ms -> secondsformula: "A * 100"withaggregationAxisPostfix: "%"— manual ratio -> percentaggregationAxisPostfix: "ms"/"s"/"min"/"hr"on raw values
These freeze the unit at one scale. The built-in formatter picks a friendly unit per value (1.5s, 2m 12s, 1h 4m) and keeps the underlying series numerically correct for further math, breakdowns, and alerts.
Available formats
Set trendsFilter.aggregationAxisFormat on the TrendsQuery:
| Value | Use when the series is... | Renders as |
|---|---|---|
numeric (default) |
a plain count | 1,234 |
duration |
seconds (any scale) | 45s, 2m 12s, 1h 4m |
duration_ms |
milliseconds | 850ms, 1.5s, 1m 4s |
percentage |
already 0-100 | 47.3% |
percentage_scaled |
a ratio 0-1 | 47.3% |
currency |
money in the project's base currency | $1,234.56 (or local code) |
short |
large counts you want compacted | 1.2K, 3.4M |
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 · 227 lines · 177 tokens per session scan A 3d942c2d85a9
formatting-insight-axes is a skill published in the GitHub repository PostHog/posthog-foss (715 stars, last pushed today), licensed MIT. It adds 177 tokens to every session and 2,210 once invoked, about $0.0009 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.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
neuron-evaluation-engineer
Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.