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 debabsah/analytics-office --skill audit-my-forecastgit clone --depth 1 https://github.com/debabsah/analytics-officeWrote 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/debabsah/analytics-office/audit-my-forecast)<a href="https://agentmods.dev/skills/debabsah/analytics-office/audit-my-forecast"><img src="https://agentmods.dev/badge/skills/debabsah/analytics-office/audit-my-forecast/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/debabsah/analytics-office/audit-my-forecast"><img src="https://agentmods.dev/badge/skills/debabsah/analytics-office/audit-my-forecast.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00226 | $0.02491 |
| Opus 5 | $0.00113 | $0.01246 |
| Sonnet 5 | $0.00045 | $0.00498 |
| Haiku 4.5 | $0.00023 | $0.00249 |
Grade A, and why
audit-my-forecast 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 10d 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
audit-my-forecast
The colleague who checks whether the forecast will actually hold before you plan against it: computes the coverage and skill tests you'd otherwise eyeball, names the leakage and backtest traps that make a backtest lie, and never blesses a projection it didn't check.
When to use
Fire when a forecast / projection / time-series prediction is heading into a plan — even under a consumption ask ("write up the forecast", "should we plan against this"). Switch into audit-mode and validate before the plan rides on it.
Do NOT fire for a controlled A/B / causal-effect result (audit-my-experiment), to diagnose why ONE production number moved (triage-my-number), to review the SQL/feature-build code as text (review-my-query), to pin the target metric's definition (kpi-contract), or to write up an already-validated forecast (brief-my-findings).
This vs. audit-my-experiment: experiment audits a controlled causal test ("is this lift real" — SRM, peeking, power); this audits an extrapolation into the future ("will this projection hold" — leakage, backtest, intervals, drift). Same shape (audit → gate → *-audit.md), different question.
The trap this exists to beat
A capable model reads a forecast with a great-looking backtest and writes "the model is accurate, plan against it" — and the backtest is lying. Its instinct is to trust the reported error, eyeball the fan chart, and never ask how the split was made. The four silent failures it skips: leakage (a feature that wasn't knowable at forecast time, or scaling computed over the test window, makes the backtest far too good); backtest design (a random K-fold shuffles time and destroys causal order; no naive baseline means "low MAPE" is meaningless); interval honesty (a point forecast shipped as certain, or a "95%" band that actually covers 60%); drift (the model was accurate at fit and has silently decayed since). All four are invisible in the reported accuracy; only structural inspection plus computing coverage/skill/trend on the actuals-vs-predicted catches them. This skill switches into audit-mode and does exactly that.
What ships with it
3 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.
- 10d ago First seen · 74 lines · 0 tokens per session scan A 05e007fad740
audit-my-forecast is a skill published in the GitHub repository debabsah/analytics-office (9 stars, last pushed 3mo ago), licensed MIT. It adds 226 tokens to every session and 2,491 once invoked, about $0.0011 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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