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 ericrisco/rsc-harness --skill forecastinggit clone --depth 1 https://github.com/ericrisco/rsc-harnessWrote 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/ericrisco/rsc-harness/forecasting)<a href="https://agentmods.dev/skills/ericrisco/rsc-harness/forecasting"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/forecasting/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/ericrisco/rsc-harness/forecasting"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/forecasting.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 Excessive Agency · line 131 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00080 | $0.02751 |
| Opus 5 | $0.00040 | $0.01375 |
| Sonnet 5 | $0.00016 | $0.00550 |
| Haiku 4.5 | $0.00008 | $0.00275 |
Grade A, and why
forecasting 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 8d 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Forecasting
A forecast that cannot beat "repeat last period" is noise. Baseline first, fancy second. The naive forecast is free, instant, and the bar every model must clear — if your AutoARIMA loses to last-quarter-repeated, ship the repeat and say so.
You are not done when a model produces a number. You are done when you can defend the number: which method, why that method for this data, how it scored against the naive baseline in a backtest, and the interval around the point. A point estimate with no error band is a guess wearing a lab coat.
The deliverable contract
Every forecast you ship is a reproducible artifact, not a number pasted in chat:
- A script that reads the history and regenerates the forecast (no manual steps).
- A CSV/Parquet with columns
ds, forecast, lo, hi— timestamp, point, interval bounds. - A one-paragraph accuracy readout: WAPE + bias from a rolling-origin backtest, and MASE vs the naive baseline (MASE < 1.0 = you beat naive; ≥ 1.0 = ship the naive forecast instead).
If you cannot produce all three, you have not forecast — you have guessed. scripts/verify.sh checks the artifact has these columns, the right row count, and an accuracy line.
The loop
Run these in order. Skipping step 3 is the most common failure.
- Frame it. Pin down the horizon
h(how many periods forward), the granularity (daily / weekly / monthly), and exactly what is being predicted (units? revenue? per-SKU or aggregate?). Forecast at the level you will act on — if you reorder per SKU, forecast per SKU, then sanity-check against the aggregate. - Establish the series. Regular timestamps, one row per period, gaps filled explicitly (a missing month is not zero unless it truly is). Flag promotions, stockouts, and outliers — they distort the signal. If the input is dirty (dupes, missing rows, mixed units), stop and hand off to
data-cleaningbefore modeling. Garbage history, garbage forecast. - Build the naive + seasonal-naive baseline. This is the bar. Naive = repeat last value. Seasonal-naive = repeat the value from one season ago (e.g. last December for this December). Compute its backtest error now — every fancier method must beat it or lose.
- Pick the method by data shape (table below). Do not reach for ARIMA on instinct.
- Backtest with rolling-origin cross-validation. Never a single holdout. Compute WAPE + bias + MASE vs the naive baseline across multiple cutoffs.
- Report. Point + interval, the one-line method rationale, the accuracy readout. Then hand off downstream (
inventory,financial-model).
What ships with it
5 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.
- 8d ago First seen · 140 lines · 80 tokens per session scan A 9feab604d0eb
forecasting is a skill published in the GitHub repository ericrisco/rsc-harness (82 stars, last pushed yesterday), licensed MIT. It adds 80 tokens to every session and 2,751 once invoked, about $0.0004 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
merge-aliases
Folds two surface names for the same backend system into one canonical entity, keeping every original mention individually retrievable, and refuses to merge pairs that only share spelling.
anchor-and-lock
Consults a check that sits outside the loop system before finalizing any decision the frozen facts bear on, and refuses every attempt by a loop to rewrite a node marked frozen, regardless of how convergent the loop's own reasoning looks.
query-graph
Loads schema.sql into a local SQLite file, then answers availability and provenance questions against the nodes/edges tables with real SQL instead of re-reading source material.
build-subgraph
Traverses a full graph and returns a bounded subgraph around one target node -- its depth-bounded dependencies plus any disputed claims attached to it -- while proving everything else was left out.
arbitrate-collision
Detects when two loops have proposed conflicting writes to the same node and field at close to the same time, applies a stated priority rule to accept exactly one, and records the rejected write with the reason it lost.
audit-sweep
Reviews a full set of the main loop's outputs together, from a wider vantage than any single one of them, looking specifically for cross-item patterns that a one-item-at-a-time loop is structurally unable to notice.