forecasting

forecasting is a skill for Claude Code, Codex from WingedGuardian/GENesis-AGI. It costs 19 tokens per session (2,162 once invoked), scanned A, original, MIT.

A forecasting guide for making specific predictions about uncertain future events and recording how confident you were.

In plain words
What is it for?
Use it to assess technology trends, project outcomes, market shifts, competitive moves, and risks by breaking large questions into smaller estimates and comparing them with historical patterns.
Why use it?
It replaces vague guesses with clear, checkable predictions and a way to review accuracy over time using Brier scores, a measure of forecast quality.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/wingedguardian/genesis-agi/forecasting
Any agent
npx skills add WingedGuardian/GENesis-AGI --skill forecasting
Clone the repo
git clone --depth 1 https://github.com/WingedGuardian/GENesis-AGI

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for forecasting

README.md
[![agentmods](https://agentmods.dev/badge/skills/wingedguardian/genesis-agi/forecasting.svg)](https://agentmods.dev/skills/wingedguardian/genesis-agi/forecasting)
Your own site
<a href="https://agentmods.dev/skills/wingedguardian/genesis-agi/forecasting"><img src="https://agentmods.dev/badge/skills/wingedguardian/genesis-agi/forecasting.svg" alt="Measured on agentmods" height="20"></a>
Per session 19 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,162 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00019 $0.02162
Opus 5 $0.00010 $0.01081
Sonnet 5 $0.00004 $0.00432
Haiku 4.5 $0.00002 $0.00216

Measured 6d ago against content hash 2a0b3dac317f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

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 6d 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.

src/genesis/skills/forecasting/SKILL.md · 225 lines

How it starts

The opening of the file, as written. The whole thing — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Forecasting

Purpose

Make specific, falsifiable predictions with calibrated confidence levels. Track accuracy over time using Brier scores. Apply superforecasting methodology (Tetlock/Good Judgment Project) to any domain — technology trends, project outcomes, market shifts, competitive moves, risk assessment.

When to Use

  • User asks for a prediction or forecast on any topic.
  • Strategic reflection identifies a decision that depends on uncertain futures.
  • Surplus compute is available and a prediction review is due.
  • A previously made prediction is approaching its resolution date.
  • Deep reflection surfaces a trend worth formally tracking.

Superforecasting Principles

  1. Triage — Focus on questions where effort improves accuracy. Ignore questions that are either trivially knowable or fundamentally unknowable.
  2. Fermi decomposition — Break big questions into smaller, estimable components. "Will X happen?" → "What's the base rate? What's different this time? What signals would I expect to see?"
  3. Balance inside and outside views — Start with the reference class (base rate from historical analogues), then adjust with specific evidence. Never skip the outside view.
  4. Update incrementally — Bayesian updating. New evidence shifts confidence by small amounts, not dramatic swings. Avoid overreaction.
  5. Calibration over precision — A well-calibrated 60% is better than an overconfident 90%. Your 70% predictions should come true ~70% of the time.
  6. Distinguish noise from signal — Most new information is noise. Ask: does this actually change the probability, or does it just feel important because it's recent?
  7. Consider contrarian views — Actively seek evidence against your current position. What must be true for the opposite outcome?
  8. Post-mortem every resolution — When a prediction resolves, analyze WHY you were right or wrong, not just whether. Update process, not just beliefs.
  9. Express uncertainty numerically — "Likely" is ambiguous. 70% is not. Use the probability scale below.
  10. Separate confidence from conviction — High confidence (90%) means high probability. Strong conviction means you've thought deeply. You can have low confidence with strong conviction (you've analyzed it thoroughly and it's genuinely uncertain).

Read the full file on GitHub · 225 lines

Changes

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.

  1. 6d ago First seen · 225 lines · 19 tokens per session scan A 2a0b3dac317f

Subscribe to this mod's changes

forecasting is a skill published in the GitHub repository WingedGuardian/GENesis-AGI (93 stars, last pushed today), licensed MIT. It adds 19 tokens to every session and 2,162 once invoked, about $0.0001 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 2026-08-30.

Related

Other skills, from other repositories

search

Unified academic paper search, citation chains, paper download (arXiv LaTeX/PDF, Sci-Hub), figure extraction from papers, LaTeX source reading, BibTeX fetching, web search, and browser automation for Cloudflare-protected sites (PRL, Science, Nature, Google Scholar).

Muuuun/luxas · 0 tokens

qec-construct

Verifier-in-the-loop CONSTRUCTION of quantum error-correcting codes with transversal non-Clifford gates (CCZ/T). Applies whenever the project goal is a new or better code/construction — INCLUDING search-phrased goals ("find codes beating X"), where the construct-loop (propose algebraic rule → qverify → debug) is the…

Muuuun/luxas · 148 tokens

figure

Hybrid figure pipeline (Nano Banana raster + rembg background removal + TikZ vector assembly). Includes a TikZ template library covering quantum circuits (quantikz), Feynman diagrams (tikz-feynman), circuits (circuitikz), molecules (chemfig), 2D/3D plots (pgfplots), energy-level diagrams, phase-space trajectories…

Muuuun/luxas · 104 tokens

memory

Cross-project research memory. Deep-dive past projects' notes, record corrections, and save cross-project insights across all Luxas research projects.

Muuuun/luxas · 30 tokens

compute-methods

Environment-verified friction sheets for field-standard computational tools (Rydberg pair interactions, QEC circuits, qLDPC decoding, code distance, atom dynamics, optics, quantum chemistry). Each sheet lists the tools the field actually uses, the first-use frictions that make agents wrongly abandon them, and one-line…

Muuuun/luxas · 113 tokens

context-window-management

Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot.

LiHongwei-cn/lihongwei-cn · 23 tokens