Foresight

Foresight is a skill for Claude Code from ralfyishere/rules-with-receipts. It costs 136 tokens per session (1,518 once invoked), scanned A, original, MIT.

A planning aid that records dated predictions about what may break, become necessary, or prove worthwhile as a project progresses. It later checks those predictions against what actually happened.

In plain words
What is it for?
Use it when choosing an architecture, roadmap, growth plan, infrastructure, pricing, market direction, integration, data model, public API, contract, or content strategy.
Why use it?
It makes future assumptions visible and testable instead of leaving them as vague expectations. This can expose risks and useful preparations before they become urgent.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

Good fit Use it when choosing an architecture, roadmap, growth plan, infrastructure, pricing, market direction, integration, data model, public API, contract, or content strategy.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ralfyishere/rules-with-receipts/foresight
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.

Any agent
npx skills add ralfyishere/rules-with-receipts --skill foresight
Clone the repo
git clone --depth 1 https://github.com/ralfyishere/rules-with-receipts

Made for: Claude Code.

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 Foresight

README.md
[![agentmods](https://agentmods.dev/badge/skills/ralfyishere/rules-with-receipts/foresight.svg)](https://agentmods.dev/skills/ralfyishere/rules-with-receipts/foresight)
Your own site
<a href="https://agentmods.dev/skills/ralfyishere/rules-with-receipts/foresight"><img src="https://agentmods.dev/badge/skills/ralfyishere/rules-with-receipts/foresight.svg" alt="Measured on agentmods" height="20"></a>
Per session 136 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,518 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 $0.00136 $0.01518
Opus 5 $0.00068 $0.00759
Sonnet 5 $0.00027 $0.00304
Haiku 4.5 $0.00014 $0.00152

Measured 8d ago against content hash 675c3ef7c955, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

Foresight 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.

.claude/skills/foresight/SKILL.md · 111 lines

How it starts

The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Foresight

Purpose

Plans reason about the present; failures and windfalls arrive from the future. The pack already attacks present-tense blind spots (failure-mode-awareness enumerates what could go wrong with the design as it stands) — but nothing owned the trajectory: by step 10 X becomes the bottleneck, by step 20 Y is cheap and we will wish we had started it now. Worse, anticipation kept in the head is unfalsifiable: hindsight rewrites "I knew it" over every outcome. Pre-registration fixes both — a prediction with a date, a credence, and an observable resolution is an asset: it can steer today's decision, and when it resolves it measures how far ahead we can actually see. Origin (operator, 2026-07-11): "I kept having to give you the ideas — you should see what's coming 5, 10, 20 steps from now."

When to use this skill

  • Commit points: a plan approved, an architecture chosen, a build started, a direction picked over alternatives.
  • Scaling-shaped decisions: growth targets, hiring, infra, pricing, market entry.
  • Shipping anything whose failure modes surface on a delay (integrations, data models, public APIs, contracts, content).
  • Reviewing a plan whose risks are all present-tense.

When NOT to use

  • Trivial or fully reversible work — a projection pass on a one-line fix is theater.
  • As a substitute for failure-mode-awareness (present-design risks) or plan-gate (the plan itself) — this runs WITH them, on the time axis.
  • To manufacture certainty: a 0.6 prediction is not a roadmap commitment, and foresight entries are not promises.

The procedure

  1. Walk the trajectory, not the snapshot. At the commit point, project along the axis that matters (steps, scale, time): what does this look like at 5, 10, 20 steps ahead? At each horizon ask four questions: what breaks? what becomes necessary? what becomes cheap or possible? what will we wish we had started now?
  2. Pre-register 3–7 predictions. Each carries: the claim, a credence (0–1), a resolve-by date, and the OBSERVABLE that resolves it. Vague predictions ("things will get complex") are inadmissible — if no observation could score it, rewrite it or drop it. Log them where the project's hypotheses live (the hypothesis queue, class foresight, or the plan doc).
  3. Keep the horizons distinct. Near entries (≈5 steps) protect the current build; far entries (≈20) are allowed to be weird — they are where the value is, because a high-credence far prediction with a cheap present-day hedge is a TASK, not a note (start the data collection now; reserve the name now; design the schema for the split now).
  4. Include positive foresight. Opportunities, compounding assets, and options-worth-buying — not only failures. Foresight is not pessimism with dates.
  5. Resolve on arrival. When a resolve-by date passes, score the prediction (right / wrong / unresolvable-as-written) BEFORE writing new ones. Unresolved predictions past their date are debt; calibration only accrues from resolved ones.
  6. Feed the result back. Resolved predictions are evidence: wrong ones go to self-improvement-loop (what did the projection miss?); patterns across resolutions become extract-approach notes; systematically over- or under-confident horizons recalibrate the next round's credences.

Read the full file on GitHub · 111 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. 8d ago First seen · 111 lines · 0 tokens per session scan A 675c3ef7c955

Subscribe to this mod's changes

Foresight is a skill published in the GitHub repository ralfyishere/rules-with-receipts (2 stars, last pushed 1mo ago), licensed MIT. It adds 136 tokens to every session and 1,518 once invoked, about $0.0007 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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