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 ralfyishere/rules-with-receipts --skill foresightgit clone --depth 1 https://github.com/ralfyishere/rules-with-receiptsWrote 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/ralfyishere/rules-with-receipts/foresight)<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>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 | $0.00136 | $0.01518 |
| Opus 5 | $0.00068 | $0.00759 |
| Sonnet 5 | $0.00027 | $0.00304 |
| Haiku 4.5 | $0.00014 | $0.00152 |
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.
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) orplan-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
- 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?
- 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). - 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).
- Include positive foresight. Opportunities, compounding assets, and options-worth-buying — not only failures. Foresight is not pessimism with dates.
- 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.
- Feed the result back. Resolved predictions are evidence: wrong ones go to
self-improvement-loop(what did the projection miss?); patterns across resolutions becomeextract-approachnotes; systematically over- or under-confident horizons recalibrate the next round's credences.
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 · 111 lines · 0 tokens per session scan A 675c3ef7c955
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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