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 discovery-loopgit 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/discovery-loop)<a href="https://agentmods.dev/skills/ralfyishere/rules-with-receipts/discovery-loop"><img src="https://agentmods.dev/badge/skills/ralfyishere/rules-with-receipts/discovery-loop.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 | $0.00142 | $0.01437 |
| Opus 5 | $0.00071 | $0.00718 |
| Sonnet 5 | $0.00028 | $0.00287 |
| Haiku 4.5 | $0.00014 | $0.00144 |
Grade A, and why
Discovery Loop 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 7d 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Discovery Loop
Purpose
Verification kills bad ideas; nothing in a kill-oriented process creates new ones. A finished investigation with an honest verdict ("no edge found", "not reproducible", "root cause is X") quietly becomes a wall: future sessions treat the kill-list as the space. The two documented failures this skill exists for: (1) a universal negative read off a finite list — "we tested 9 strategies, all dead" became "no edge exists," until an outsider's success disproved it in one evening; (2) belief moving on vibes — credence climbing through a streak of confirmations with no attack, or a "winner" anointed from a short window that a full-history audit later showed was net-negative. The loop makes generation a standing duty and belief an explicit, attackable number.
When to use this skill
- Standing investigations that survive across sessions: market/edge hunts, recurring incidents, performance regressions, growth experiments, research programs.
- A verdict/kill-list exists and is about to be cited as the reason not to look.
- External evidence contradicts your closed conclusion (a competitor does the "impossible" thing) — that is a lead, not an annoyance.
- You notice every recent hypothesis came from your own head, none from observation.
When NOT to use
- One-shot tasks with a definite end (a bug fixed and verified; a question answered).
- Domains where more hypotheses have no value (the decision is made, the system is being decommissioned).
- As a license to re-litigate a kill WITHOUT new evidence — re-opening requires a new observation, not boredom with the verdict.
The loop (every session the investigation is touched)
- Observe before theorizing. Spend the first cycle on what the domain is DOING now — who is succeeding, what changed, what the live data shows — via the cheapest real-observation channel (public fills, logs, dashboards, competitor output). Winners' observed behavior outranks your priors as a hypothesis source.
- Generate ≥1 NEW hypothesis, dated, even half-baked. From the observation, not from re-reading old brainstorms. Log it in the hypothesis ledger with a status (OPEN).
- Keep a belief-state, not a feeling. Each hypothesis carries: credence (your probability it's real, 0–1), the evidence chain (each test, dated, with the credence delta it caused), and its cheapest next falsifying test. Update the number when evidence lands — both directions.
- Attack every streak. Track consecutive credence-raising events with no kill-attempt between them. At 3, stop confirming and run the strongest attack you can design (decay slice, capacity, full-history depth, discriminating test). A streak is a signal to attack, never to size up.
- Depth before anointing. Any "winner" found in a short observation window (a performer, a config, a tactic) gets a full-history audit before it becomes evidence — short windows manufacture winners (verified: a candidate that looked strong over ~8 samples reversed hard at depth; a "most consistent" performer was net-negative over its full history).
- Kill or promote, in writing. Every hypothesis resolves to a dated verdict with receipts; killed ideas stay on the ledger (they block re-litigation without new evidence). Scope every verdict honestly: "no edge in what we generated" — never "the space is empty."
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.
- 7d ago First seen · 104 lines · 0 tokens per session scan A c4b18af9d87b
Discovery Loop is a skill published in the GitHub repository ralfyishere/rules-with-receipts (2 stars, last pushed 1mo ago), licensed MIT. It adds 142 tokens to every session and 1,437 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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