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 agentmods add rules/xclaw-bot/benchmark-task-authoring/10-hardness-gategit clone --depth 1 https://github.com/Xclaw-bot/benchmark-task-authoringWhat 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 | $0.01669 | $0.01669 |
| Opus 5 | $0.00834 | $0.00834 |
| Sonnet 5 | $0.00334 | $0.00334 |
| Haiku 4.5 | $0.00167 | $0.00167 |
Grade A, and why
10-hardness-gate 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 2d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
description: The hardness law, its pre-build check and the strong-probe protocol. Load when designing a task, judging whether a design is hard enough, or diagnosing a pass@ result that came back too easy. alwaysApply: false
The hardness gate
Canonical source: ../hardness-laws.md. Derived over seven printed-optimum
tasks (four measured pass@5 clears, three withdrawn on probe). Read its "Honest
limits" section before treating any of this as certainty — at n=5 against a 60%
bar, a genuinely 70%-fail design still misses the gate ~16% of the time.
The law
Ship only if, with every stated rule held correctly, a competent solver still cannot cheaply construct the optimum. That requires ≥2 load-bearing beliefs that are misreadable, narrowing, public-blind, unrepairable by restart, and silent under the printed optimum, plus one stated resource constraint that makes the bulk/uniform architecture illegal.
Whether a belief is stated in one sentence or entailed across sections is irrelevant — that axis is dead. State everything. A stated rule is worth exactly what its measured wrong-belief rate says.
Why those conditions and not others
The agent's cheapest winning strategy is transcribe → simulate → over-enumerate → check against N. Each condition closes one leg of it:
- A transcribed simulator enforces per-step legality for free. So any fact entailed by step legality is worth zero — the simulator rejects violations without ever deriving the fact. Difficulty can only live in facts needed to emit a candidate: whole-plan or terminal properties, or the identity/layout of the objects acted on. Constructive, not checkable.
- A printed optimum is a free belief oracle on sealed data.
cost == Nruns inside the graded program with no feedback channel. A wrong belief that overshoots, undershoots, wedges, crashes or exhausts a resource is an alarm, and one retry repairs it. Only beliefs whose wrong plans self-reportcost == N, all checks passsurvive. - Restart repairs every over-permissive slip. It cannot cross an action-space boundary. Measured: one slot's over-permissive seal-blind was restarted around 12/12; its narrowing freeze-blind killed 9/12 and was unrepairable.
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.
- 2d ago First seen · 124 lines · 1,669 tokens per session scan A 9c2479953cc1
10-hardness-gate is a cursor rule published in the GitHub repository Xclaw-bot/benchmark-task-authoring (2 stars, last pushed 17d ago), licensed MIT. It adds 1,669 tokens to every session, about $0.0083 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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