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/20-design-doctrinegit clone --depth 1 https://github.com/Xclaw-bot/benchmark-task-authoringWrote 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/rules/xclaw-bot/benchmark-task-authoring/20-design-doctrine)<a href="https://agentmods.dev/rules/xclaw-bot/benchmark-task-authoring/20-design-doctrine"><img src="https://agentmods.dev/badge/rules/xclaw-bot/benchmark-task-authoring/20-design-doctrine.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 | $0.01151 | $0.01151 |
| Opus 5 | $0.00575 | $0.00575 |
| Sonnet 5 | $0.00230 | $0.00230 |
| Haiku 4.5 | $0.00115 | $0.00115 |
Grade A, and why
20-design-doctrine 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 3d 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
description: Measured dead ends and what the accepted-task corpus looks like. Load before proposing or choosing a task shape. alwaysApply: false
Design doctrine — what is measured, not theorised
Source: the FIELD NOTES sections of ../hardness-laws.md. Every claim here came
from a build that was carried to measurement.
The decisive finding
The benchmark model does not depend on supplied samples to verify its work. It writes a second independent implementation and cross-checks against it. Observed directly in trial analyses: "it built two independent solvers … both agreed on all 8 sample positions and all 32 graded positions before the final artifact was written." It also derives correct semantics from a spec and self-corrects the exact bug a corpus was built around.
The structural consequence:
Any task specified completely enough to be fair is self-verifiable. Corpus and sample selection cannot create a gap, because the gap they exploit is "the agent stops while still wrong" — and this model does not stop.
Design accordingly. A difficulty story that depends on the agent failing to check its own work is already refuted.
Dead ends — do not retry these
| Trap | Measurement |
|---|---|
| Latent crux via sample selection (samples blind to the wrong method) | Works on paper, irrelevant in practice — the model verifies independently of the samples. |
| Making the deciding rule rare | Rare hides it from the evidence and from the answer; both move together. Measured twice: rarer scoring clause moved log-visibility 76%→93% while answer-divergence fell 17%→3%. |
| More interacting rules | A 10-clause engine measured as a 2-clause one: 6 of 8 rule-omission variants diverged on 0% of games, because the clauses never fired. |
| Bigger state spaces | 11× growth (6k → 67k states) changed nothing; still solved. |
| Value-identical error modes | Provably blind value-checking still lost 2/2 — the model wrote a second solver. |
| Format reproduction from a corpus | The corpus gives complete feedback: encode, diff bytes, localise, fix, repeat. Bit-packing makes it worse — any width or ordering error shifts every later bit, so all four wrong encoders were 100% visible. |
| Scale as a validation barrier | Dead, and provably so from our own records: brute force was already infeasible at the shipped size and the agent still solved it 4/5 — because its second implementation was never brute force, it was a second retrograde solver. Two independent correct solvers agree at any size. |
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.
- 3d ago First seen · 86 lines · 1,151 tokens per session scan A b88186dab45e
20-design-doctrine is a cursor rule published in the GitHub repository Xclaw-bot/benchmark-task-authoring (2 stars, last pushed 19d ago), licensed MIT. It adds 1,151 tokens to every session, about $0.0058 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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