Borrowing it
Nothing to install: this file belongs to Xclaw-bot/benchmark-task-authoring. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Xclaw-bot/benchmark-task-authoring/main/AGENTS.mdgit 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/instructions/xclaw-bot/benchmark-task-authoring/agents-md)<a href="https://agentmods.dev/instructions/xclaw-bot/benchmark-task-authoring/agents-md"><img src="https://agentmods.dev/badge/instructions/xclaw-bot/benchmark-task-authoring/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/xclaw-bot/benchmark-task-authoring/agents-md"><img src="https://agentmods.dev/badge/instructions/xclaw-bot/benchmark-task-authoring/agents-md.svg" alt="Reviewed on agentmods" width="80" 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 | $0.06621 | $0.06621 |
| Opus 5.5 | $0.02648 | $0.02648 |
| Sonnet 5.5 | $0.01324 | $0.01324 |
| Haiku 4.5 | $0.00662 | $0.00662 |
Grade A, and why
benchmark-task-authoring AGENTS.md 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 2mo 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 — 400 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Authoring hard agent-benchmark tasks
Which file am I? The
AGENTS.mdentry point, read automatically by OpenAI Codex, Google Antigravity, and other AGENTS.md-aware agents. Cursor reads the same knowledge from.cursor/rules/; Claude Code fromSKILL.md. All of them are generated from one source — editSKILL.mdorreferences/, then runpython scripts/port.py.
Everything below applies whenever you are designing, building, critiquing or debugging a Terminal-Bench 2 / Harbor benchmark task.
This skill packages what was measured across 30+ Terminal-Bench 2 task slots — the ones that cleared a difficulty gate, and the many that died first. It exists because the intuition almost everyone brings to "write a hard task" is wrong in a specific, repeatable way, and the correction is cheap once you know it.
New here? Fifteen minutes, in this order
1 · Read "The one law" and the kill-list below (5 min). Do not skip to the reference files. If you internalise only one thing, make it the pre-build test: what must the agent build that the verifier could not hand it by simulating?
2 · Set up retrieval (1 min). The field manual is ~35k tokens and will not fit in one read call. Index it once and query it instead — this is the difference between a design question costing ~1k tokens and ~35k:
python scripts/dr.py index --no-embed
python scripts/dr.py ask "what trips ava_review verifier_coverage" --fast
3 · Put a board up (1 min). Before you write anything, see where your slots actually stand — and specifically which review stage each one is sitting at:
python scripts/taskdesk.py --org <your-task-org>
4 · Then, when you have a slot to work: references/ci-stages.md before your first push
(it is the difference between one three-hour CI run and three of them), and the phase table
below for everything else.
The two mistakes that cost the most, stated once so you can avoid both: designing a task where the agent checks a property instead of constructing an object, and treating CI as a test loop instead of a confirmation step.
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.
- 2mo ago First seen · 400 lines · 6,621 tokens per session scan A d0eafb62816f
benchmark-task-authoring AGENTS.md is an instructions file published in the GitHub repository Xclaw-bot/benchmark-task-authoring (2 stars, last pushed 2mo ago), licensed MIT. It adds 6,621 tokens to every session, about $0.0265 per session on Opus 5.5. 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.
Other instructions, from other repositories
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