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 petar-djukic/writing-skills --skill bake-offgit clone --depth 1 https://github.com/petar-djukic/writing-skillsWrote 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/petar-djukic/writing-skills/bake-off)<a href="https://agentmods.dev/skills/petar-djukic/writing-skills/bake-off"><img src="https://agentmods.dev/badge/skills/petar-djukic/writing-skills/bake-off/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/skills/petar-djukic/writing-skills/bake-off"><img src="https://agentmods.dev/badge/skills/petar-djukic/writing-skills/bake-off.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 | $0.00000 | $0.01284 |
| Opus 5 | $0.00000 | $0.00642 |
| Sonnet 5 | $0.00000 | $0.00257 |
| Haiku 4.5 | $0.00000 | $0.00128 |
Grade B, and why
bake-off scanned grade B with 1 finding 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 6d 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.
Asks the agent to reveal its instructionsmediumSystem prompt leakage
Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.
models expose YOUR prompt bugs — GH-171's discovery path took How it starts
The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: bake-off description: >- Multi-payload model comparison as a first-class skill: arms through the real pipeline over real published material, >= 2 payloads before any verdict, seed-pinned replays, one Pangram framing per table, scores read together with per-model error forensics, dated results re-run on pipeline change. Owns the harness (sweep / replicate / bakeoff arms, any model the shared transport routes) and the report template. Triggers: bake-off, bakeoff, model comparison, A/B the models, which model should the stage use, re-pin the default, sweep the arms. argument-hint: 'bakeoff <draft.md> --out-dir (see scripts/bakeoff.py)'
Bake-off (multi-payload model comparison)
The methodology ran four times in one arc (GH-160, GH-166, GH-168, GH-170) and re-pinned two pipeline defaults, but it lived as a match-voice script plus discipline carried in issue prose — a fifth run meant re-deriving the discipline again. This skill owns both: the harness and the contract.
The method contract
Every rule below has a run that broke without it.
- >= 2 payloads, real published material, never toy prompts. Single-draft verdicts reversed twice (GH-160 vs GH-166/168). A verdict from one payload is a note, not a result.
- Arms run through the REAL pipeline, not bare prompts. The prompt scaffold, the gate, and the retry loop are part of what is being measured; a bare-prompt arm measures a pipeline nobody runs.
- Read scores and per-model error forensics together. Obedient models expose YOUR prompt bugs — GH-171's discovery path took citation survival from 46% to 88% by reading the error log of the model that followed instructions too well.
- Seed-pin every arm (
COHERE_SEED, verified bit-identical at temp 0.9) so any arm replays exactly. Do NOT best-of-N over seeds: the null measured 5 seeds within 0.003 Pangram — the detector reads register, not phrasing. - One Pangram framing per table. Slice / whole / prose-only scores of the same article measured 0.225 / 0.913 / 1.000 — a table mixing framings compares nothing. Uploads run under the consent rule (per-document, or the operator's standing grant, GH-210).
- Date every verdict and re-run on pipeline change. Rankings are model x pipeline; the GH-194 re-pin overturned a July ranking without any model changing.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago First seen · 107 lines · 0 tokens per session scan B 7b0d41f8cb87
bake-off is a skill published in the GitHub repository petar-djukic/writing-skills (4 stars, last pushed 7d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,284 tokens. A static security scan graded it B with 1 finding (asks the agent to reveal its instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
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