Borrowing it
Nothing to install: this file belongs to bonginkan/fairy_tale. 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/bonginkan/fairy_tale/main/.agents/skills/fairy-tale-benchmark-feedback/SKILL.mdgit clone --depth 1 https://github.com/bonginkan/fairy_taleWrote 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/bonginkan/fairy_tale/fairy-tale-benchmark-feedback)<a href="https://agentmods.dev/skills/bonginkan/fairy_tale/fairy-tale-benchmark-feedback"><img src="https://agentmods.dev/badge/skills/bonginkan/fairy_tale/fairy-tale-benchmark-feedback/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/bonginkan/fairy_tale/fairy-tale-benchmark-feedback"><img src="https://agentmods.dev/badge/skills/bonginkan/fairy_tale/fairy-tale-benchmark-feedback.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.02302 |
| Opus 5.5 | $0.00000 | $0.00921 |
| Sonnet 5.5 | $0.00000 | $0.00460 |
| Haiku 4.5 | $0.00000 | $0.00230 |
Grade A, and why
fairy-tale-benchmark-feedback 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 — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fairy Tale Benchmark Feedback
Use this skill after a measured benchmark miss, work-product failure, or successful benchmark slice whose practice should be made reproducible in agentic coding, HLE-style closed-ended reasoning, or defensive ExploitBench sandbox runs.
Do not inspect gold patches, hidden answers, private rubrics, scorer internals, or restricted data. Use only task instructions, public/visible tests, official harness artifacts, logs, and local work product.
Feedback Loop
- Preserve the run conditions: model, effort, prompt, tools, scorer, sample IDs, budget, concurrency, and artifacts.
- Convert misses and observed success practices into a feedback ledger:
- SWE-Bench Pro:
scripts/benchmark_feedback_ledger.py swe-bench-pro - HLE-style tasks:
scripts/benchmark_feedback_ledger.py hle - ExploitBench:
scripts/benchmark_feedback_ledger.py exploitbench
- SWE-Bench Pro:
- Before writing a candidate rule, localize the first actionable fault step in the failed trajectory or work product. Link it to an existing Fairy Tale rule when that rule misled the run; otherwise mark it as missing coverage.
- Revise an existing rule when responsibility is clear. Generate a new narrow rule only when no existing rule can be safely revised. Make no skill update when the trace does not support the attribution.
- Run pruning before promotion:
scripts/feedback_pruner.py --ledger <ledger.json> --output <prune.json>. - Keep only narrow candidate or observed-success rules with evidence. Do not promote a rule because it sounds plausible.
- If the same failure signature repeats, a run produces no meaningful artifact, or the validation ledger is missing, run bounded Fairy Fusion before retry: isolated reviewers, one synthesis pass, append-only review artifacts, and only a compact closure hint returned to the main agent. Continue retrying until the local clear condition is met or the user/operator stops the run.
- Retry a held-out or failed slice under the same scorer. Record before/after pass rate, confidence interval when applicable, cost, and regressions.
- Promote only rules that improve the retry without task-ID hardcoding or cross-domain regression.
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 · 205 lines · 0 tokens per session scan A ea77049234d7
fairy-tale-benchmark-feedback is a skill published in the GitHub repository bonginkan/fairy_tale (19 stars, last pushed 25d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 2,302 tokens. 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 2026-08-30.
fairy-tale-benchmark-feedback compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| fairy-tale-benchmark-feedbackbonginkan/fairy_tale | Skill | 19 | 25d ago | Apache-2.0 | A | 0 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |