fairy_tale: Skill for Claude Code

.agents/skills/fairy-tale-legal-feedback/SKILL.md

fairy-tale-legal-feedback is a skill for Claude Code, Codex from bonginkan/fairy_tale. It costs 46 tokens per session (892 once invoked), scanned A, original, Apache-2.0.

A legal-review checklist for checking drafts, reviews, issue lists, forms, and calculations against common omissions. It also classifies failures such as missing clauses, incomplete issue coverage, or broken form structure.

In plain words
What is it for?
Use it to review legal drafts, spot issues, check forms and calculations, and build practice-area checklists.
Why use it?
It helps catch overlooked requirements after a legal task or benchmark error, especially in long or high-risk work. It adds a final completeness check without using hidden grading answers.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is bonginkan/fairy_tale's own configuration. It tells Claude Code and Codex how to work on fairy_tale itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything fairy_tale configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/bonginkan/fairy_tale/main/.agents/skills/fairy-tale-legal-feedback/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/bonginkan/fairy_tale

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for fairy-tale-legal-feedback

README.md
[![agentmods](https://agentmods.dev/badge/skills/bonginkan/fairy_tale/fairy-tale-legal-feedback/github.svg)](https://agentmods.dev/skills/bonginkan/fairy_tale/fairy-tale-legal-feedback)
Your own site
<a href="https://agentmods.dev/skills/bonginkan/fairy_tale/fairy-tale-legal-feedback"><img src="https://agentmods.dev/badge/skills/bonginkan/fairy_tale/fairy-tale-legal-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.

agentmods 80×15 button for fairy-tale-legal-feedback

Your own site · 80×15
<a href="https://agentmods.dev/skills/bonginkan/fairy_tale/fairy-tale-legal-feedback"><img src="https://agentmods.dev/badge/skills/bonginkan/fairy_tale/fairy-tale-legal-feedback.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 46 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 892 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00046 $0.00892
Opus 5 $0.00023 $0.00446
Sonnet 5 $0.00009 $0.00178
Haiku 4.5 $0.00005 $0.00089

Measured 12d ago against content hash 2faf2b39ebfc, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

fairy-tale-legal-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 12d 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.

.agents/skills/fairy-tale-legal-feedback/SKILL.md · 99 lines

How it starts

The opening of the file, as written. The whole thing — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Use this skill after a legal benchmark miss, on high-risk legal work product, or when a legal task resembles known weak areas from the 2026-06-14 LAB-style sample.

Do not read grading rubrics or hidden expected answers. Use only task instructions, provided matter documents, authorized tools, and the visible work product.

Failure Classes

  • near_miss_final_criterion: one missing requirement, caveat, citation, clause, date, party, threshold, schedule, exhibit, or signature item.
  • small_coverage_gap: two or three missed requirements.
  • moderate_coverage_gap: several missed requirements despite a plausible top-level structure.
  • domain_scaffold_gap: a practice area needs a domain-specific checklist.
  • large_draft_collapse: a long draft lost clause architecture, defined terms, cross-references, schedules, or negotiated business terms.
  • calculation_or_form_collapse: a worksheet, covenant, tax, support, or finance-like form was not handled table-first.
  • issue_spotting_coverage_collapse: discovery, diligence, counterparty review, or issue spotting lacked an exhaustive row-by-row matrix.

Required Closure Sweep

Before final output:

  1. Build a requirement ledger from the instructions, matter documents, playbooks, requested filenames, and requested output format.
  2. Mark each requirement as included, omitted, not applicable, or conflict.
  3. Resolve every omitted or conflict row before finalizing.
  4. Run a one-miss audit for headings, defined terms, party names, dates, jurisdictions, thresholds, notice mechanics, exceptions, schedules, exhibits, signature blocks, citations, and caveats.

Weak-Area Scaffolds

  • Long drafts: create clause inventory, defined-term ledger, cross-reference ledger, section-to-requirement reconciliation, and schedule/exhibit/signature closure before prose polish.
  • Calculations/forms: extract inputs into a table, record governing formula, units, dates, periods, thresholds, and reconcile every output field.
  • Issue spotting: create one row per source document, request, objection, clause, counterparty mark, or issue before deduplication.
  • Final criterion closure: when the work product is close, ask what single criterion a grader would still mark missing; verify every instruction bullet, playbook rule, counterparty position, requested category, filename, and output-format obligation is explicitly represented or ruled out with evidence.

Read the full file on GitHub · 99 lines

Changes

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.

  1. 12d ago First seen · 99 lines · 46 tokens per session scan A 2faf2b39ebfc

Subscribe to this mod's changes

fairy-tale-legal-feedback is a skill published in the GitHub repository bonginkan/fairy_tale (18 stars, last pushed 4d ago), licensed Apache-2.0. It adds 46 tokens to every session and 892 once invoked, about $0.0002 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 2026-08-30.

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