fablize: A Claude Code plugin that makes Opus behave like Fable — completion, evidence, and verification enforced

by fivetaku · Agent Tool · ★ 895

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install fablize

/plugin marketplace add fivetaku/fablize
/plugin install fablize

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 68/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

agenticanthropicclaudeclaude-codefableharnessopus

Quick Facts

Stars895
Forks125
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score68/100
Open Issues10
Last Updated2026-07-06
Created2026-06-14
Platformsclaude-code, python
Est. Tokens~5k

About fablize

English | 한국어 fablize — run Opus like Fable A Claude Code plugin that makes Opus (or any Claude model) see a task through to the end — with evidence and verification — as procedure, not as luck. Why When Fable 5 shipped, I ran a controlled comparison of Fable 5 and Opus 4.8 (an A/B set of 19 runs plus 26 real working sessions, 1,500 tool calls).

Compatible Skills

These tools work well together with fablize for enhanced workflows:

  • fusion-fable — semantic(0.54)+complementary+same_lang+similar_pop+shared_platform (64%)
  • verify — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (56%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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