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Get Started Free →Apply a Claude Fable 5 inspired operating style inside Codex. Use when the user asks to make Codex act like Fable, Fable5, fablize, or Value-for-Fable/VFF; convert Anthropic or Claude system-prompt/tool instructions to Codex; set up a Fable-style tool-first workflow with goal gates, investigation, verification grounding, cost-aware routing, or 2-pass review; create Codex AGENTS.md guidance from Fable-like behavior; or connect Codex to an authorized Fable-compatible provider through an OpenAI-com
.claude/skills/bilal140202-codex-fable5/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-07 | ✗→✓ | ▲ Improved | 22% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 23% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -21% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 26% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 14% | 0% |
Use this skill to translate Fable-style operating discipline into Codex behavior. It cannot change model weights, context length, training, hidden runtime behavior, or safety systems. It should make Codex inspect first, route deliberately, track evidence when work is long or review-sensitive, and verify before claiming completion.
rg or rg --files first for local search.scripts/codex_goals.py or an equivalent visible plan with evidence checkpoints and a final verification gate.scripts/codex_findings.py for review findings, failed verification, unresolved clues, security-sensitive work, or multi-file changes with expensive misses.| Signal | Read or use | | --- | --- | | Multi-step, long autonomous, migration, review-sensitive, or failed/uncertain verification work | references/task-routing.md; use goal and findings gates when appropriate | | Fablize, VFF, cost-aware routing, diagnosis, 2-pass review | references/task-routing.md, references/operating-structure.md | | Claude/Fable prompt or tool conversion | references/fable-to-codex-map.md | | Fable coverage, parity, or "100% covered" requests | references/coverage-matrix.md; run scripts/fable_coverage.py --source ... when source is available | | Actual Fable-family provider routing | references/provider-bridge.md; verify model access, credentials, and Codex provider support before config edits | | Search, current facts, citations, copyright, safety, refusals, wellbeing, high-stakes advice | references/currentness-safety.md | | Files, artifacts, generated apps, visual verification, package management, or Claude tool schemas | references/artifact-and-tooling.md | | Apps, plugins, MCP, connector installation, or private workspace data | references/connectors-and-mcp.md | | Memory, persistent state, ledgers, storage boundaries, or durable behavior | references/state-memory.md | | Attribution, source notes, licensing, or upstream prompt provenance | references/provenance.md |
Use the smallest durable surface that fits: one prompt for one-off behavior, AGENTS.md for repo conventions, a skill for reusable workflow, a plugin for distribution, a connector for live external data, or provider config only for authorized model routing.
scripts/codex_goals.py for a local, stdlib-only multi-story ledger with evidence checkpoints and a final verification gate.scripts/codex_findings.py for a local, stdlib-only review findings ledger. Final codex_goals.py checkpoints fail while open or blocked findings remain.plugins/codex-fable5/bin to PATH and run codex-fable5 status, codex-fable5 goals ..., or codex-fable5 findings ....scripts/fable_coverage.py --source /path/to/CLAUDE-FABLE-5.md to verify that every source heading is accounted for in references/coverage-matrix.md.scripts/make_litellm_config.py to generate a LiteLLM config for an Anthropic model alias. Use this only after confirming the user has a valid Anthropic key and model access.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 4,127 | 4,426 | +7% | 1 | 1 | 0% | 316 | 1,658 | +425% | 0 | 0 | — |
case-02 | fail→fail | 11,691 | 4,768 | -59% | 1 | 1 | 0% | 231 | 1,721 | +645% | 0 | 0 | — |
case-03 | fail→fail | 3,929 | 4,437 | +13% | 1 | 1 | 0% | 205 | 1,605 | +683% | 0 | 0 | — |
case-04 | pass→pass | 9,337 | 8,299 | -11% | 1 | 1 | 0% | 2,127 | 3,138 | +48% | 0 | 0 | — |
case-05 | pass→pass | 2,077 | 1,862 | -10% | 1 | 1 | 0% | 287 | 1,622 | +465% | 0 | 0 | — |
case-06 | fail→fail | 1,755 | 4,198 | +139% | 1 | 1 | 0% | 286 | 2,073 | +625% | 0 | 0 | — |
case-07 | fail→pass | 9,639 | 3,636 | -62% | 1 | 1 | 0% | 1,622 | 1,985 | +22% | 0 | 0 | — |
case-08 | fail→pass | 7,977 | 2,934 | -63% | 1 | 1 | 0% | 1,567 | 1,922 | +23% | 0 | 0 | — |
case-09 | pass→pass | 4,011 | 2,518 | -37% | 1 | 1 | 0% | 797 | 1,794 | +125% | 0 | 0 | — |
case-10 | fail→pass | 10,090 | 2,893 | -71% | 1 | 1 | 0% | 2,213 | 1,750 | -21% | 0 | 0 | — |
case-11 | fail→pass | 6,478 | 2,941 | -55% | 1 | 1 | 0% | 1,232 | 1,551 | +26% | 0 | 0 | — |
case-12 | fail→pass | 9,062 | 3,375 | -63% | 1 | 1 | 0% | 1,655 | 1,885 | +14% | 0 | 0 | — |
case-13 | fail→pass | 9,241 | 1,348 | -85% | 1 | 1 | 0% | 1,549 | 1,530 | -1% | 0 | 0 | — |
case-14 | pass→pass | 6,254 | 2,720 | -57% | 1 | 1 | 0% | 1,160 | 1,769 | +53% | 0 | 0 | — |
case-15 | pass→pass | 7,899 | 3,403 | -57% | 1 | 1 | 0% | 1,373 | 1,881 | +37% | 0 | 0 | — |
case-16 | fail→pass | 12,546 | 10,404 | -17% | 1 | 1 | 0% | 2,283 | 2,920 | +28% | 0 | 0 | — |
case-17 | fail→pass | 5,770 | 2,792 | -52% | 1 | 1 | 0% | 938 | 1,695 | +81% | 0 | 0 | — |
case-18 | pass→pass | 11,625 | 8,200 | -29% | 1 | 1 | 0% | 1,887 | 2,583 | +37% | 0 | 0 | — |
case-19 | fail→pass | 9,101 | 2,298 | -75% | 1 | 1 | 0% | 1,651 | 1,770 | +7% | 0 | 0 | — |
case-20 | pass→pass | 3,019 | 3,346 | +11% | 1 | 1 | 0% | 590 | 1,964 | +233% | 0 | 0 | — |
case-21 | fail→pass | 10,190 | 1,931 | -81% | 1 | 1 | 0% | 1,690 | 1,604 | -5% | 0 | 0 | — |
case-22 | fail→pass | 5,265 | 1,730 | -67% | 1 | 1 | 0% | 919 | 1,663 | +81% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 19 counted toward the lift figure. The other 3 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +50 percentage points is the difference between those two pass rates over the 19 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.