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 agentmods add skills/rhein1/fable5-codex/fable-design-optionsnpx skills add rhein1/fable5-codex --skill fable-design-optionsgit clone --depth 1 https://github.com/rhein1/fable5-codexWrote 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/rhein1/fable5-codex/fable-design-options)<a href="https://agentmods.dev/skills/rhein1/fable5-codex/fable-design-options"><img src="https://agentmods.dev/badge/skills/rhein1/fable5-codex/fable-design-options.svg" alt="Measured on agentmods" 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 | $0.00060 | $0.00554 |
| Opus 5 | $0.00030 | $0.00277 |
| Sonnet 5 | $0.00012 | $0.00111 |
| Haiku 4.5 | $0.00006 | $0.00055 |
Grade A, and why
fable-design-options 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 5d 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 — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fable Design Options
Generate options after reading the actual system. Avoid abstract architecture advice when source evidence is available.
For ECF-style governed runs, use ../../references/ecf-run-contract.md. For large or high-risk design-option tasks, use real Codex subagents when the runtime exposes a subagent tool and the user has not opted out; treat architecture, migration, API, data model, security/privacy/money/data/API, operational, release, or cross-module decisions as large by default. Otherwise run single-agent multi-lens and say why no subagents were used when workflow trace is requested.
For large or high-risk Fable tasks, recommend running the parent task on gpt-5.6-sol with Ultra (model_reasoning_effort = "ultra") when available. Ultra may delegate proactively, but still explicitly request parallel delegation for disjoint Fable lenses when the runtime supports subagents; otherwise use single-agent multi-lens and report the reason.
Workflow
- Clarify the decision, constraints, authority boundaries, and success criteria from the prompt and repo instructions.
- Declare the ECF run mode when the user asks for ECF, subagents, or a receipt.
- Read the relevant implementation, callers/importers, tests, docs, and operational surfaces.
- Identify hard constraints: public contracts, migration compatibility, security/privacy rules, performance needs, deploy boundaries, and rollback requirements.
- Produce 2-4 viable options. Include a conservative option and a higher-leverage option when both are realistic.
- For each option, state:
- implementation shape
- benefits
- risks
- migration/rollback path
- tests or probes needed
- evidence that makes it fit or not fit the repo
- Recommend one option only after comparing it against the constraints.
Evidence Safety
Never print raw secrets, tokens, private keys, wallet keys, credential files, or .env values. Redact secret-like values and cite only the file/path/key name needed to explain the issue.
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.
- 5d ago First seen · 39 lines · 60 tokens per session scan A d3c8b4751325
fable-design-options is a skill published in the GitHub repository rhein1/fable5-codex (4 stars, last pushed 15d ago), licensed MIT. It adds 60 tokens to every session and 554 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
grok-build-delegate
Use the local Grok Build CLI for second-opinion reviews, adversarial critiques, or delegated read-only investigation from Codex.
watch
File sentinel that monitors the working directory for changes and marker comments, then auto-triggers appropriate skills. Poll-based via git diff against the last scan commit. Writes intake items for batch processing and routes marker actions through /do. Use for automatic reactions to file changes; do NOT use for…
review
5-pass structured code review — correctness, security, performance, readability, consistency.
live-preview
Mid-build visual verification loop. Takes screenshots of components during construction, not just after. Catches visual regressions and invisible features before they compound. Requires Playwright or similar screenshot tool.
marshal
Meta-orchestrator that takes any direction — broad, specific, or vague — and autonomously chains skills and context into actionable work. Gathers context from codebase, docs, and memory. Only asks the user when it genuinely cannot proceed. Single-session orchestrator.
wiki
Markdown-first knowledge base where the LLM acts as librarian. Ingests raw sources, compiles and interlinks topic files, self-maintains an index. No vector DB or embeddings required -- uses LLM-native navigation over structured markdown up to 400K words.