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 rules/joey114132/fable-workflow-skill/fable-workflowgit clone --depth 1 https://github.com/joey114132/fable-workflow-skillWhat 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 | $0.00049 | $0.00430 |
| Opus 5 | $0.00024 | $0.00215 |
| Sonnet 5 | $0.00010 | $0.00086 |
| Haiku 4.5 | $0.00005 | $0.00043 |
Grade A, and why
fable-workflow 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 2d 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.
What it actually says
Fable Workflow
The map is not the territory. The spec/prompt is the map; the real codebase and constraints are the territory. Wherever they diverge is an unknown — an unspecified decision. Don't guess it silently. Surface it first, then build. (Skip this for trivial one-line changes.)
The loop
- Unhobble — use tools, not memory. Counting / enumeration / precise lookup → write a script.
- Find the unknowns before building:
- Blind-spot pass — "list my unknown-unknowns and the gotchas in this module/domain."
- Interview me — ask questions, prioritising ones that would change the architecture.
- Variants — for taste calls (design/format/API), offer N genuinely different options.
- References as maps — prefer an example/mockup over a written spec.
- Build, logging deviations — keep a running ASSUMPTIONS / NOTES list of every unknown hit.
- Verify — make it real — exercise the result (run it / smallest check); a good plan isn't a correct answer. On failure, loop attempt→verify→diagnose with a max-tries cap; stop/surface if the signal stalls.
- Stay in the loop — quiz the human before merge.
Rules
- Vague spec / unfamiliar territory → find unknowns before implementing; don't build one interpretation silently.
- Enumeration / counting / precise lookup → script it, don't recall.
- Taste / subjective call → offer variants.
- Can't ask (autonomous)? Write an UNKNOWNS list, pick a default per item with a one-line reason, log them, then proceed.
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.
- 2d ago First seen · 29 lines · 49 tokens per session scan A 20f9ccde399b
fable-workflow is a cursor rule published in the GitHub repository joey114132/fable-workflow-skill (2 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 430 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 2026-08-31.
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