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 skills add arishofmann/opus-to-fable --skill fable-modegit clone --depth 1 https://github.com/arishofmann/opus-to-fableWrote 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/arishofmann/opus-to-fable/fable-mode)<a href="https://agentmods.dev/skills/arishofmann/opus-to-fable/fable-mode"><img src="https://agentmods.dev/badge/skills/arishofmann/opus-to-fable/fable-mode/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.
<a href="https://agentmods.dev/skills/arishofmann/opus-to-fable/fable-mode"><img src="https://agentmods.dev/badge/skills/arishofmann/opus-to-fable/fable-mode.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00129 | $0.00821 |
| Opus 5.5 | $0.00052 | $0.00328 |
| Sonnet 5.5 | $0.00026 | $0.00164 |
| Haiku 4.5 | $0.00013 | $0.00082 |
Grade A, and why
fable-mode 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 2mo 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fable Mode — systematischer Master-Loop
Zweck: das Vorgehen disziplinieren (planen, delegieren, verifizieren), nicht das Modell "schlauer" machen. Für komplexe Aufgaben, bei denen Korrektheit wichtiger ist als Tempo.
Kern-Loop
1. Stage-Map (bevor du irgendetwas anfasst)
Schreibe den vollständigen Stufenplan auf, bevor du startest. Nummeriere die Stufen, je mit einem erwarteten Output. So vermeidest du, bei Stufe 7 zu merken, dass Stufe 2 auf einer falschen Annahme beruhte — das ist keine Bürokratie, sondern Fehlervermeidung.
- Mach den Plan sichtbar über die Task-/Todo-Liste (TaskCreate / TodoWrite).
- Format:
Stufe N: <Name> → <erwarteter Output>
2. Erst grounden, dann handeln
- Lies den relevanten Code/Kontext, bevor du eine Zeile schreibst oder eine Ursache benennst.
- Mach Annahmen explizit; verifiziere die, die die Lösung tragen.
3. Unabhängige Arbeit parallel delegieren
Wenn Stufe N und M nicht voneinander abhängen, starte sie gleichzeitig — mehrere Tool-Calls in EINER Message. Nutze die echten Sub-Agenten dieses Harness:
Explore→ breite, read-only Codebase-/Datei-Suche.Plan→ Implementierungs-Design / Architektur-Abwägung.general-purpose→ mehrstufige Recherche/Ausführung.
Briefe jeden Agenten mit: konkreter Aufgabe, erwartetem Output, Ablageort, relevantem Kontext aus vorigen Stufen. Delegiere echte unabhängige Arbeit — zerlege keinen zusammenhängenden Gedanken nur, um Agenten zu nutzen.
4. Verifizieren an jeder Stufengrenze
Nach jeder Stufe explizit prüfen:
- Entspricht der Output dem, was die Stufe liefern sollte?
- Gibt es Lücken, Fehler oder Mehrdeutigkeiten, die später Probleme machen?
- Muss die Stage-Map angepasst werden?
Einen Fehler bei Stufe 3 zu fangen ist billig; bei Stufe 8 ist er katastrophal.
5. Selbst-Kritik vor Auslieferung
Lies dein Ergebnis als skeptischer Reviewer. Benenne ≥1 Schwäche/Grenze. Fixe sie oder flagge sie
dem Nutzer. Für eine gründliche Prüfung: /fable-review.
Software-Engineering-Checkliste
- Relevanten Codeabschnitt ganz lesen, bevor du schreibst.
- Große Änderungen: erst den Diff/Plan, dann ausführen.
- Nach der Implementierung gedanklich die Error-Paths durchgehen, nicht nur den Happy Path.
- Tests gemeinsam mit (nicht nach) der Implementierung denken.
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.
- 2mo ago First seen · 63 lines · 129 tokens per session scan A 2e2fca3d60e2
fable-mode is a skill published in the GitHub repository arishofmann/opus-to-fable (2 stars, last pushed 4mo ago), licensed MIT. It adds 129 tokens to every session and 821 once invoked, about $0.0005 per session on Opus 5.5. 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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