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 imMamdouhaboammar/get-fable --skill fable-loopgit clone --depth 1 https://github.com/imMamdouhaboammar/get-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/immamdouhaboammar/get-fable/fable-loop)<a href="https://agentmods.dev/skills/immamdouhaboammar/get-fable/fable-loop"><img src="https://agentmods.dev/badge/skills/immamdouhaboammar/get-fable/fable-loop/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/immamdouhaboammar/get-fable/fable-loop"><img src="https://agentmods.dev/badge/skills/immamdouhaboammar/get-fable/fable-loop.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 | $0.00111 | $0.01700 |
| Opus 5 | $0.00056 | $0.00850 |
| Sonnet 5 | $0.00022 | $0.00340 |
| Haiku 4.5 | $0.00011 | $0.00170 |
Grade A, and why
fable-loop 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 7d 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 — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fable Loop
Repeat a check only when repetition can reveal new state, with explicit termination, backoff, error classification, and zero ambiguity about why the loop stopped.
Mission
Polling is not "run the same command until it turns green." A good loop models a changing external condition, distinguishes pending from failure, respects rate/cost budgets, and terminates on success, terminal failure, cancellation, or exhausted budget.
If repeated execution cannot produce new information, the task belongs in diagnosis—not a loop.
Activate When
- waiting for CI/deployment/build/batch-job state to change;
- monitoring a bounded asynchronous operation;
- checking eventually-consistent external state;
- repeating a safe measurement while a known process progresses;
- running a finite stabilization sample where repetition itself is the measurement.
Do Not Activate When
- one command/probe is enough (
fable-run/fable-verify); - the same deterministic failure is repeating with no external state change (
fable-recover); - user expects a future notification/scheduled task rather than an in-session bounded loop;
- polling would create repeated non-idempotent side effects;
- no termination criteria or budget can be defined.
Loop Classification
| Loop type | Success/terminal semantics |
|---|---|
| CI/build | pending → success or terminal failure/cancelled |
| Deployment | progressing → healthy/rolled back/failed |
| Batch job | queued/running → completed/failed |
| Eventual consistency | old state → expected state within deadline |
| Rate-limited API | pending/retryable → success or terminal auth/schema error |
| Stabilization sampling | N bounded observations → distribution/variance verdict |
Protocol
Stage 1 — Define the state machine
Before iteration, enumerate:
- success state;
- pending/retryable states;
- terminal failure states;
- malformed/unknown states;
- cancellation condition.
A loop that treats every non-success as "try again" is unsafe.
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 195 lines · 111 tokens per session scan A 3732c1e3cbcd
fable-loop is a skill published in the GitHub repository imMamdouhaboammar/get-fable (4 stars, last pushed today), licensed MIT. It adds 111 tokens to every session and 1,700 once invoked, about $0.0006 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 2026-09-05.
Other skills, from other repositories
deployment-pipeline-design
Design multi-stage CI/CD pipelines with approval gates, security checks, and deployment orchestration. Use this skill when designing zero-downtime deployment pipelines, implementing canary rollout strategies, setting up multi-environment promotion workflows, or debugging failed deployment gates in CI/CD.
bazel-build-optimization
Optimize Bazel builds for large-scale monorepos. Use when configuring Bazel, implementing remote execution, or optimizing build performance for enterprise codebases.
baby-sit
Monitor a GitHub pull request until CI is green, diagnose failures, and rerun only evidence-backed flaky GitHub Actions jobs.
babysit
Use when the user says "babysit", "monitor", "keep checking", "keep an eye on", "loop on this PR", "let me know when", or wants polling that outlives a wait+poll window. Same-session monitoring loop for PRs, CI runs, tickets, and deployments using the monitorstart / monitorupdate / autonudgestop MCP tools. The loop…
deploying-to-staging-environment
Use when deploying changes to staging across relay, relay-dashboard, and relay-cloud repos - coordinates multi-repo branch syncing using git worktrees, automatically triggers staging deployments via GitHub Actions.
turborepo-caching
Configure Turborepo for efficient monorepo builds with local and remote caching. Use when setting up Turborepo, optimizing build pipelines, or implementing distributed caching.