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-evalgit 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-eval)<a href="https://agentmods.dev/skills/immamdouhaboammar/get-fable/fable-eval"><img src="https://agentmods.dev/badge/skills/immamdouhaboammar/get-fable/fable-eval/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-eval"><img src="https://agentmods.dev/badge/skills/immamdouhaboammar/get-fable/fable-eval.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 | $0.00109 | $0.01722 |
| Opus 5 | $0.00055 | $0.00861 |
| Sonnet 5 | $0.00022 | $0.00344 |
| Haiku 4.5 | $0.00011 | $0.00172 |
Grade A, and why
fable-eval 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 4d 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 — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fable Eval
Measure whether an agent-control change improves the behavior it claims to improve without quietly overfitting the benchmark or breaking neighboring behavior.
Mission
An eval is a decision instrument, not a scoreboard. It needs a frozen comparison point, representative semantic families, oracle isolation, explicit failure costs, and a rollback decision.
A candidate should not win because the prompts resemble its instructions, because holdouts leaked into authoring, or because one average score hides a severe regression.
Activate When
- changing Skills, prompts, routers, hooks, agent profiles, policies, or model-control logic;
- comparing candidate prompt/agent configurations;
- measuring trigger precision/recall or action compliance;
- validating a new behavioral maturity claim;
- investigating whether an apparent improvement is robust or benchmark-specific.
Do Not Activate When
- verifying ordinary application behavior (
fable-verify); - authoring a Skill before its intended behavior is clear (
skill-creator); - running a one-off subjective prompt demo with no acceptance decision.
Evaluation Classification
| Change | Primary eval risk |
|---|---|
| Router/trigger | false positives, false negatives, precedence |
| Skill instruction | action correctness, forbidden shortcuts, boundary behavior |
| Spark/next-action | top-1 action, unsafe suggestion, silence precision |
| Hook/guard | enforcement, false blocking, bypasses |
| Prompt/persona | task quality + regressions + instruction conflicts |
| Tool policy | correct tool choice, unsafe/missing action |
| Model/config | quality/latency/cost variance across representative tasks |
Protocol
Stage 1 — Define the decision before running tests
State:
- candidate being evaluated;
- baseline/control;
- exact behavior expected to improve;
- metrics and thresholds;
- unacceptable regressions;
- rollback action.
Avoid inventing metrics after seeing results.
Stage 2 — Build semantic scenario families
Cover distinct decisions, not wording variants. Include as applicable:
- straightforward positive case;
- non-trigger/boundary case;
- ambiguous competing action;
- adversarial shortcut pressure;
- partial/contradictory evidence;
- failure/recovery path;
- legacy/constrained environment;
- unseen holdout.
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.
- 4d ago First seen · 213 lines · 109 tokens per session scan A 32dfc8d44e41
fable-eval is a skill published in the GitHub repository imMamdouhaboammar/get-fable (3 stars, last pushed 4d ago), licensed MIT. It adds 109 tokens to every session and 1,722 once invoked, about $0.0005 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
brainstorming
Interactive spec generation — turn ideas into concrete specs with R-numbered requirements and testable acceptance criteria.
backpropagation
Trace runtime bugs back to spec gaps — identify missing acceptance criteria, update specs, generate regression tests, and detect patterns.
tdd
Test-driven development with red-green-refactor loop. Use when user wants to build features or fix bugs using TDD, mentions "red-green-refactor", wants integration tests, or asks for test-first development.
temporal-python-testing
Test Temporal workflows with pytest, time-skipping, and mocking strategies. Covers unit testing, integration testing, replay testing, and local development setup. Use when implementing Temporal workflow tests or debugging test failures.
workflow-patterns
Use this skill when implementing tasks according to Conductor's TDD workflow, handling phase checkpoints, managing git commits for tasks, or understanding the verification protocol.
llm-as-judge-evaluation
Evaluate LLM outputs using frontier models as judges. Use for pairwise model comparison, quality scoring with custom rubrics, and automated evaluation pipelines. Covers position bias mitigation, statistical significance, and generating preference data for DPO/RLHF.