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-verifygit 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-verify)<a href="https://agentmods.dev/skills/immamdouhaboammar/get-fable/fable-verify"><img src="https://agentmods.dev/badge/skills/immamdouhaboammar/get-fable/fable-verify/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-verify"><img src="https://agentmods.dev/badge/skills/immamdouhaboammar/get-fable/fable-verify.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 | $0.00119 | $0.01789 |
| Opus 5 | $0.00060 | $0.00894 |
| Sonnet 5 | $0.00024 | $0.00358 |
| Haiku 4.5 | $0.00012 | $0.00179 |
Grade A, and why
fable-verify 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 6d 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 — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fable Verify
Try to prove the implementation wrong, then report only the claims that survive fresh evidence.
Mission
Verification is not "run the test suite." It is coverage of changed risk with evidence that is both relevant and fresh.
A green command proves only what it actually exercised. Build success does not prove runtime behavior. Security success does not prove functional correctness. Unit success does not prove a packaging or integration path. Old evidence does not prove a newer mutation.
Activate When
- implementation or repair is ready for independent proof;
- a completion claim needs fresh evidence;
- a review/release gate requires test/build/runtime evidence;
- stale evidence must be refreshed after mutation;
- the suspected regression surface spans more than the focused TDD test.
Do Not Activate When
- writing the implementation (
fable-execute/fable-tdd); - deciding architecture (
fable-plan); - reviewing design/maintainability from the diff (
fable-review); - diagnosing repeated confusing failures (
fable-recover).
Risk Classification
Map each changed surface to the evidence capable of falsifying it.
| Changed surface | Typical evidence |
|---|---|
| pure behavior | focused unit/property + affected suite |
| cross-module contract | integration/contract test |
| public CLI/API | invocation/smoke + contract tests |
| build/export/package | build + package/clean-install smoke |
| persistence/migration | integration + migration/compatibility fixtures |
| async/concurrency | deterministic ordering/stress supplement |
| config/feature flag | tests under relevant config branches |
| browser/UI | component/integration/E2E as appropriate |
| security boundary | security-specific checks plus functional evidence |
| performance-sensitive path | targeted measurement when requirement exists |
Verification Protocol
Stage 1 — Read the diff and execution packet
Do not choose commands from habit alone. Identify:
- behavior changed;
- files/contracts touched;
- tests added/changed;
- generated/package/config surfaces;
- residual risks from implementation;
- current mutation generation.
What ships with it
8 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.
- agents/openai.yaml 362 B
- evals/scenarios.json 4.3 KB
- examples/falsification-session.md 483 B
- references/evidence-recording.md 934 B
- references/falsification-heuristics.md 1.0 KB
- references/verification-matrix-and-evidence-strength.md 2.9 KB
- skill.package.json 513 B
- templates/verification-evidence.template.md 624 B
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.
- 6d ago First seen · 208 lines · 119 tokens per session scan A a57790355485
fable-verify is a skill published in the GitHub repository imMamdouhaboammar/get-fable (4 stars, last pushed yesterday), licensed MIT. It adds 119 tokens to every session and 1,789 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
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.