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-securitygit 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-security)<a href="https://agentmods.dev/skills/immamdouhaboammar/get-fable/fable-security"><img src="https://agentmods.dev/badge/skills/immamdouhaboammar/get-fable/fable-security/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-security"><img src="https://agentmods.dev/badge/skills/immamdouhaboammar/get-fable/fable-security.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 | $0.00126 | $0.01888 |
| Opus 5 | $0.00063 | $0.00944 |
| Sonnet 5 | $0.00025 | $0.00378 |
| Haiku 4.5 | $0.00013 | $0.00189 |
Grade A, and why
fable-security 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 — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fable Security
Reason about attacker-controlled paths and trust boundaries until every security finding has a concrete source, sink, prerequisite, and impact.
Mission
Security review is not a secret scan plus a generic checklist. It asks what an attacker can control, which privilege/data boundary that input can cross, what enforcement must hold, and whether the changed code preserves that property under failure and concurrency.
A clean scanner is evidence about scanner coverage, not a proof that the design is secure.
Activate When
- authentication, authorization, sessions, tokens, permissions, tenancy, or privileged actions change;
- untrusted input reaches parsers, queries, templates, files, URLs, shells, webhooks, or deserializers;
- secrets/credentials or cryptographic material are handled;
- a diff changes trust boundaries, network exposure, storage access, package/install logic, or sandboxing;
- a reported vulnerability/finding needs validation or severity calibration;
- repository/package supply-chain behavior needs security review.
Do Not Activate When
- the task is only functional verification with no security question (
fable-verify); - a generic maintainability review has no trust-boundary impact (
fable-review); - a vulnerability is already validated and the user wants only a bounded fix (
fable-execute, while preserving security acceptance).
Security Work Classification
| Mode | Primary question |
|---|---|
| Threat model | what assets/actors/boundaries/abuse paths exist? |
| Security diff review | what security property changed in this diff? |
| Finding validation | can attacker-controlled data actually reach a sensitive sink? |
| Repository audit | which exposed surfaces deserve deeper inspection? |
| Secret hygiene | can sensitive values enter source/logs/artifacts? |
| Supply-chain/package | can dependency/install/build boundaries be abused? |
Protocol
Stage 1 — Define assets, actors, and trust boundaries
Identify:
- protected assets/data/operations;
- authenticated/unauthenticated/privileged actors;
- tenant/user ownership boundaries;
- external systems/webhooks/plugins;
- process/filesystem/network privilege transitions.
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 392 B
- evals/scenarios.json 4.1 KB
- examples/security-audit-walkthrough.md 423 B
- references/secret-sanitization.md 1.2 KB
- references/threat-modeling-matrix.md 1.6 KB
- references/trust-boundary-and-finding-validation.md 3.3 KB
- skill.package.json 510 B
- templates/security-finding.template.md 628 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.
- 4d ago First seen · 204 lines · 126 tokens per session scan A cde853d3b47d
fable-security is a skill published in the GitHub repository imMamdouhaboammar/get-fable (3 stars, last pushed 4d ago), licensed MIT. It adds 126 tokens to every session and 1,888 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
starting-a-new-project
Use when the workspace is empty — no code yet — and the user brings a raw idea: the brand-new branch of setting-up-a-project, normally reached via that dispatcher, directly only when the situation is unmistakable. Not for features in an existing project — use brainstorming instead.
todos
This chat has a shared, live TODO plan — your tasks for the conversation, which the user also edits. Read this skill and reach for the todo tools whenever a request takes more than a couple of steps. It covers the plan model (group = task, items = its steps; loose items are the user's lane), how to work it: propose…
writing-workflow-skills
Use when adding a new workflow skill to pi-thinkrail-workflow, changing an existing workflow skill's role, trigger, handoff, or structure, or checking a workflow skill against the workflow system's rules. Not for authoring general-purpose skills outside this package.
brainstorming
Use this BEFORE any creative or feature work: building a new feature, adding functionality, changing behavior, or making a nontrivial design decision. Turns the user's request into a validated design — recorded as a spec-graph task-spec — before any implementation. Do not skip this because a change looks small.
reviewing-changes
Use when a review package asks you to review a plan step's change set (todo.startReview): you are the REVIEWER, not the author. How to judge an agent-written diff, file findings with addreviewcomment, and settle with exactly one reviewverdict.
spec-graph
The project's specs are its ground truth: durable documents describing the architecture, decisions, contracts, and boundaries behind the code, organized as a connected graph. Read this skill and reach for the spec tools FIRST — before reading code — whenever you explore the project, plan or start a task, add or change…