spec-kit-template: Skill for Claude Code

.github/skills/spec-kit-security-baseline/SKILL.md

spec-kit-security-baseline is a skill for Claude Code, Codex from lksnext-ai-lab/spec-kit-template. It costs 64 tokens per session (1,820 once invoked), scanned A, original, Apache-2.0.

A security-planning guide for documenting how a software product handles identity, permissions, sensitive data, secrets, audits, integrations, and common threats.

In plain words
What is it for?
Use it to define or review authentication, access rules, data protection, audit records, API controls, and threat-to-mitigation checks.
Why use it?
It helps make security expectations clear and checkable without assuming protections that have not been decided.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

This is lksnext-ai-lab/spec-kit-template's own configuration. It tells Claude Code and Codex how to work on spec-kit-template itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything spec-kit-template configures →

Reuse

Borrowing it

Nothing to install: this file belongs to lksnext-ai-lab/spec-kit-template. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/lksnext-ai-lab/spec-kit-template/main/.github/skills/spec-kit-security-baseline/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/lksnext-ai-lab/spec-kit-template

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for spec-kit-security-baseline

README.md
[![agentmods](https://agentmods.dev/badge/skills/lksnext-ai-lab/spec-kit-template/spec-kit-security-baseline/github.svg)](https://agentmods.dev/skills/lksnext-ai-lab/spec-kit-template/spec-kit-security-baseline)
Your own site
<a href="https://agentmods.dev/skills/lksnext-ai-lab/spec-kit-template/spec-kit-security-baseline"><img src="https://agentmods.dev/badge/skills/lksnext-ai-lab/spec-kit-template/spec-kit-security-baseline/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.

agentmods 80×15 button for spec-kit-security-baseline

Your own site · 80×15
<a href="https://agentmods.dev/skills/lksnext-ai-lab/spec-kit-template/spec-kit-security-baseline"><img src="https://agentmods.dev/badge/skills/lksnext-ai-lab/spec-kit-template/spec-kit-security-baseline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 64 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,820 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00064 $0.01820
Opus 5 $0.00032 $0.00910
Sonnet 5 $0.00013 $0.00364
Haiku 4.5 $0.00006 $0.00182

Measured 11d ago against content hash 458ae2629060, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

spec-kit-security-baseline 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 11d 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.

.github/skills/spec-kit-security-baseline/SKILL.md · 155 lines

How it starts

The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Skill: Security Baseline (spec-kit)

Objetivo

Dejar docs/spec/80-seguridad.md en un estado operable y verificable para MVP:

  • AuthN (autenticación, sesiones/tokens)
  • AuthZ (modelo, reglas por recurso)
  • Protección de datos y secretos
  • Auditoría / trazabilidad de acciones sensibles
  • Controles de API e integraciones
  • Threat-lite (amenazas → mitigaciones → verificación/evidencia)

Archivos que se pueden modificar

  • Primario: docs/spec/80-seguridad.md
  • Si aplica:
    • docs/spec/11-requisitos-tecnicos-nfr.md (NFRs de seguridad verificables/medibles)
    • docs/spec/90-infra.md (secretos, hardening, retenciones operativas)
    • docs/spec/40-arquitectura.md (impactos y patrones)
    • docs/spec/adr/ADR-*.md (trade-offs)
    • docs/spec/95-open-questions.md (gaps críticos)
    • docs/spec/96-todos.md (pendientes no bloqueantes)

No tocar otros ficheros salvo petición explícita.

Reglas duras (anti-deriva)

  1. No inventar
  • No asumir MFA/SSO, RBAC/ABAC, cifrado en reposo, retenciones, herramientas, ni niveles de logging si no están definidos.
  • Si falta info que condiciona el diseño/aceptación: OPENQ: (no rellenar).
  1. No secretos / no datos sensibles
  • Nunca incluir valores reales (keys, tokens, passwords, certificados) ni datos personales en ejemplos.
  • Regla de logs/errores: no deben exponer secretos ni datos sensibles (si aplica, declararlo como control + verificación).
  1. Vendor-neutral por defecto
  • Describir patrones (OIDC, secrets manager, rate limiting) sin imponer proveedor/stack salvo evidencia explícita.
  1. Deny-by-default
  • Por defecto, sin permiso explícito → acceso denegado.
  • Excepciones solo si están justificadas y evidenciadas.
  1. Trade-offs relevantes → ADR
  • Ej.: RBAC vs ABAC, scopes vs permisos granulares, auditoría detallada vs coste/privacidad, cifrado en reposo MVP vs fase 2, estrategia rotación.
  1. Clasificación: requerido vs recomendado En el documento, marcar explícitamente cada control como:
  • REQUERIDO (evidenciado o exigido por FR/NFR/ADR)
  • RECOMENDADO (baseline sugerido)
  • TBD/OPENQ (falta decisión o datos)

Read the full file on GitHub · 155 lines

Changes

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.

  1. 11d ago First seen · 155 lines · 64 tokens per session scan A 458ae2629060

Subscribe to this mod's changes

spec-kit-security-baseline is a skill published in the GitHub repository lksnext-ai-lab/spec-kit-template (5 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 64 tokens to every session and 1,820 once invoked, about $0.0003 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 2026-08-31.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens