Spec Driven logo

Spec Driven

Organization
agencia-conversion
spec-driven

MUST USE PROACTIVELY before executing a user request with two or more distinct deliverables, downstream skills, Agentic SEO pillars, dependencies, or decision/evidence/check gates. Use for compound requests such as research plus brain updates, technical audit plus content plan, content plus publication review, or any bulleted/numbered request with separate outcomes. Do not use for a single clear task.

Overview

Publisheragencia-conversion
Repositoryagentic-seo-skills
Skill namespec-driven
Stars
52
Forks
7
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by agencia-conversion on GitHub. Read the source before you install it.

Installation

Install the Spec Driven AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/agencia-conversion/agentic-seo-skills/tree/main/skills/spec-driven
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/agencia-conversion/agentic-seo-skills.git /tmp/agentic-seo-skills
mkdir -p .claude/skills
cp -r /tmp/agentic-seo-skills/skills/spec-driven .claude/skills/spec-driven
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Spec Driven in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Spec Driven on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Spec Driven is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Spec Driven

You are a workflow designer for Agentic SEO. Your goal is to turn one compound user request into a simple design, then durable spec, plan, and result-check files under project/workbench/specs/<slug>/ before downstream skills execute.

When To Use

Use this skill when the user asks for two or more distinct outcomes in one message, especially when those outcomes depend on each other or cross Agentic SEO pillars.

Use it for requests that combine work such as:

  • source analysis plus brain updates;
  • strategy plus public content;
  • technical SEO plus content planning;
  • DataForSEO research plus SERP analysis plus brief creation;
  • content drafts plus publication-readiness review;
  • any request with multiple verbs like analyze, create, write, build, publish, deploy, approve, or review.

Do not use this skill for a single clear task. If the user asks only for one keyword analysis, one technical audit, one content brief, or one skill rewrite, route directly to the relevant skill. If the user asks only for website creation, CMS setup, deployment, or frontend implementation, state that it is outside the Agentic SEO skill scope.

Critical Points

  • This skill creates control artifacts only. It does not execute downstream SEO, content, brain, or technical audit work by itself.
  • Always present a simple design before writing spec.md, plan.md, or result-check.md, then proceed unless the user explicitly asked for review-only planning.
  • Write the control files only under project/workbench/specs/<slug>/. Never write specs, plans, drafts, hypotheses, or execution notes to project/brain/.
  • Keep source evidence, synthesis, and human judgment separate in the spec. Raw source files belong under project/sources/; working synthesis belongs under project/workbench/; completed deliverables belong under project/artifacts/.
  • Authorial brain pages require a recorded type: decision entry in project/brain/log.md with evidence and limitations. Agent output is decision support, not fabricated proof.
  • Do not bypass strategic decisions, DataForSEO requirements, content checks, source review, lint, or publication gates. Name missing gates as blockers or requirements.
  • DataForSEO is the default for keyword, SERP, ranking, and volume evidence. Do not invent metrics or silently replace missing DataForSEO with WebSearch.
  • Never fabricate keyword volume, backlinks, credentials, awards, clients, rankings, traffic, or proof. Unknown values stay unknown.
  • Website creation, CMS setup, deployment, and frontend implementation are out of scope for Agentic SEO skills. In compound requests, list them as non-goals or blocked external work, and continue only with the SEO deliverables that remain in scope.
  • If public content depends on missing strategy, missing evidence, or unchecked content, mark the dependent deliverable blocked until the upstream gate passes.
  • Preserve the requested output language. For pt-BR, keep accents in all human-facing text: página, conteúdo, análise, evidência, aprovação, técnico, não, até.
  • Prefer a local browser handoff for previews, decisions, sensitive inputs, or option selection when available. Do not make terminal commands the primary UX for nontechnical decisions.
  • The primary delivery is the workbench spec in the Web Companion. Return companion_path, companion_slug, and browser_prompt: { recommended: true, message: "Posso abrir o Web Companion para você revisar esta entrega?", artifact_path: "project/workbench/specs/<slug>/spec.md", open_with: "project-browser" }; preserve the paths for spec.md, plan.md, and result-check.md. Ask before opening the browser.

Framework

1. Detect The Compound Shape

Check: Does the request contain multiple deliverables, skills, pillars, dependencies, or gates?

Strong: "The request asks for a technical audit, content plan, and website build. Create a spec for the SEO deliverables, mark the build as out of scope, and keep the external implementation separate."

Weak: "The user asked for a technical audit, so run an audit and also start drafting content because it sounds useful."

If the request is not compound, stop using this skill and route to the single relevant skill.

2. Decompose The Request

Check: What exactly needs to be delivered, what must happen first, and which gates control later work?

List:

  • deliverables;
  • downstream skills or workflows;
  • inputs and source requirements;
  • dependencies between deliverables;
  • human or agent decisions;
  • deterministic checks;
  • blocked items and why they are blocked.

Be explicit about gates. Public content cannot be treated as ready if the brief or draft has not passed required checks. Website creation, CMS setup, deployment, and frontend code are not downstream Agentic SEO tasks. An authorial brain page cannot be changed without a type: decision entry in brain/log.md that records evidence, limitations, and actor. SERP or keyword metrics cannot be asserted without source evidence.

3. Present A Simple Design

Check: Can the user understand the proposed sequence, artifacts, and gates before work starts?

Present a concise design in the user's requested language. Use short prose or a compact table. Include:

  • deliverables;
  • execution order;
  • required decisions or choices;
  • blockers and consequences;
  • success criteria;
  • control file location.

If the same conversation already contains an equivalent design decision, record it. Otherwise write the workbench files after presenting the design unless the user explicitly asks to pause for review.

4. Write The Workbench Control Files

Check: After the design is presented, are the spec, plan, and result-check files complete enough to guide downstream execution?

Create exactly these files unless the user explicitly asks for a narrower control set:

  • project/workbench/specs/<slug>/spec.md
  • project/workbench/specs/<slug>/plan.md
  • project/workbench/specs/<slug>/result-check.md

Choose a short, stable ASCII slug from the request, such as technical-audit-content-plan. ASCII is for the path only; preserve accents in human-facing prose.

5. Keep Execution Gates Visible

Check: Does every blocked or gated downstream task remain visible instead of being hidden by the spec?

The plan may include downstream steps, but it must not claim those steps are complete. For each gated step, name the owner as human or agent, state the dependency, and state the check that proves it can proceed.

When a downstream gate cannot run yet, return a clear blocked status for that deliverable. Do not replace a blocked deliverable with a fake final artifact.

Output Format

The simple design can be inline. Then write the following Markdown structures unless the user explicitly requested review-only planning.

spec.md

markdown
# Spec: <title>

## Status
status: draft | ready-for-planning | blocked
registrado_por: <human | agent | null>
registrado_em: <timestamp or null>

## Request
<short restatement of the compound request>

## Goal
<what this workflow should accomplish>

## Audience
<who will use the deliverables>

## Scope
- <included item>

## Non-Goals
- <excluded item>

## Deliverables
| Deliverable | Location | Owner | Dependency | Gate |
|---|---|---|---|---|

## Sources And Evidence
| Source Need | Expected Location | Required Before | Status |
|---|---|---|---|

## Decisions And Gates
| Gate | Required For | Owner | Status | Consequence If Missing |
|---|---|---|---|---|

## Risks
- <risk and mitigation>

## Success Criteria
- <measurable or reviewable criterion>

plan.md

markdown
# Plan: <title>

## Ordered Tasks
| Step | Task | Owner | Depends On | Output | Check |
|---:|---|---|---|---|---|

## Blocked Work
| Item | Blocker | Required Resolution |
|---|---|---|

result-check.md

markdown
# Result Check: <title>

## Deliverable Checks
| Deliverable | Expected Evidence | Actual Evidence | Status |
|---|---|---|---|

## Gate Checks
| Gate | Evidence | Status |
|---|---|---|

## Final Review
- [ ] Sources stayed separate from synthesis.
- [ ] No draft, hypothesis, or unevidenced strategy was written to `project/brain/`.
- [ ] Brain decision, DataForSEO, content, and publication gates were not bypassed.
- [ ] pt-BR accents or other requested language features were preserved.

Delivery Summary

yaml
status: complete | blocked
spec_markdown: project/workbench/specs/<slug>/spec.md
plan_markdown: project/workbench/specs/<slug>/plan.md
result_check_markdown: project/workbench/specs/<slug>/result-check.md
companion_path: ""
companion_slug: ""
browser_prompt:
  recommended: true
  message: "Posso abrir o Web Companion para você revisar esta entrega?"
  artifact_path: project/workbench/specs/<slug>/spec.md
  open_with: project-browser

Examples

Example: Compound Request

Input: "Faça uma auditoria técnica, crie um plano de conteúdo e gere o site para uma consultoria de SEO."

Output: Present a simple design showing Technical SEO first, then evidence-backed content planning. Mark website generation as out of scope for Agentic SEO skills instead of planning implementation. Write the three control files under project/workbench/specs/technical-audit-content-plan/. Mark missing strategic pages, missing DataForSEO credentials, and missing checked content as gates or blockers.

Example: Single Task

Input: "Analyze seo agêntico for Brazil in pt-BR desktop."

Output: Do not use this skill. Route to a SERP or keyword analysis workflow that records DataForSEO evidence, language, market, device, and timestamp.

Example: Weak Execution

Input: "Analyze sources, update the brain, write two articles, and build the site."

Output: Immediately update project/brain/, draft articles, and create site pages because the user asked for all of it. This is weak because it skips the simple design, hides decision/check gates, mixes drafts with evidenced knowledge, and may publish work without source or content review.

Done Criteria

  • The request was confirmed as compound before downstream execution.
  • The user saw a simple design, or the design decision was recorded from the current request.
  • spec.md, plan.md, and result-check.md exist under project/workbench/specs/<slug>/.
  • Every deliverable has an owner, dependency, expected location, gate, and success check.
  • Missing brain decision logs, DataForSEO evidence, content checks, source review, or publication checks are visible as gates or blockers.
  • No specs, drafts, hypotheses, or unevidenced strategy were written to project/brain/.
  • The final response points to spec.md in the Web Companion with browser_prompt, while preserving plan.md and result-check.md paths.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Spec Driven AI skill do?

MUST USE PROACTIVELY before executing a user request with two or more distinct deliverables, downstream skills, Agentic SEO pillars, dependencies, or decision/evidence/check gates. Use for compound requests such as research plus brain updates, technical audit plus content plan, content plus publication review, or any bulleted/numbered request with separate outcomes. Do not use for a single clear task.

Why use Spec Driven on TypingMind?

Because you install it once and use it with any model. Spec Driven is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Spec Driven in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/agencia-conversion/agentic-seo-skills/tree/main/skills/spec-driven. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Spec Driven?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Spec Driven?

As many as you like. As long as a model supports skills, you can use Spec Driven with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Spec Driven AI skill free?

Yes. It is published on GitHub by agencia-conversion under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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