Page Report logo

Page Report

Organization
agencia-conversion
page-report

Shared report-page contract for Agentic SEO modules that render editable, human-first Markdown reports in the Web Companion.

Overview

Publisheragencia-conversion
Repositoryagentic-seo-skills
Skill namepage-report
Stars
52
Forks
7
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Page Report 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/page-report
  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/page-report .claude/skills/page-report
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Page Report 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 Page Report 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 Page Report 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.

Page Report

Use this skill whenever an Agentic SEO workflow generates a report page for the Web Companion. It is a shared contract, not a standalone SEO module.

When To Use

Use page-report from report-producing skills and runtime code when creating or reviewing report.md for Technical SEO, Internal Links, SEO Analysis, Keyword Research, SERP Extract, Backlink Analysis, Topic Cluster, E-E-A-T, or Competitive Analysis.

Do not use this skill for public content drafts, approval handoffs, inline chat answers, raw provider captures, or authorial Brain updates that are not report pages.

Critical Points

  • The canonical report path is project/analyses/<module>/<run-slug>/report.md.
  • The report is the editable human presentation layer. Raw evidence, normalized YAML/JSON, logs, and operational artifacts stay in sources/, audits/, keywords/, clusters/, eeat/, or workbench/.
  • Frontmatter must include title, slug, report_type, generated_at, status, source_artifact, and summary; include score only when the module has a real score.
  • Do not write # <title> in the Markdown body. The Companion renders the title from frontmatter.
  • Use the project language from project/.agentic-seo/project.json.language unless a command has an explicit language override. Complete copy is required for pt-BR and en.
  • New visual fences must be YAML with version: 1: agentic-kpis, agentic-chart, and agentic-table. JSON fences are legacy read compatibility only.
  • Tables must use stable lower-snake ASCII key values. User-facing label values may be translated or edited without changing keys.
  • Calculation tables must mark columns with role: weight, role: points, and role: loss so score recalculation survives label edits.
  • Each report-producing module has skills/<module>/contract.yaml and templates/analyses/<module>/report-skeleton.md; keep required H2 sections and table keys from the skeleton.
  • Before declaring a manual report done, run npm run validate:reports -- --module=<module> --slug=<run-slug> or use scripts/lib/report-writer.mjs, which refuses invalid reports before writing.
  • Never paste raw provider JSON, object arrays, [object Object], internal IDs, or snake_case evidence into the visual body. Convert evidence into natural language and readable tables.
  • Format numbers per the project language. The canonical source is shared/locale.mjs (getProjectLanguage, formatNumber, formatPercent, canonicalKeyword). Skills either ship locale-formatted strings in agentic-table/agentic-kpis cells, or leave numbers raw for the Companion (useI18n().formatNumber / formatPercent) to format on render. Percent-bearing columns use the _pct key suffix on the 0..100 scale.
  • Always return report_md and browser_prompt: { recommended: true, message: "Posso abrir o Web Companion para você ver a análise?", open_with: "project-browser" }.
  • Register the generated report in project/brain/log.md as type: decision unless a more specific log type is required.

Framework

1. Build The Source Artifact

Write the module's authoritative machine-readable artifact first. It may be YAML or JSON, but it must remain outside project/analyses/ and be referenced by frontmatter source_artifact.

2. Render The Human Page

Start with an executive reading. Then add module-specific sections, visual blocks, and human-readable appendices. Keep technical depth, but translate it into prose, friendly labels, and compact evidence.

3. Validate The Contract

Before returning the result, verify: canonical path, frontmatter, no H1 body title, no HTML default, YAML visual fences, no raw JSON/object dumps, readable evidence, source separation, and log entry.

4. Review The Result

For QA, generate one report per module with fixtures/offline data. A report is satisfactory only when it scores at least 4/5 for clarity, evidence separation, visual usefulness, actionability, and localization, with no category below 3/5.

Output Format

The report Markdown must have this shape:

markdown
---
title: "Human report title"
slug: "stable-run-slug"
report_type: "technical-seo"
generated_at: "2026-05-23T00:00:00.000Z"
status: "ready"
source_artifact: "audits/example/report.yaml"
summary: "One-sentence executive summary."
score: "92"
---

Executive summary prose without a duplicate H1.

```agentic-kpis
version: 1
items:
  - label: Score
    value: 92/100
```

Use only supported report_type values: technical-seo, internal-links, seo-analysis, keyword-research, serp-extract, backlink-analysis, topic-cluster, eeat, and competitive-analysis.

Done Criteria

  • report.md exists under the canonical module path and is returned as report_md.
  • source_artifact points to the normalized technical source.
  • The Companion can list, open, edit, save, and roundtrip the page.
  • Visual fences parse as YAML version: 1.
  • No visible JSON/object dumps or duplicated title appear in the report body.
  • The report is registered in brain/log.md.

Frequently asked questions

What does the Page Report AI skill do?

Shared report-page contract for Agentic SEO modules that render editable, human-first Markdown reports in the Web Companion.

Why use Page Report on TypingMind?

Because you install it once and use it with any model. Page Report 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 Page Report in TypingMind?

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

Which AI models can use Page Report?

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 Page Report?

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

Is the Page Report 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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