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Blog Flow

CommunityPopular
AgriciDaniel
blog-flow

FLOW framework integration for bloggers. Evidence-led content workflow using the Find, Optimize, Win loop with stage-specific AI prompts from the FLOW knowledge base (30 blog-applicable prompts, CC BY 4.0). Use when user says "FLOW", "FLOW framework", "blog flow", "evidence-led blogging", "find optimize win", or wants stage-specific blog prompts.

Overview

PublisherAgriciDaniel
Repositoryclaude-blog
Skill nameblog-flow
Stars
2.2K
Forks
362
Bundled files
34
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • 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.

  • 34 bundled files

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

  • Open source

    Published by AgriciDaniel on GitHub. Read the source before you install it.

Installation

Install the Blog Flow 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.
  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/AgriciDaniel/claude-blog.git /tmp/claude-blog
mkdir -p .claude/skills
cp -r /tmp/claude-blog/skills/blog-flow .claude/skills/blog-flow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Blog Flow 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 Blog Flow 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 Blog Flow 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.

FLOW Framework for Bloggers (Find, Optimize, Win)

Runs FLOW Find/Optimize/Win prompts for a blog topic or URL, turning query data, source notes, and page evidence into structured decisions instead of improvised prompts.

Framework and prompts (c) Daniel Agrici, CC BY 4.0. Source: github.com/AgriciDaniel/flow

FLOW is an evidence-led operating model for retrieval, citation, and conversion workflows. Claude Blog integrates the FLOW prompt library so writers can turn query data, source notes, and page evidence into structured decisions instead of improvised prompts.

This skill exposes the three blog-relevant stages (Find, Optimize, Win) and keeps the single Leverage prompt available through the prompts index. The local-SEO prompts (GBP, citations, local audits) are intentionally excluded because they target brick-and-mortar work, not blogs.

Runtime context. Load references/flow-framework.md on every /blog flow activation. Load prompt files on demand only, scoped to the stage the user requests.

Commands

CommandWhat it does
/blog flowShow FLOW overview and stage menu
/blog flow find [topic|url]Find-stage: keyword discovery, intent mapping, gap analysis (5 prompts)
/blog flow optimize [url]Optimize-stage: select 2 to 3 most relevant prompts of 21 based on context
/blog flow win [url]Win-stage: BOFU, conversion, dual-surface scorecard (3 prompts)
/blog flow promptsFull index of all 30 blog-applicable prompts (Find, Leverage, Optimize, Win)
/blog flow syncPull latest prompt files from github.com/AgriciDaniel/flow

The single Leverage prompt (off-site authority) is reachable through /blog flow prompts and is not promoted to a top-level command, since most blog workflows route off-site work elsewhere.

Orchestration Logic

On /blog flow (no sub-command)

  1. Read references/flow-framework.md.
  2. Show the FLOW stage overview with a one-line description of each stage.
  3. Ask the user which stage matches their current situation.

On /blog flow find [topic|url]

  1. Read all files in references/prompts/find/.
  2. Apply each prompt to the topic or URL, capturing demand and intent signals.
  3. Cross-reference: "For deeper briefs and outlines, see /blog brief <topic>, /blog outline <topic>, and /blog cannibalization to detect overlap with existing posts."

On /blog flow optimize [url]

  1. Read the file names in references/prompts/optimize/.
  2. Read prior context (target URL, niche, any prior skill output in this conversation, scoring deltas from /blog analyze).
  3. Select 2 to 3 most relevant prompts, then load only those files.
  4. Apply the selected prompts; note that the rest are accessible via /blog flow prompts.
  5. Cross-reference: "For deeper rewrites and validation, see /blog rewrite <file>, /blog seo-check <file>, /blog geo <file>, /blog schema <file>, and /blog factcheck <file>."

On /blog flow win [url]

  1. Read all files in references/prompts/win/.
  2. Apply each prompt to the URL's conversion and BOFU context.
  3. Cross-reference: "For repurposing, full-site health, and quality scoring, see /blog repurpose <file>, /blog audit, and /blog analyze <file>."

On /blog flow prompts

  1. Read references/prompts/README.md.
  2. Display the full index: 30 prompts grouped by stage (Find, Leverage, Optimize, Win) with name and trigger conditions.
  3. State that local-SEO prompts are excluded by design; point users to claude-seo (/seo flow local) if they need them.

On /blog flow sync

  1. Run: python3 scripts/sync_flow.py.
  2. Display the JSON summary (files added, updated, unchanged).
  3. Show the attribution notice after the sync completes.

Context Matching (Optimize stage)

The optimize stage has 21 prompts. Dumping all 21 is noise. Select by priority:

  1. Niche (SaaS or B2B blog leans on-page plus technical; lifestyle leans freshness plus E-E-A-T; publisher leans authority plus citations).
  2. Prior skill output (/blog analyze E-E-A-T gap routes to authority prompts; /blog seo-check failures route to on-page prompts; /blog geo gaps route to extraction-format prompts).
  3. URL signals (commercial pages need conversion prompts; informational posts need freshness plus answer-first prompts).

Always surface exactly 2 to 3 prompts. State which prompts you chose and why.

Reference Files

Load on demand. Do NOT load all at startup.

  • references/flow-framework.md. FLOW operating model. Load on every /blog flow activation.
  • references/bibliography.md. Evidence sources. Load when citing studies or statistics.
  • references/prompts/README.md. Prompt index. Load for /blog flow prompts.
  • references/prompts/find/. 5 prompts. Load for /blog flow find.
  • references/prompts/leverage/. 1 prompt. Load only when surfaced through /blog flow prompts.
  • references/prompts/optimize/. 21 prompts. Load selectively for /blog flow optimize.
  • references/prompts/win/. 3 prompts. Load for /blog flow win.

If references/ is missing, instruct the user to run /blog flow sync first.

Sync Script

scripts/sync_flow.py pulls prompt files from github.com/AgriciDaniel/flow and writes them under skills/blog-flow/references/. Stdlib only, HTTPS only, host-allowlisted to api.github.com, 5 MB response cap, atomic writes, path-traversal guarded.

Modes:

  • python3 scripts/sync_flow.py. Sync the latest version of every blog-relevant stage to disk and refresh the lockfile.
  • python3 scripts/sync_flow.py --dry-run. Report planned changes without writing.
  • python3 scripts/sync_flow.py --ref <sha>. Pin fetches to a specific FLOW commit SHA for reproducible installs.

The lockfile lives at skills/blog-flow/references/flow-prompts.lock and uses sha256sum-compatible format. Drift between the on-disk content and the lockfile is reported on every sync run.

The script syncs only blog-applicable stages (find, leverage, optimize, win). The local stage is intentionally skipped to keep the references directory aligned with the skill's surface area.

GitHub API calls are anonymous by default. If GITHUB_TOKEN is set in the environment, or gh auth token returns a token after a 403 response, the script retries the request with that token. No tokens are written to disk.

Attribution

Every /blog flow activation (any sub-command) outputs before analysis:

Framework and prompts (c) Daniel Agrici, CC BY 4.0. Source: github.com/AgriciDaniel/flow

Do not omit or modify the attribution. Synced files also carry an HTML comment license header injected by the sync script.

Error Handling

ScenarioAction
references/flow-framework.md missing"FLOW reference files not synced. Run: /blog flow sync."
Prompt file missing"Run /blog flow sync to pull the latest prompts from the FLOW repo."
sync_flow.py network errorDisplay the script's stderr. Check rate limits with gh api rate_limit if gh is installed.
sync_flow.py 403 after retrySet GITHUB_TOKEN or run gh auth login, then retry.
Path-traversal abortThe sync target tried to escape the references directory. Inspect the upstream repo and pin to a known-good --ref.

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 Blog Flow AI skill do?

FLOW framework integration for bloggers. Evidence-led content workflow using the Find, Optimize, Win loop with stage-specific AI prompts from the FLOW knowledge base (30 blog-applicable prompts, CC BY 4.0). Use when user says "FLOW", "FLOW framework", "blog flow", "evidence-led blogging", "find optimize win", or wants stage-specific blog prompts.

Why use Blog Flow on TypingMind?

Because you install it once and use it with any model. Blog Flow 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 Blog Flow in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AgriciDaniel/claude-blog/tree/main/skills/blog-flow. 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 Blog Flow?

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 Blog Flow?

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

Is the Blog Flow AI skill free?

Yes. It is published on GitHub by AgriciDaniel 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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