Percolate Kb logo

Percolate Kb

Organization
hraness
percolate-kb

Review a hraness/wordcell Markdown vault for recurring ideas and missing structural connections, then promote evidence-backed concepts and typed relationships with the Wordcell CLI. Use after materially adding or revising notes, when organizing an accumulated vault, or when an agent needs to turn repeated tags and prose references into an explicit queryable knowledge graph.

Overview

Publisherhraness
Repositoryslopcamera
Skill namepercolate-kb
Stars
7
Forks
1
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Percolate Kb 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/hraness/slopcamera/tree/main/.agents/skills/percolate-kb
  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/hraness/slopcamera.git /tmp/slopcamera
mkdir -p .claude/skills
cp -r /tmp/slopcamera/.agents/skills/percolate-kb .claude/skills/percolate-kb
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Percolate Kb 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 Percolate Kb 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 Percolate Kb 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.

Percolate concepts and relationships

Keep the graph authored, local, and reviewable. wordcell percolate proposes candidates from deterministic evidence; it never changes a note. Backlinks, graph reports, and QMD results are derived views, while Markdown remains the authority.

Locate the vault

  • Resolve <vault> to the directory containing its authored or managed index.md front door.
  • Read the applicable repository and vault instructions before editing.
  • Pass the resolved path to every --root.
  • Identify the note or small neighborhood changed by the current task. Prefer a bounded review to a vault-wide cleanup during parallel work.

Inspect candidates

Run percolation on the changed note when possible:

sh
wordcell percolate notes/example --root "$KB_ROOT" --limit 25 --json

Run it without a note only when reviewing the whole vault:

sh
wordcell percolate --root "$KB_ROOT" --min-support 2 --limit 50 --json

Treat each result as a prompt to open the cited notes and read the relevant prose. Candidate kinds may include:

  • a recurring tag with no maintained type: concept note;
  • notes that share a concept or tag but have no explicit relationship;
  • an exact title or alias mentioned without a contextual link;
  • a self, reciprocal, malformed, broken, or ambiguous authored relationship.

For missing relationships, support counts independent shared tags or concept neighbors; the evidence array shows the participating notes. The default minimum of two therefore requires two shared signals, not merely both endpoints of one tag match. Other candidate kinds count their natural unit: supporting notes, mention occurrences, or authored hygiene evidence.

For a missing concept, use suggestedId. When collidesWith is non-null, the natural ID is already an ordinary note, so KB chooses an unoccupied *-concept ID. Read the occupied note before deciding whether to create the suggested concept or promote and improve the existing note instead.

Semantic search may help discover evidence, but similarity is never enough to author an edge.

Promote durable concepts

Create a concept only when the idea is likely to be reused and its definition can be stated from the source material:

sh
wordcell note create notes/local-first \
  --root "$KB_ROOT" \
  --title "Local-first" \
  --type concept \
  --tag architecture \
  --body '# Local-first

A concise reviewed definition grounded in the cited notes.'

Write a concise definition and cite or link the notes that establish it. Concepts are ordinary Markdown notes, so they can carry aliases, evidence, context, and their own outbound relationships. Do not create a concept merely to mirror every tag.

After promotion, rerun percolation on the cited non-concept notes. The new concept may support relationships among its neighbors even when a run scoped to the concept itself has no candidate:

sh
wordcell percolate notes/write-path --root "$KB_ROOT" --limit 25 --json

Author typed relationships

Add a relationship from the note that owns the assertion:

sh
wordcell relation add notes/write-path supports notes/durable-agent-memory \
  --root "$KB_ROOT"

Use a specific lower-kebab-case predicate. Targets are exact vault-root note IDs without .md. Ground the assertion in nearby prose or evidence; the frontmatter is an indexable statement, not a substitute for explanation.

List or remove relationships without editing reciprocal notes:

sh
wordcell relation list notes/write-path --root "$KB_ROOT" --json
wordcell relation remove notes/write-path supports notes/durable-agent-memory \
  --root "$KB_ROOT"

Never write inverse edges, generated backlinks, inferred transitive relationships, or semantic-search scores into Markdown. Those are derived views.

Query before concluding

Use exact structure to verify that the promoted graph says what the prose says:

sh
wordcell links notes/write-path --root "$KB_ROOT" --direction both --depth 2 --json
wordcell relation list notes/write-path --root "$KB_ROOT" --json
wordcell graph --root "$KB_ROOT" --json

Prefer the note-scoped commands first. Use the whole-vault graph only when the question spans several neighborhoods, and confirm returned IDs against their Markdown notes before reporting a conclusion.

Finish under the vault's catalog mode

When working alone or integrating several lanes:

sh
wordcell refresh --root "$KB_ROOT"
wordcell check --root "$KB_ROOT"

When several agents are editing different notes in a managed-catalog vault, each lane should validate authored structure and local attachments without rewriting the shared catalog:

sh
wordcell check --root "$KB_ROOT" --no-catalog

The integrating agent runs one final managed refresh and normal check. In an authored-catalog vault, refresh and check leave the front door untouched, while wordcell catalog --root "$KB_ROOT" renders an exhaustive disposable inventory. Resolve same-note Git conflicts from the prose and evidence; do not accept one side's frontmatter mechanically.

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 Percolate Kb AI skill do?

Review a hraness/wordcell Markdown vault for recurring ideas and missing structural connections, then promote evidence-backed concepts and typed relationships with the Wordcell CLI. Use after materially adding or revising notes, when organizing an accumulated vault, or when an agent needs to turn repeated tags and prose references into an explicit queryable knowledge graph.

Why use Percolate Kb on TypingMind?

Because you install it once and use it with any model. Percolate Kb 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 Percolate Kb in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hraness/slopcamera/tree/main/.agents/skills/percolate-kb. 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 Percolate Kb?

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 Percolate Kb?

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

Is the Percolate Kb AI skill free?

Yes. It is published on GitHub by hraness 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇