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Recall

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oliver-kriska
recall

Recall prior work from past sessions — how a bug was fixed, what was decided, where a pattern lives. Use when asked 'have we done this before' or 'how did I fix X' in Elixir/Phoenix work. ccrider MCP when available, else git + solution docs.

Overview

Publisheroliver-kriska
Repositoryclaude-elixir-phoenix
Skill namerecall
Stars
555
Forks
40
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 oliver-kriska on GitHub. Read the source before you install it.

Installation

Install the Recall 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/oliver-kriska/claude-elixir-phoenix.git /tmp/claude-elixir-phoenix
mkdir -p .claude/skills
cp -r /tmp/claude-elixir-phoenix/plugins/elixir-phoenix/skills/recall .claude/skills/recall
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Recall 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 Recall 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 Recall 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.

Recall — Session & History Archaeology

Search three evidence layers, cheapest first, to answer "have we solved this before?". Stop at the first layer that answers the question.

Usage

/phx:recall how did we fix the LiveView form that saved silently?
/phx:recall what did we decide about the billing context boundaries?
/phx:recall which library did we pick for rate limiting and why?

Iron Laws

  1. Cheapest layer first — solution docs (local grep) → git history → session transcripts. Don't fetch sessions when a solution doc answers
  2. ONE ccrider fetch = ONE subagentget_session_messages responses are 3–15KB each. Spawn a subagent per session that writes a summary file and exits; NEVER batch multiple session fetches into one context
  3. Cite the evidence — every claim names its source (solution doc path, commit hash, or session date + match snippet). No vague "we did this once"
  4. Graceful degradation, stated plainly — if ccrider MCP is absent, say so once and use the fallback layers; never error out

Workflow

Layer 1: Compound Solution Docs (always)

Run Grep with keywords from the question over .claude/solutions/. Treat a hit here as the best answer — it was written for exactly this purpose. Present it and stop unless the user wants more.

Layer 2: Git Archaeology (always available)

bash
git log --oneline --grep="{keyword}" -i -20      # commit messages
git log -S "{code-symbol}" --oneline -10          # when a symbol changed
git log --follow --oneline -10 -- {file}          # one file's history
git show {hash} --stat                            # inspect a candidate

Use -S (pickaxe) when the question names code; --grep when it names intent. Show matching commits with one-line context each.

Layer 3: Session Transcripts (ccrider MCP, gated)

Check for mcp__ccrider__* tools (load via ToolSearch if deferred).

If absent: report "ccrider MCP not connected — answered from solution docs + git history" and stop after Layers 1–2.

If present:

  1. mcp__ccrider__search_sessions with the question's key phrases — returns ranked hits with session IDs and snippets
  2. Present the top 3–5 hits (date, snippet) and pick the most relevant (or ask, if genuinely ambiguous)
  3. Per selected session, spawn ONE subagent: "Fetch session {id} via mcp__ccrider__get_session_messages, extract only what answers '{question}', write ≤30 lines to .claude/recall/{id}.md" (Iron Law 2)
  4. Read the summary files, synthesize the answer with citations

Step 4: Answer + Compound

Present the answer with its evidence trail. If the recalled knowledge was NOT already in .claude/solutions/, offer /phx:compound so the next recall stops at Layer 1.

Integration

text
"have we done this before?" → /phx:recall
   Layer 1 .claude/solutions/ ──hit──► answer + cite
   Layer 2 git log --grep/-S  ──hit──► answer + cite
   Layer 3 ccrider sessions (gated) ─► answer + cite → offer /phx:compound

References

  • ${CLAUDE_SKILL_DIR}/references/archaeology-patterns.md — git pickaxe recipes, ccrider query patterns, subagent prompt template

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 Recall AI skill do?

Recall prior work from past sessions — how a bug was fixed, what was decided, where a pattern lives. Use when asked 'have we done this before' or 'how did I fix X' in Elixir/Phoenix work. ccrider MCP when available, else git + solution docs.

Why use Recall on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/oliver-kriska/claude-elixir-phoenix/tree/main/plugins/elixir-phoenix/skills/recall. 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 Recall?

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 Recall?

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

Is the Recall AI skill free?

Yes. It is published on GitHub by oliver-kriska 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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