o9k-recall
Dispatch a Haiku sub-agent to search hmem for relevant memories. Sub-agent returns matching entries as ID + one-line summary. Main agent context stays clean.
Best use case
o9k-recall is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Dispatch a Haiku sub-agent to search hmem for relevant memories. Sub-agent returns matching entries as ID + one-line summary. Main agent context stays clean.
Teams using o9k-recall should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/o9k-recall/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How o9k-recall Compares
| Feature / Agent | o9k-recall | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Dispatch a Haiku sub-agent to search hmem for relevant memories. Sub-agent returns matching entries as ID + one-line summary. Main agent context stays clean.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# o9k-recall ## TRIGGER Use when: - You need to find past decisions, lessons, or session context from hmem - You don't know the exact node ID - You want to keep the search work out of the main context ## STEP 1: Define the search query Before dispatching, write down: - QUERY: what to search for (keywords, concept, or question) - TYPE: what kind of memory (L-Entry = lesson, O-Entry = session, P-Entry = project, any) ## STEP 2: Dispatch Haiku sub-agent Send the sub-agent exactly this prompt (fill in QUERY and TYPE): --- Search hmem for: <QUERY> Memory type filter: <TYPE or "any"> Use these tools in order: 1. search_memory(query: "<QUERY>") — keyword search 2. find_related(id: "<active P-Entry ID>", query: "<QUERY>") — semantic search Collect all results. Deduplicate by ID. Return ONLY this format: [RECALL RESULTS] <ID> | <one-line summary of what this entry contains> <ID> | <one-line summary> ... [/RECALL RESULTS] If nothing found: [RECALL RESULTS] none [/RECALL RESULTS] Max 10 results. Most relevant first. IDs exact (e.g., L0042, O0056.3.2, P0048.6). Nothing before [RECALL RESULTS]. Nothing after [/RECALL RESULTS]. No commentary, no explanation. --- ## STEP 3: Use results The main agent receives the [RECALL RESULTS] block. To read a specific entry in full: call read_memory(id: "<ID from results>") To load a project: call load_project(id: "<P-Entry ID from results>") Do NOT load all results at once — pick only what the current question needs.