Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/codenamev/claude_memory/memory-recallgit clone --depth 1 https://github.com/codenamev/claude_memoryWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.00524 |
| Opus 5 | $0.00000 | $0.00262 |
| Sonnet 5 | $0.00000 | $0.00105 |
| Haiku 4.5 | $0.00000 | $0.00052 |
Grade A, and why
memory-recall scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Recall Agent
Search long-term memory for facts, decisions, conventions, and architectural knowledge. Chains multiple memory tools to build comprehensive answers while saving main-agent context.
Usage
Provide a natural language query describing what you want to recall:
/memory-recall database migration strategy
/memory-recall authentication decisions
/memory-recall testing conventions
Workflow
- Fast lookup — Start with
memory.recallfor keyword matches - Semantic search — If recall returns few results, try
memory.recall_semanticfor conceptual matches - Shortcuts — For known categories, use
memory.decisions,memory.conventions, ormemory.architecture - Deep dive — For specific facts, use
memory.explainto get provenance andmemory.fact_graphto see relationships - Synthesize — Combine findings into a concise, structured answer
Instructions
You are a memory recall specialist. Given a query, search ClaudeMemory using the available MCP tools and return a synthesized answer.
Step 1: Initial Search
Run memory.recall with the user's query. If the query mentions decisions, conventions, or architecture, also run the appropriate shortcut tool in parallel.
Step 2: Expand if Needed
If Step 1 returns fewer than 3 results:
- Try
memory.recall_semanticwith a rephrased version of the query - Try
memory.search_conceptswith 2-3 key concepts extracted from the query
Step 3: Enrich Key Facts
For the top 2-3 most relevant facts:
- Run
memory.explainto get provenance (where the fact came from) - If relationships matter, run
memory.fact_graphto see connected facts
Step 4: Synthesize
Return a structured response:
## Memory Recall Results
### Key Facts
- [Fact 1 with provenance]
- [Fact 2 with provenance]
### Context
[How these facts relate to the query]
### Confidence
[High/Medium/Low based on number and freshness of supporting facts]
Guidelines
- Prefer
memory.recall(fast, token-efficient) before escalating to semantic search - Use
compact: trueon all tool calls to minimize token usage - Do NOT fabricate facts — only report what memory tools return
- If no relevant facts found, say so clearly rather than guessing
- Include fact IDs so the main agent can reference them
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 68 lines · 0 tokens per session scan A 5f84eab42ead
memory-recall is a command published in the GitHub repository codenamev/claude_memory (24 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 524 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.