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 skills/ibrain-bvba/gutt-claude-code-plugin/memory-retrievalnpx skills add iBrain-BVBA/gutt-claude-code-plugin --skill memory-retrievalgit clone --depth 1 https://github.com/iBrain-BVBA/gutt-claude-code-pluginWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ibrain-bvba/gutt-claude-code-plugin/memory-retrieval)<a href="https://agentmods.dev/skills/ibrain-bvba/gutt-claude-code-plugin/memory-retrieval"><img src="https://agentmods.dev/badge/skills/ibrain-bvba/gutt-claude-code-plugin/memory-retrieval.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00052 | $0.00193 |
| Opus 5 | $0.00026 | $0.00097 |
| Sonnet 5 | $0.00010 | $0.00039 |
| Haiku 4.5 | $0.00005 | $0.00019 |
Grade A, and why
memory-retrieval 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 4d 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.
What it actually says
Memory Retrieval (deprecated → use memory-search)
This skill has been superseded by memory-search, which teaches the
shallow-first ladder, summary-first reading, and token discipline for the gutt
knowledge graph (GP-856).
The trigger phrases that used to auto-fire this skill now belong to
memory-search, so it no longer activates on its own — it stays only as an
alias for the existing /gutt-pro:memory-retrieval command.
Use memory-search for all memory reads. For multi-hop traversal see
graph-traversal; for writing memory see memory-capture.
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.
- 4d ago First seen · 18 lines · 52 tokens per session scan A 47215f52a36d
memory-retrieval is a skill published in the GitHub repository iBrain-BVBA/gutt-claude-code-plugin (5 stars, last pushed 2d ago), licensed MIT. It adds 52 tokens to every session and 193 once invoked, about $0.0003 per session on Opus 5. 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 2026-08-31.
Other skills, from other repositories
retrieval-practice-generator
Generate low-stakes retrieval-practice questions with grounded answer notes and implementation guidance. Use for quiz starters, revision activities, delayed recall, misconception checks, or adapting recall difficulty.
generating-mod-envs
Generates and reviews mod learning env JSON files for Letta Code local mods. Use when asked to teach, learn, or optimize a mod behavior; create, draft, validate, improve, or explain envs for /mods learn --env; or design evaluation scenarios, memory fixtures, requiredResultMarkers, requiredTraceMarkers, negative…
aws-secrets-manager
Secret safety for AWS Secrets Manager, secret management, credentials, API keys, tokens, and passwords. Prevents AI agents from directly fetching secret values and teaches runtime dynamic references with asm-exec so plaintext never enters the LLM context window.
ai-memory-learning-maintenance
Use this skill for any ai-memory knowledge-base maintenance request: consolidating observations, reviewing session lessons, proposing durable learnings, auditing or linting the wiki, finding contradictions, pruning stale memory, or running auto-improvement. Trigger by semantic intent rather than exact wording.
self-improve-with-tinyfish
Enables Hermes to create new reusable skills for itself by researching live web sources with TinyFish Search and Fetch, analyzing source coverage, writing SKILL.md files, and installing them into Hermes memory. Use when the user asks Hermes to learn, teach itself, upgrade itself, or save a reusable capability.
cao-learning
Report task outcomes and distill lessons so the team improves across runs — reportoutcome after each unit of work, retrospector handoffs at natural boundaries, and applying injected lessons. Use in workflows that run repeatedly over similar work items. Requires memory.learningenabled; degrade silently when the tools…