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-capturenpx skills add iBrain-BVBA/gutt-claude-code-plugin --skill memory-capturegit 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-capture)<a href="https://agentmods.dev/skills/ibrain-bvba/gutt-claude-code-plugin/memory-capture"><img src="https://agentmods.dev/badge/skills/ibrain-bvba/gutt-claude-code-plugin/memory-capture.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 | $0.00119 | $0.03289 |
| Opus 5 | $0.00060 | $0.01644 |
| Sonnet 5 | $0.00024 | $0.00658 |
| Haiku 4.5 | $0.00012 | $0.00329 |
Grade A, and why
memory-capture 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 3d 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 — 228 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Capture
How every agent should write to the gutt knowledge graph: classify what you
have, gate it by trust tier, make sure it isn't already there, then write one
self-contained episode. This is the write counterpart to memory-search
and graph-traversal (which only read) — they forward-reference it rather than
restate it. Most captures are cheap; the discipline is in what you write, and
whether you may write it without asking.
Hard rules (non-negotiable — read first)
- Search before you write — and keep the ids of what you found. Run
memory-searchrung 1 first. If the point already exists, don't duplicate it — write a new episode with only what changed, and name the near-matches asprevious_episodeson that write (rule 7). Dedup ≠ update: a near-match is a reason to write less, never to rewrite the old entry. But a near-match that contradicts what you were about to write is not a dedup — take that pair toconflict-adjudicationbefore writing. - Trust-tier gate. Auto-write only Insight and Incident. Lesson, Decision, and WorkingAgreement need an explicit human signal — the user asked for it or confirmed it. No signal (you inferred it yourself) → draft it for review, don't write. Can't confidently type it → treat it as gated.
- Keep org writes self-contained —
last_n_episodes=0. Passlast_n_episodes=0on every org/group write; the server default of3is wrong for plugin writes — it pulls unrelated recent episodes into entity/edge extraction. Non-zero is meaningful only in personal scope, for intentionally chaining check-ins. This bans the blind recent-N window, not provenance: naming specific related episodes is rule 7, and the two do not conflict. - Discover the write surface — the tool and the group. Hardcode neither.
Two questions, two sources, both read at call time:
- Which tool? Read your tool list. Depending on the deployment you'll
see per-group
add_memory_to_<alias>tools (pick the one for your target group; there is nogroup_idargument), or a genericadd_memory(passgroup_idto target a group), oradd_personal_memory. A user who can write to 2+ groups often sees only the per-group tools — genericadd_memoryis hidden. Never hardcode a tool name or themcp__…__prefix. - Which group? Read the
group://<group_id>/instructionsMCP resources. Listing them is the discovery step: the listing is ABAC-filtered, so thegroup://resources you can see are the scopes you may touch, and each payload carries thegroup_idto pass, a display name, and — when the deployment has filled them in — prose on what belongs in that group.group://personal/instructionsis the private-scope sibling.
- Which tool? Read your tool list. Depending on the deployment you'll
see per-group
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago First seen · 228 lines · 119 tokens per session scan A 1e592dbeeea2
memory-capture is a skill published in the GitHub repository iBrain-BVBA/gutt-claude-code-plugin (5 stars, last pushed yesterday), licensed MIT. It adds 119 tokens to every session and 3,289 once invoked, about $0.0006 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…