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/modeled-information-format/mnemonic/custodiannpx skills add modeled-information-format/mnemonic --skill custodiangit clone --depth 1 https://github.com/modeled-information-format/mnemonicWrote 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/modeled-information-format/mnemonic/custodian)<a href="https://agentmods.dev/skills/modeled-information-format/mnemonic/custodian"><img src="https://agentmods.dev/badge/skills/modeled-information-format/mnemonic/custodian.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 5.1 | $0.00058 | $0.01049 |
| Opus 5 | $0.00029 | $0.00524 |
| Sonnet 5 | $0.00012 | $0.00210 |
| Haiku 4.5 | $0.00006 | $0.00105 |
Grade A, and why
custodian 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 6d 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory
Search first: /mnemonic:search {relevant_keywords}
Capture after: /mnemonic:capture {namespace} "{title}"
Run /mnemonic:list --namespaces to see available namespaces from loaded ontologies.
Custodian Skill
Provides custodial services for the mnemonic memory system: auditing, validation, link repair, decay management, relocation, and summarization.
When to Use
- Proactively when session_start reports low health scores or potential duplicates
- On request when user asks about memory health, broken links, or maintenance
- After project renames to relocate memories and update references
- Periodically to keep decay scores current and identify orphaned memories
Architecture
The custodian operates through focused Python modules:
| Module | Responsibility |
|---|---|
memory_file.py |
Parse/validate/update MIF frontmatter |
link_checker.py |
Build UUID/slug index, validate links, find orphans |
decay.py |
Calculate exponential/linear/step decay, update strength |
relocator.py |
Move files + update all cross-references |
validators.py |
MIF schema validation, ontology relationship checks |
report.py |
Structured findings with markdown/JSON output |
custodian.py |
CLI orchestrator dispatching to modules |
Subcommands
audit (default)
Runs all checks in a single pass. This is the recommended entry point.
/mnemonic:custodian audit [--fix] [--dry-run]
Checks performed:
- Frontmatter: Required fields, UUID format, type enum, date format
- Links: Wiki-links resolve to existing memories
- Relationships: Types match MIF built-in or ontology-defined types
- Decay: Recalculates strength values using configured model
- Orphans: Detects memories with no incoming references
relocate
Moves memories when project or org names change:
/mnemonic:custodian relocate <old-path> <new-path> [--dry-run]
What ships with it
8 files 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.
- 6d ago First seen · 138 lines · 58 tokens per session scan A ba3b643871a3
custodian is a skill published in the GitHub repository modeled-information-format/mnemonic (23 stars, last pushed 1mo ago), licensed MIT. It adds 58 tokens to every session and 1,049 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 2026-08-30.
Other skills, from other repositories
reflect
Deep reflection skill - read memory and context, decide whether there is something worth sending.
digest-auto
EpisodicRAG システムの現在の状態を分析することで、 まだらボケ（未処理 Loop/プレースホルダー/欠番）を検出し、 生成可能なダイジェスト階層を判別するスキルです。 まだらボケの早期検出・予防のため、定期的に実行することを推奨します。.
digest-setup
EpisodicRAG初期セットアップ（対話的）.
digest-config
EpisodicRAG設定変更（対話的）.
okf
Author, maintain, and consume Open Knowledge Format (OKF) knowledge bundles — portable markdown + YAML frontmatter that both humans and agents read. Use when capturing project knowledge (services, APIs, schemas, metrics, runbooks, decisions) into an OKF bundle, when updating one after code or docs change, or when a…
forgetful-remember
Remember knowledge worth keeping — a decision made, a solution found, a preference stated, a pattern confirmed. Use when work surfaces something future sessions will need, or the user asks to remember something. Routes content to the right store (memory, document, code artifact, entity, procedure, file) and enforces…