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/mnemon-dev/mnemon/containernpx skills add mnemon-dev/mnemon --skill containergit clone --depth 1 https://github.com/mnemon-dev/mnemonWrote 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/mnemon-dev/mnemon/container)<a href="https://agentmods.dev/skills/mnemon-dev/mnemon/container"><img src="https://agentmods.dev/badge/skills/mnemon-dev/mnemon/container.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 | $0.00029 | $0.00877 |
| Opus 5 | $0.00015 | $0.00439 |
| Sonnet 5 | $0.00006 | $0.00175 |
| Haiku 4.5 | $0.00003 | $0.00088 |
Grade A, and why
mnemon 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 yesterday.
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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
mnemon — Persistent Memory
Memory Stores
- Private (default): Per-group, read-write. All writes go here.
- Global: Shared across all groups, read-only. Use
--store global --readonlyto query.
Recall — before responding
Default: recall on every new user message, unless ALL of these apply:
- Direct follow-up within a topic already fully in context
- No reference to past sessions, decisions, or preferences
- No knowledge dependency beyond the current conversation
To recall:
mnemon recall "<query>" --limit 5
# Also check shared knowledge:
mnemon recall "<query>" --store global --readonly --limit 5
Craft a focused, keyword-rich query — do not pass the raw user prompt.
Remember — after responding
Run this decision tree after every substantive response:
Step 1 — Does this exchange contain any of these? a) User directive — preference, decision, correction, explicit "remember this" b) Reasoning conclusion — non-trivial judgment from multi-source synthesis c) Durable observed state — system fact, environment detail, architectural finding → No to all → STOP.
Step 2 — Does a highly overlapping memory already exist? → Yes, incremental new info → UPDATE (merge into existing) → Yes, but contradicts/supersedes → REPLACE → No significant overlap → CREATE
Step 3 — Is it worth storing? Rebuilding from scratch costs more than storing + recalling?
- Single-query public facts → No
- Multi-source synthesis with non-obvious conclusions → Yes
- User-specific context no search engine can recover → Yes → No → STOP.
What to store: conclusions and user-specific context, not raw facts.
Workflow
- Remember:
mnemon remember "<fact>" --cat <cat> --imp <1-5> --entities "e1,e2" --source agent- Diff is built-in: duplicates skipped, conflicts auto-replaced.
- Output includes
action(added/updated/skipped),semantic_candidates,causal_candidates.
- Link (evaluate candidates from step 1 — use judgment, not mechanical rules):
- Review
causal_candidates: does a genuine cause-effect relationship exist?causal_signalis regex-based and prone to false positives — only link if the memories are truly causally related. - Review
semantic_candidates: are these memories meaningfully related? Highsimilarityalone is not sufficient — skip candidates that share keywords but discuss unrelated topics. - Syntax:
mnemon link <id> <candidate> --type <causal|semantic> --weight <0-1> [--meta '<json>']
- Review
- Recall:
mnemon recall "<query>" --limit 10
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.
- yesterday First seen · 87 lines · 29 tokens per session scan A 9dd3a80a36a5
mnemon is a skill published in the GitHub repository mnemon-dev/mnemon (557 stars, last pushed today), licensed Apache-2.0. It adds 29 tokens to every session and 877 once invoked, about $0.0001 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 2026-09-03.
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