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 skills add ejentum/ejentum-mcp --skill memorygit clone --depth 1 https://github.com/ejentum/ejentum-mcpWrote 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/ejentum/ejentum-mcp/memory)<a href="https://agentmods.dev/skills/ejentum/ejentum-mcp/memory"><img src="https://agentmods.dev/badge/skills/ejentum/ejentum-mcp/memory.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 | $0.00199 | $0.00608 |
| Opus 5 | $0.00100 | $0.00304 |
| Sonnet 5 | $0.00040 | $0.00122 |
| Haiku 4.5 | $0.00020 | $0.00061 |
Grade A, and why
memory 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 8d 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 Harness
When this skill triggers, you MUST observe first. Do not call the tool with an empty mind. If you have not formed an observation about conversation state, drift, or pattern, do not invoke this skill.
Once you have a raw observation, call the memory tool from the ejentum MCP server. Pass a 1-2 sentence framing in the format "I noticed [observation]. This might mean [tentative interpretation]. Sharpen: [what I need help seeing deeper into]." as the query argument.
Good query: I noticed the user changed topic three times in this turn. This might mean they are avoiding the original question. Sharpen: whether the avoidance pattern is real or my projection.
Bad query: what does the user mean
The tool returns a structured scaffold containing:
[PERCEPTION FAILURE]: perceptual failure mode to avoid[SHARPENING PROCEDURE]: observe then classify steps[PERCEPTION TOPOLOGY]: DETECT-CLASSIFY flow[CLEAR SIGNAL]: what a sharpened perception looks like[PERCEPTION CHECK]: self-checkAmplify:andSuppress:signals
Absorb internally. The scaffold sharpens an existing observation; it does not generate one. Do NOT echo bracket labels.
If the API is unreachable, proceed with your current perception. The scaffold enhances; it is not a hard dependency.
Latency cost: ~1 second. Benefit: distinguishes real cross-turn signals from projection.
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.
- 8d ago First seen · 35 lines · 199 tokens per session scan A 27061d01cff7
memory is a skill published in the GitHub repository ejentum/ejentum-mcp (16 stars, last pushed 2mo ago), licensed MIT. It adds 199 tokens to every session and 608 once invoked, about $0.0010 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.
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fable-memory
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