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 gosha70/code-copilot-team --skill memkernel-memorygit clone --depth 1 https://github.com/gosha70/code-copilot-teamWrote 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/gosha70/code-copilot-team/memkernel-memory)<a href="https://agentmods.dev/skills/gosha70/code-copilot-team/memkernel-memory"><img src="https://agentmods.dev/badge/skills/gosha70/code-copilot-team/memkernel-memory/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gosha70/code-copilot-team/memkernel-memory"><img src="https://agentmods.dev/badge/skills/gosha70/code-copilot-team/memkernel-memory.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00026 | $0.00338 |
| Opus 5.5 | $0.00010 | $0.00135 |
| Sonnet 5.5 | $0.00005 | $0.00068 |
| Haiku 4.5 | $0.00003 | $0.00034 |
Grade A, and why
memkernel-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 1mo 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
MemKernel Memory
Use this rule only when the memkernel MCP server is configured for the project.
Purpose
MemKernel gives the project persistent memory across sessions and compaction events. Use it to retain decisions, conventions, important code context, and structured session checkpoints.
When To Recall
- At the start of a new session, call
recallfor the current task or topic. - After compaction, recover the latest checkpoint first, then broaden recall.
- Before repeating earlier work, recall prior decisions instead of guessing.
What To Retain
decision: architecture choices, tradeoffs, dependency decisionsconvention: naming rules, style decisions, workflow expectationscode: important interfaces, schemas, contracts, or code snapshotsepisode: session events and structured checkpoints
Checkpoint Pattern
Before compaction, retain a checkpoint with:
- what was completed
- active constraints
- current task
- next steps
- open questions
Use checkpoint=true for these records so they can be recovered separately.
Working Rules
- Be specific. Vague memories are hard to retrieve later.
- Prefer one focused memory per decision or convention.
- Use
get(ref_id)when arecallresult looks relevant but the preview is not enough. - Do not spam duplicate memories.
retainis idempotent for identical content and type. - Treat recalled content as context to verify, not as unquestionable truth.
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.
- 1mo ago First seen · 48 lines · 26 tokens per session scan A ba2ed42736e6
memkernel-memory is a skill published in the GitHub repository gosha70/code-copilot-team (7 stars, last pushed 17d ago), licensed MIT. It adds 26 tokens to every session and 338 once invoked, about $0.0001 per session on Opus 5.5. 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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