Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add memseekai/memseek/plugin install memseek-memoryWrote 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/memseekai/memseek/memseek-feedback)<a href="https://agentmods.dev/skills/memseekai/memseek/memseek-feedback"><img src="https://agentmods.dev/badge/skills/memseekai/memseek/memseek-feedback/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/memseekai/memseek/memseek-feedback"><img src="https://agentmods.dev/badge/skills/memseekai/memseek/memseek-feedback.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 | $0.00023 | $0.00259 |
| Opus 5 | $0.00012 | $0.00130 |
| Sonnet 5 | $0.00005 | $0.00052 |
| Haiku 4.5 | $0.00002 | $0.00026 |
Grade A, and why
memseek-feedback 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 9d 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
Send context feedback
Attach $ARGUMENTS to the latest bound Memseek artifact use for this project. This is a
learning signal; it does not directly edit or promote a procedure.
Parse the first word as the feedback kind and the remainder as a concrete comment. If the
kind is missing, ask for one of thumbs_up, thumbs_down, correction, task_success,
or task_failure. Then run:
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/memseek_doctor.py" feedback \
--kind "<kind>" --comment "<comment>" \
--session-id "<exact conversation session from SessionStart>"
Use normal shell argument safety: pass values as separate quoted arguments and do not interpolate them into executable shell syntax. The session id prevents feedback from a parallel terminal attaching to the wrong render. Report the artifact-use id and whether the signal was sent or queued. Never claim feedback automatically changed a live procedure.
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.
- 9d ago First seen · 26 lines · 23 tokens per session scan A d4d8ce96eba8
memseek-feedback is a skill published in the GitHub repository memseekai/memseek (7 stars, last pushed 14d ago), licensed Apache-2.0. It adds 23 tokens to every session and 259 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 2026-08-31.
Other skills, from other repositories
atomicmemory
Persistent semantic memory across Claude Code sessions — user preferences, project context, prior decisions, codebase facts. Call memorysearch before answering questions that reference past work. Call memoryingest after the user shares durable facts.
atomicmemory-cli
Use the installed AtomicMemory CLI for memory search, ingestion, packaging, diagnostics, and agent-safe JSON output.
openmemory
Manage persistent memory via OpenMemory MCP. TRIGGER when: user says "remember this", "save to memory", "store this", "recall", "what do you remember about", "check memory", "forget this", "delete memory", "clean up memory", or when agent forms a stable conclusion worth persisting. DO NOT TRIGGER when: user refers to…
recall-before-claim
Forces a memorysearch before the agent sends a message containing a factual assertion that has not yet been grounded this turn. Closes the citation-rate gap from 40% to 90%+.
route-by-query-shape
When the agent calls memorysearch with a relationship-shaped query ("who did I talk to about X"), redirect to the knowledgegraph backend where it will actually find the answer.
deja-search
Search deja before re-deriving past work: when the user refers to earlier sessions or decisions, before debugging an error, and before implementing something that may already exist. It searches this machine's own history across every AI coding tool used on it, going back further than deja itself was installed.