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 TT-Wang/memem/plugin install mememWrote 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/tt-wang/memem/memem-doctor)<a href="https://agentmods.dev/skills/tt-wang/memem/memem-doctor"><img src="https://agentmods.dev/badge/skills/tt-wang/memem/memem-doctor/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/tt-wang/memem/memem-doctor"><img src="https://agentmods.dev/badge/skills/tt-wang/memem/memem-doctor.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 | $0.00057 | $0.00358 |
| Opus 5 | $0.00028 | $0.00179 |
| Sonnet 5 | $0.00011 | $0.00072 |
| Haiku 4.5 | $0.00006 | $0.00036 |
Grade A, and why
memem-doctor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Run bash "${CLAUDE_PLUGIN_ROOT}/bootstrap.sh" --help to confirm the bootstrap shim is present, then run the doctor:
PYTHONPATH="${CLAUDE_PLUGIN_ROOT}" python3 -m memem.server --doctor
Display the full output verbatim. If the result says FAILING, the user must fix the listed blockers before memem will work — walk them through each one:
- mcp package missing →
pip install mcp(or re-run bootstrap.sh which willuv sync) - ~/.memem not writable → set
MEMEM_DIR=/writable/pathenv var in their shell profile - vault not writable → set
MEMEM_OBSIDIAN_VAULT=/writable/pathenv var
If the result says DEGRADED, the system works but Haiku-powered context assembly and smart-recall are disabled because the claude CLI isn't on PATH. memem falls back to FTS-only recall, which is still useful — just less smart. Tell the user how to fix it (install Claude Code CLI, ensure it's on PATH) but don't block them on it.
If the result says HEALTHY, tell the user everything looks good and suggest running /memem-status to see memory counts, or just continue their conversation — memem runs automatically in the background.
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.
- 10d ago First seen · 22 lines · 57 tokens per session scan A 2121cd20a34b
memem-doctor is a skill published in the GitHub repository TT-Wang/memem (32 stars, last pushed 8d ago), licensed MIT. It adds 57 tokens to every session and 358 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
troubleshooting-assistant
Diagnoses and resolves MCP server registration failures, GPU detection, BigQuery authentication, index build failures, import errors, search quality issues, and performance problems.
diagnosing-bugs
Diagnosis loop for hard bugs and performance regressions. Builds a red-capable feedback loop and runs it before hypothesising — complements static code review (which finds bugs by reading) by running actual repros. Use when the user says 'diagnose'/'debug this', or reports something broken/throwing/failing/slow.
skills
Your next session starts cold. No memory of what you built, what broke, what you decided. Every memory you write is a gift to that future session. The richer the memory, the less time re-learning.
issue-debugging
Systematic methodology for issue debugging including root cause analysis, impact mapping, tiered validation plans, and confidence assessment. Use when analyzing bugs, fixing issues, or validating fixes.
fix-bug
Run the Fix Validation pipeline to investigate, fix, and validate a bug. Ensures deterministic pipeline execution with IssueAnalyzer, FixWriter, TestWriter (conditional), TestAudit (conditional), and FixValidator stages.
ccc-systematic-debugging
Root-cause debugging via the Iron Law: no fix without confirmed root cause. Reproduce → hypothesize → verify → fix. Use when investigating bugs, test failures, or…