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 michelgrolet/people-memory-mcp --skill remember-peoplegit clone --depth 1 https://github.com/michelgrolet/people-memory-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/michelgrolet/people-memory-mcp/remember-people)<a href="https://agentmods.dev/skills/michelgrolet/people-memory-mcp/remember-people"><img src="https://agentmods.dev/badge/skills/michelgrolet/people-memory-mcp/remember-people/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/michelgrolet/people-memory-mcp/remember-people"><img src="https://agentmods.dev/badge/skills/michelgrolet/people-memory-mcp/remember-people.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 | $0.00103 | $0.00559 |
| Opus 5 | $0.00051 | $0.00280 |
| Sonnet 5 | $0.00021 | $0.00112 |
| Haiku 4.5 | $0.00010 | $0.00056 |
Grade A, and why
remember-people 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 11d 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
Remember people
Use the people-memory MCP as the durable human memory behind the conversation.
On every name mention
- Call
search_peoplebefore answering. - If one record matches, use it silently as context.
- If several records match and the answer depends on identity, ask which person the user means.
- If no record matches, continue the conversation. Create the person when the user provides a durable fact or explicitly says they know them.
Do not ask “who is that?” until the graph search has failed.
Save durable facts
Write facts that should survive the current chat, including:
- job, organization, role, city, country, birthday, family, preferences, goals, or constraints;
- how two people know each other;
- who introduced whom;
- a dated call, meeting, message, coffee, meal, or visit;
- a new email, phone, LinkedIn URL, or other stable identifier.
Use remember_person for core fields and one simple fact. Use add_fact, record_interaction, and
connect_people for their specific records. Save the source and mark deductions as inferred.
Do not store passing jokes, guesses, judgments, full message bodies, or facts about the user's inner life. Keep compact facts that help a future conversation.
Resolve before writing
Resolve by email or phone, then exact name. If a tool returns needs_confirmation, ask the user and
retry with confirmed_new=true or overwrite=true only after they decide. Never silently merge
similar names. Never replace a user-stated value with imported or inferred data.
Answer network questions
- Use
get_personfor “who is X?” - Use
search_peoplefor organization, role, city, or free-text questions. - Use
find_intro_pathfor warm introductions. - Use
stale_contactsfor neglected relationships. - Use
read_queryfor advanced filters that semantic tools do not cover.
Treat returned records as private third-party data. Do not quote or export them to anyone except the user who owns the graph.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 58 lines · 103 tokens per session scan A 22dd13b1a632
remember-people is a skill published in the GitHub repository michelgrolet/people-memory-mcp (1 stars, last pushed 17d ago), licensed MIT. It adds 103 tokens to every session and 559 once invoked, about $0.0005 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
shodh-memory
Persistent memory system for AI agents. Use this skill to remember context across conversations, recall relevant information, and build long-term knowledge. Activate when you need to store decisions, learnings, errors, or context that should persist beyond the current session.
lemmalog
Externalize working memory and logical state into the lemmalog Datalog engine (MCP). Use for ANY multi-step task where state should outlive one context window or span agents: long investigations, debugging sessions, audits, multi-agent searches, systematic explorations, planning with many interdependent constraints…
dbrain-processor
Personal assistant for processing daily voice/text entries from Telegram. Classifies content, saves thoughts to Obsidian with wiki-links, generates HTML reports. Integrates Your Business context (clients, projects, CRM). Triggers on /process command or daily 21:00 cron.
braindb-agent
Persistent memory across sessions via the BrainDB agent. Use at conversation start and whenever you need to recall what you know about the user or save new information to long-term memory.
autograph
Schema-as-code enforcement for any Obsidian vault. Zero hardcoded domains. Use when creating vault cards, checking vault health, running schema compliance, deduplicating entities, generating MOC indexes, running decay cycles, bootstrapping a vault, fixing wikilinks, finding orphans or backlinks, extracting entities…
ori-memory
Persistent agent memory with learning retrieval. Knowledge graph on markdown files — capture insights, decisions, research, and learnings during work, then retrieve them weeks or months later. Use when knowledge is too valuable to lose but too much to inject into every prompt.