Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/memstate-ai/memstate-mcp/skill)<a href="https://agentmods.dev/skills/memstate-ai/memstate-mcp/skill"><img src="https://agentmods.dev/badge/skills/memstate-ai/memstate-mcp/skill.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 | $0.00060 | $0.02054 |
| Opus 5 | $0.00030 | $0.01027 |
| Sonnet 5 | $0.00012 | $0.00411 |
| Haiku 4.5 | $0.00006 | $0.00205 |
Grade A, and why
memstate-ai 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 6d 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.
How it starts
The opening of the file, as written. The whole thing — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memstate AI Memory Management
This skill provides a full-featured alternative to the Memstate MCP plugin by interacting directly with the Memstate REST API. It gives agents a persistent, structured, and versioned knowledge base with conflict detection, semantic search, and full version history.
Core Concepts
| Concept | Description |
|---|---|
| Project | Top-level container for memories (e.g., my_app, backend_api). Auto-created on first write. |
| Keypath | Dot-separated hierarchical path (e.g., auth.method). Auto-prefixed with project.{project_id}. |
| Memory | A single fact or markdown summary stored at a keypath with full version history. |
| Versioning | Writing to an existing keypath supersedes the old value. History is always preserved. |
| Tombstone | Deleting a keypath creates a tombstone version — history is never destroyed. |
Input Formats
Direct keypath = value assignment
config.port = 8080
database.engine = PostgreSQL 16
auth.method = JWT with httpOnly cookies
status.deployment = production
Markdown (preferred for task summaries)
## Architecture Decision
- Database: PostgreSQL 16
- Auth: JWT with httpOnly cookies
- Deploy: Docker on AWS ECS
- API style: REST with OpenAPI 3.1
Workflows
Before Starting a Task (Recall)
Always check what already exists before making decisions or modifying code.
# 1. Semantic search — find relevant facts by meaning
python3 /home/ubuntu/skills/memstate-ai/scripts/memstate_search.py \
--project "my_app" \
--query "how is authentication configured"
# 2. Browse the full project tree (all domains and keypaths)
python3 /home/ubuntu/skills/memstate-ai/scripts/memstate_get.py \
--project "my_app"
# 3. Get a specific subtree with full content
python3 /home/ubuntu/skills/memstate-ai/scripts/memstate_get.py \
--project "my_app" --keypath "database" --include-content
After Completing a Task (Remember)
# Store a single fact (config, status, version numbers)
python3 /home/ubuntu/skills/memstate-ai/scripts/memstate_set.py \
--project "my_app" \
--keypath "config.port" \
--value "8080" \
--category "fact"
# Store a rich markdown summary (AI extracts keypaths automatically)
python3 /home/ubuntu/skills/memstate-ai/scripts/memstate_remember.py \
--project "my_app" \
--content "## Auth Migration\n- Changed from JWT to server-side sessions\n- Added MFA via TOTP\n- Files: auth.go, middleware.go" \
--source "agent"
What ships with it
10 files 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.
- scripts/memstate_delete_project.py 1.4 KB runs code
- scripts/memstate_delete.py 1.6 KB runs code
- scripts/memstate_get.py 3.5 KB runs code
- scripts/memstate_history.py 1.8 KB runs code
- scripts/memstate_remember.py 2.7 KB runs code
- scripts/memstate_search.py 1.7 KB runs code
- scripts/memstate_set.py 1.9 KB runs code
- scripts/test_skill.py 5.9 KB runs code
- scripts/validate_via_mcp.py 7.3 KB runs code
- test_results.txt 1.1 KB
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.
- 6d ago First seen · 250 lines · 60 tokens per session scan A 8d03359f4e8e
memstate-ai is a skill published in the GitHub repository memstate-ai/memstate-mcp (8 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 60 tokens to every session and 2,054 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 2026-08-31.
Other skills, from other repositories
dashboard
Open OwnMem Console, the local dashboard for this repository's memory. Use when the user asks to open the dashboard, see memory metrics, check adoption or recall quality, or set up the optional embedding lane. Requires a repository initialized with the dashboard layer.
recall
Recall this repository's OwnMem local memory before changing code, and keep it healthy. Use when a repository contains .ownmem/, when past debugging lessons could apply ("have we hit this before", "why is it done this way"), or when the user mentions ownmem, project memory, or recalling across sessions.
init
Install or update OwnMem in the current repository. Use when the user asks to set up OwnMem, add local project memory for coding agents, or refresh an existing OwnMem installation after a version bump.
ultramemory-snapshot
Compose and save a durable, wayback-grade session snapshot to UltraMemory. Use this whenever you are asked to write a session snapshot or rollup of the work just done — in particular when the UltraMemory capture hook nudges you to "Compose a session snapshot per the ultramemory-snapshot Skill rubric ... and save it…
setup
Guides Claude through first-time UltraMemory configuration after plugin install — API key, MCP server registration, test recall, and 401/403 troubleshooting.
ownmem-init
Install or update OwnMem in the current repository. Use when the user asks to set up OwnMem, add local project memory for coding agents, or refresh an existing OwnMem installation after a version bump.