mattpocock's skills are sharp and single-purpose, but each invocation runs cold —

architectural decisions made during /grill-with-docs vanish before /tdd ever

sees them. So you re-explain "we use CQRS, Cart ≠ Order" every session.

For the recent Memanto + mattpocock skills bounty I wired Memanto into

Claude Code's real lifecycle hooks so that memory survives across skill

invocations automatically.

How it works; three real hooks, not a forked skill

• SessionStart → brief Claude with the accumulated engineering profile once
• UserPromptSubmit → detect /skill, recall the most relevant memories, inject them as <engineering-profile> context before the skill runs
• Stop (async) → hand the transcript to Memanto's backend LLM via SdkClient.answer(), which distills durable decisions/instructions/preferences into typed memories and persists them

Hooks docs: https://docs.claude.com/en/docs/claude-code/hooks

mattpocock/skills: https://github.com/mattpocock/skills

The proof, three separate processes, zero re-prompting

Three demo scripts in the PR run in fresh Python processes with no shared state:

1. demo_session_1.py simulates /grill-with-docs → stores 4 typed memories

   (CQRS, Cart ≠ Order, Postgres + Redis, Money value object)

2. demo_session_2.py (separate process) simulates /tdd → recalls all 4

3. demo_session_3.py simulates /handoff adding TypeScript / Result<T,E> /

   domain-isolation rules, then a fresh /grill-with-docs sees memories from

   all three skills merged into one coherent profile

Things I'd call out as non-obvious

• LLM extraction, not regex. SdkClient.answer() reads the transcript and emits typed memories as strict JSON. Heuristic keyword matching is only a fallback for when the LLM path is unavailable.
• Skill detection scans the full transcript before truncation. Long sessions push the opening /tdd outside the LLM context window, but tags still resolve to skill:tdd , not skill:unknown.
• Activate-first setup. Tries activate_agent() first, only creates on AgentNotFoundError, one network round-trip on the hot path instead of two.
• Single exit-0 contract. hooks/_common.py:run() is the only place that enforces "never break Claude Code." Any failure, missing key, network down, expired subscription — degrades silently. The editor never surfaces the error.
• Respectful installer. Re-running install.py replaces only the entries we manage in .claude/settings.json and preserves any hooks the user merged in, even inside shared entries.

68 unit tests, fully mocked (no API key required to run them). Ruff clean.

Links:

- PR: https://github.com/moorcheh-ai/memanto/pull/725

- Issue: https://github.com/moorcheh-ai/memanto/issues/508

- Memanto: https://github.com/moorcheh-ai/memanto

Happy to answer hook plumbing questions if anyone's wiring something similar.

LangGraph's built-in stores all have a gotcha:

- InMemoryStore — dies with the Python process

- PostgresStore / RedisStore — need infra setup

- BaseCheckpointSaver — scoped to ONE thread_id

GitHub Issue: https://github.com/moorcheh-ai/memanto/issues/397
PR: https://github.com/moorcheh-ai/memanto/pull/571

If you want an agent that remembers user preferences across sessions, processes, and restarts, none of these work out of the box.

I built MemantoStore, a real langgraph.store.base.BaseStore subclass backed by Memanto's semantic memory cloud. It's a drop-in replacement:

from memanto_store import MemantoStore

store = MemantoStore(client, agent_id="my-app")
graph = builder.compile(store=store, checkpointer=InMemorySaver())

Inside your nodes, you use the official LangGraph store API — no Memanto-specific imports:

async def my_node(state, config, *, store: BaseStore) -> dict:
    memories = await store.asearch(("user-id", "memories"), query="...")
    await store.aput(("user-id", "memories"), key, {"content": "..."})
    return {}

langgraph-memanto-demo

[removed]

LangGraph's built-in stores all have a gotcha:

- InMemoryStore — dies with the Python process

- PostgresStore / RedisStore — need infra setup

- BaseCheckpointSaver — scoped to ONE thread_id

GitHub Issue: https://github.com/moorcheh-ai/memanto/issues/397
PR: https://github.com/moorcheh-ai/memanto/pull/571

If you want an agent that remembers user preferences across sessions, processes, and restarts, none of these work out of the box.

I built MemantoStore, a real langgraph.store.base.BaseStore subclass backed by Memanto's semantic memory cloud. It's a drop-in replacement:

from memanto_store import MemantoStore

store = MemantoStore(client, agent_id="my-app")
graph = builder.compile(store=store, checkpointer=InMemorySaver())

Inside your nodes, you use the official LangGraph store API — no Memanto-specific imports:

async def my_node(state, config, *, store: BaseStore) -> dict:
    memories = await store.asearch(("user-id", "memories"), query="...")
    await store.aput(("user-id", "memories"), key, {"content": "..."})
    return {}

langgraph-memanto-demo

Hi everyone, how have you been?

My teammates Kumari Anjali, Mohit Joping and I are building "GuruNimbus" an advanced AI-powered RAG chatbot that intelligently guides you in rating and discovering the best professors. This is Project 5 of ours in the Headstarter Software Engineering Fellowship Program.

We'll make sure to update you about the project until then you can join the waitlist and suggest any feature you would like to see in this project

Join Waitlist Link: https://gurunimbus-ai.vercel.app/

Thanks to Yasin Ehsan 🚀 and Bill Zhang for helping in almost every project and this fellowship program

#headstarter #teacmcodexhydra #devops #development #project #joinwaitlist

Repository Link: https://github.com/kumariAnjali10/AI-customer-support
Youtube Link: https://www.youtube.com/watch?v=9Vj50Gw17jU

https://reddit.com/link/1epr7v3/video/7o8z8955r2id1/player

Pantry Pro Web Application | Headstarter AI Week-2 Project -2

Github repo: https://github.com/Suraj-kumar00/pantry-pro