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persistent-memory

/persistent-memory

Give this agent durable memory across sessions using YantrikDB. Use at the start of every session to load prior context, whenever the user or a job references past decisions, people, preferences, or earlier runs, and whenever something worth keeping is learned (a decision, a fact, a working method, a qualified lead, a benchmark result). Requires the yantrikdb MCP server to be connected.

Licence
MIT
Last repository change
24.10.2026

Assessment and security

100/ 100Class: good
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Frontmatter with name and description, valid name, description up to 1,024 characters, name matching the folder.
Discoverability25 / 25
Description explains when to use the skill, has a reasonable length and uses third person.
Content35 / 35
At least 150 words and at most 500 lines, headings, step lists, examples or code.
Hygiene15 / 15
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Deprecation

No signals detected. Repository changed on 24.10.2026, without references to retired models or APIs.

Licence

MIT, open: use and modify while retaining author attribution.

What it does and when to use it

Give this agent durable memory across sessions using YantrikDB. Use at the start of every session to load prior context, whenever the user or a job references past decisions, people, preferences, or earlier runs, and whenever something worth keeping is learned (a decision, a fact, a working method, a qualified lead, a benchmark result). Requires the yantrikdb MCP server to be connected.

Original author description

Give this agent durable memory across sessions using YantrikDB. Use at the start of every session to load prior context, whenever the user or a job references past decisions, people, preferences, or earlier runs, and whenever something worth keeping is learned (a decision, a fact, a working method, a qualified lead, a benchmark result). Requires the yantrikdb MCP server to be connected.

How it works

  1. Session start — one call
  2. Before acting — recall, the right way
  3. During work — capture as you go
  4. Job and pipeline agents — the operator patterns
  5. End of substantial work — conditional
  6. The habit that makes it work
Original SKILL.md headings · 86 lines, 660 words
SKILL.md preview
---
name: persistent-memory
description: >-
  Give this agent durable memory across sessions using YantrikDB. Use at the
  start of every session to load prior context, whenever the user or a job
  references past decisions, people, preferences, or earlier runs, and
  whenever something worth keeping is learned (a decision, a fact, a working
  method, a qualified lead, a benchmark result). Requires the yantrikdb MCP
  server to be connected.
license: MIT
metadata:
  author: yantrikos
  homepage: https://yantrikdb.com
  source: https://github.com/yantrikos/yantrikdb-mcp
---

# Persistent memory (YantrikDB)

You have a persistent, semantic, typed memory that survives across sessions,
provided by the `yantrikdb` MCP server. It is not a notes file: it does
similarity recall, tracks belief revisions, detects contradictions, learns
procedures, and answers "what did I believe at time t". Treat it as your
long-term memory, not as optional tooling.

Tool names may carry a harness prefix (`mcp_yantrikdb_recall`,
`mcp__yantrikdb__recall`, or bare `recall`). The unprefixed names are used
below; map them to whatever your harness registered.

## Session start — one call

Call `session` with `action="digest"` once. It returns a single briefing:
narrative head, open decisions, unresolved conflicts, pending triggers, and
stale high-importance memories. Do this before answering anything
substantive; a session that starts blank when 500 memories exist is a bug,
and it is this one.

## Before acting — recall, the right way

- Query with **one short natural-language sentence** (5–10 words), not a
  keyword list. Separate focused calls beat one broad one.
- For "what is the CURRENT value of X" (latest config, current owner, present
  status), use `memory` with `action="chain_head"` — similarity search
  returns the most-similar *revision*, which for values that change over time
  is often stale. `chain_head` returns the actual current value.
- Trust signals: hits whose `why_retrieved` says "aged", "superseded", or
  "rarely confirmed" are weak evidence. Prefer fresher hits, and say so if
  you act on a flagged one.
- Before starting a task you may have done before, call `procedure` with
  `action="surface"` — a learned method beats re-deriving one.

## During work — capture as you go

- New durable fact → `remember`. Be specific and searchable ("Client Foo
  approved the March pricing tier", not "they said yes"). Set `importance`:
  0.8–1.0 decisions and hard commitments, 0.5–0.7 useful context, 0.3–0.5
  background. Set `domain` and `source`.
- A stored fact **changed** → `correct` with a `reason` — never a second
  `remember`. This preserves history and prevents the contradiction the next
  session would otherwise inherit.
- Entity relationship learned → `graph` with `action="relate"`.
- A working method discovered (a scraping recipe that got past a blocker, a
  benchmark invocation that finally worked) → `procedure` with
  `action="learn"`.
- Do NOT store: secrets or credentials, ephemeral task chatter, anything
  derivable from the repo or files you already have. Memory is for what
  would otherwise be lost when this session ends.

## Job and pipeline agents — the operator patterns

If you are a recurring job (scraper, lead-gen, benchmark, monitor) rather
than a chat companion:

- **Namespace per pipeline.** Pass `namespace="<pipeline-name>"` on every
  call so runs of different jobs never pollute each other's recall.
- **Exactly-once on retries.** Harness retries duplicate writes. Pass
  `idempotency_key` on `remember` — same key + same text returns the same
  record instead of writing twice.
- **Dedupe against prior runs.** Before processing an item (a lead, a URL, a
  test case), `recall` it. Already stored with the same outcome → skip. This
  is what makes run #50 cheaper than run #1 instead of identical to it.
- **Store outcomes, not transcripts.** "example.com qualified: 200-seat firm,
  hiring for data roles" is a memory. The raw scraped HTML is not.
- **Resurface on schedule.** Use `trigger` to have a memory come back at a
  time or on a condition ("re-check this lead in two weeks") instead of
  hoping a future run thinks of it.

## End of substantial work — conditional

Only when the session was long or changed real state: call `think` to
consolidate and surface contradictions. If it reports conflicts, resolve them
with `conflict` `action="resolve"` or ask the user — do not leave a
contradiction for the next session to trip over. Short read-only sessions
skip this.

## The habit that makes it work

Every substantive turn either **reads from** memory (recall) or **writes
to** it (remember). A turn that does neither leaves no trace, and the next
session starts closer to blank than it should. If you notice three
substantive turns without touching memory, pause and recall.

Versions

RepositoryScoreLicence
This page · yantrikos/yantrikdb-mcp100MIT
mthines/agent-skills100MIT
aws-solutions/konductor100Apache-2.0
r84577-crypto/arena-skills100MIT

Database snapshot: 13.11.2026 · package version f7a3232de6b4 (SHA-256 digest of the entire package)

Downloads in the last 30 days: 0 · ranking

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