Skip to main content

hindsight-memory

Long-term memory for the agent via Hindsight. Use to recall relevant past context before answering, retain durable facts as you learn them, and reflect over accumulated memory for the "why" behind a decision. Load whenever continuity across sessions matters — the user refers to earlier work, states a lasting preference, or asks a question that prior context could answer.

Informations de source

Dépôt
vectorize-io/hindsight
Dernière activité de la source
11 août 2026 à 16:06
Langue détectée de SKILL.md
anglais
Étoiles
57 893
Forks
7 091

Options d'installation

Le prompt qui vérifie d'abord la source est sélectionné par défaut. Vous pouvez passer à une commande directe ou télécharger une copie locale.

Vérifiez les fichiers source

Lisez SKILL.md et les fichiers associés affichés par SkillsMP avant de décider de l'installer.

Affichage de SKILL.md

SKILL.md
Instructions source · Aperçu en lecture seule
name
hindsight-memory
description
Long-term memory for the agent via Hindsight. Use to recall relevant past context before answering, retain durable facts as you learn them, and reflect over accumulated memory for the "why" behind a decision. Load whenever continuity across sessions matters — the user refers to earlier work, states a lasting preference, or asks a question that prior context could answer.
# Hindsight long-term memory This plugin connects the agent to **Hindsight**, a long-term memory engine. Memory persists across sessions in a **bank** (scoped by `HINDSIGHT_BANK_ID`), so what you retain now is available to recall in future conversations. The `hindsight` MCP server exposes the tools below. Prefer these over guessing from scratch when the answer might live in past context. ## When to recall (read memory) Call **`recall`** at the start of a task, or whenever the user: - refers to earlier work, a past decision, or "the thing we set up", - states a preference or constraint that may already be recorded, - asks a question that accumulated project/user context could answer. ``` recall(query: "how do we deploy the API and which region") ``` `recall` runs semantic + keyword + graph + temporal retrieval and returns the most relevant memories. Ground your answer in what comes back, and say when nothing relevant was found rather than inventing continuity. ## When to retain (write memory) Call **`retain`** when you learn something **durable and reusable** — worth having in a future session, not just this one: - stable user preferences ("prefers pnpm; deploys from `main` only"), - project facts and decisions ("staging DB is Postgres 16 on Neon"), - outcomes and gotchas ("the flaky test was a timezone bug, fixed in #482"). ``` retain(content: "The user deploys the API to us-east-1 via GitHub Actions on push to main.") ``` Do **not** retain transient chatter, secrets, or anything the user asked you to keep out of memory. Retain the fact, not the whole transcript. ## When to reflect (reason over memory) Call **`reflect`** when a single recall is too shallow and you need synthesized reasoning over everything remembered — the *why* behind a behavior, or a judgment that weighs many facts together: ``` reflect(query: "What has repeatedly caused our CI to flake, and what should we standardize?") ``` `reflect` is slower and disposition-aware; use it deliberately, not for lookups. ## Bank scope All tools operate on the bank selected by the connection (`HINDSIGHT_BANK_ID`, default `default`). One bank = one memory store — keep a project's or a user's memory in its own bank so context stays isolated and relevant. You never pass a bank id to a tool; it is implicit from the endpoint.
Voir sur GitHub