EXAMPLES.md
# remember worked examples
## 1. Saving a decision
User: "Remember that we decided to drop Redis and use the iii-engine state store for session caching."
Invocation:
```json
memory_save {
"content": "We decided to drop Redis and use the iii-engine state store for session caching to remove a moving part.",
"concepts": "session-caching, state-worker-store, dropped-redis",
"files": ""
}
```
Response:
```json
{ "id": "7f3a9c21", "concepts": ["session-caching", "state-worker-store", "dropped-redis"], "saved": true }
```
Present:
> Saved. Tagged it `session-caching`, `state-worker-store`, `dropped-redis`, so a
> later recall on caching or Redis will surface this decision.
## 2. Saving a code-specific gotcha
User: "Save this: the rate limiter in middleware/limit.ts counts per-IP, not per-user, so logged-in users on shared IPs collide."
Invocation:
```json
memory_save {
"content": "The rate limiter in middleware/limit.ts counts per-IP, not per-user, so logged-in users on shared IPs collide.",
"concepts": "rate-limiter, per-ip-bug, shared-ip-collision",
"files": "src/middleware/limit.ts"
}
```
Response:
```json
{ "id": "b21d004e", "concepts": ["rate-limiter", "per-ip-bug", "shared-ip-collision"], "saved": true }
```
Present:
> Saved memory `b21d004e` referencing `src/middleware/limit.ts`. Concepts:
> `rate-limiter`, `per-ip-bug`, `shared-ip-collision`.
## 3. Vague request, no files
User: "Remember this for later."
Ask once for the substance, then save:
```json
memory_save {
"content": "Staging deploys must run the migration job before the app rollout, never after.",
"concepts": "staging-deploy, migration-ordering, rollout-sequence",
"files": ""
}
```
Present the confirmation with the concepts echoed back.
SKILL.md
---
name: remember
description: Save an insight, decision, or learning to agentmemory's long-term storage with searchable concept tags. Use when the user says "remember this", "save this", "note that", "don't forget", or wants to preserve knowledge for future sessions.
argument-hint: "[what to remember]"
user-invocable: true
---
The user wants to save this to long-term memory: $ARGUMENTS
## Quick start
```json
memory_save {
"content": "We rotate JWT refresh tokens on every use; the old token is revoked server-side in auth/refresh.ts.",
"concepts": "jwt-refresh-rotation, token-revocation, auth-flow",
"files": "src/auth/refresh.ts"
}
```
Expected output:
```text
Saved memory abc12345 with 3 concepts: jwt-refresh-rotation, token-revocation, auth-flow.
```
## Why
A memory is only as useful as the terms that retrieve it. Tag with specific
concepts so a future `recall` finds it, and preserve the user's own phrasing.
## Workflow
1. Pull the core insight, decision, or fact out of `$ARGUMENTS`.
2. Extract 2-5 lowercased concept phrases. Prefer specific over generic
(`jwt-refresh-rotation` beats `auth`).
3. Extract referenced file paths (absolute or repo-relative). Empty if none.
4. Call `memory_save` with `content`, `concepts` (comma-separated string), and
`files` (comma-separated string). In a multi-agent setup pass `agentId` so
the memory lands in the right agent's scope.
5. Confirm the save and echo the concepts so the user knows the retrieval terms.
6. To update a fact, save the corrected version outright: near-duplicate content
supersedes the old record, which leaves recall but stays in the version chain.
## Anti-patterns
WRONG: `concepts: "stuff, code, notes"` (generic tags nothing can find later).
RIGHT: `concepts: "jwt-refresh-rotation, token-revocation"` (specific, retrievable).
## Checklist
- Content preserves the user's phrasing, not a paraphrase.
- Concepts are specific, lowercased, 2-5 items.
- File paths are real references, not guesses.
- Confirmation echoes the exact concepts tagged.
## See also
- `recall`: retrieve what you save here (the pair to this skill).
- `forget`: remove a memory you saved by mistake.
- `lesson`: behavioral rules from corrections; memories are for facts.
- `memory-discipline`: when to save unprompted.
## Troubleshooting
See [TROUBLESHOOTING.md](TROUBLESHOOTING.md) if `memory_save` is not available.
TROUBLESHOOTING.md
<!-- AUTOGEN:troubleshooting START - generated by scripts/skills/generate.ts, do not edit by hand -->
# Troubleshooting agentmemory skills
Recovery steps for separately installed skills. This document is generated into
each skill directory so native skill readers can open it without parent traversal.
## "MCP tool not available"
Missing tools can mean a stopped MCP process, a disconnected daemon, or an
explicitly reduced tool surface. Walk these in order:
1. Identify the host. In Claude Code, inspect `/plugin list` and `/mcp`.
In Hermes, inspect the configured memory provider and `mcp_servers` separately.
In OpenClaw, validate the host configuration and inspect its MCP servers and
native plugins separately. In Pi or OMP, inspect `/agentmemory-status` and the
MCP inventory separately; native save/search does not prove the broader tools
are connected. Do not run another host's setup commands.
2. Restart or reload the affected host connection after configuration changes.
3. Inspect the connected tool inventory. Hermes may namespace MCP tools; invoke
the actual advertised names and schemas. A native memory provider is not
proof that every MCP tool or shared skill is available.
4. Check `agentmemory status` and the daemon's `AGENTMEMORY_TOOLS` setting.
Codex's bundled bridge lists the daemon's enabled tools. The standalone shim
used by other hosts can fall back to seven basic tools with a separate store.
Do not claim full daemon functionality based on those seven tools.
5. If `agentmemory connect codex` and the Codex plugin are both enabled, choose
one MCP connection. Check `/hooks` before adding global hook fallbacks.
Hermes, OpenClaw, Pi and OMP connector installations bundle skills; other hosts
may install them separately. Check the host's skill inventory and open this file
from the installed skill directory. Test automatic capture with a real turn and
a recorded observation; tool connectivity alone does not establish lifecycle
capture.
An outage or timeout is not an empty search result. Report the failure. A write
may have committed before the response was lost; inspect state before retrying.
## REST fallback
When the MCP tools stay unavailable but the daemon is running, call the REST
API directly:
1. Set `AGENTMEMORY_URL` to the daemon base URL (default `http://localhost:3111`).
2. The REST API requires a bearer credential by default. Use the host's explicit
`AGENTMEMORY_SECRET`; for loopback URLs only, fall back to the secret in
`~/.agentmemory/.env` and then `~/.agentmemory/secret`. Load the value locally
without displaying it. Never send a local-file secret to a remote URL.
If `AGENTMEMORY_CREDENTIAL_MODE=explicit`, require the explicit host credential
and do not read local credential files.
Authenticated REST fallback requests require HTTPS for non-loopback
URLs; HTTP is allowed for loopback URLs (`localhost`, `127.0.0.0/8`, or `[::1]`).
Reject redirects on authenticated requests. Do not print or save the secret.
Endpoint map by skill:
| Skill | REST call |
| --------------- | --------------------------------------------------------------- |
| remember | `POST /agentmemory/remember` |
| recall | `POST /agentmemory/smart-search` |
| recap | `GET /agentmemory/sessions` + `POST /agentmemory/smart-search` |
| handoff | `GET /agentmemory/sessions` + `POST /agentmemory/smart-search` |
| session-history | `GET /agentmemory/sessions` |
| commit-context | `GET /agentmemory/session/by-commit?sha=<sha>` |
| commit-history | `GET /agentmemory/commits` (URL-encode every query param) |
The MCP host reads the plugin MCP configuration. Restart the daemon after
changing its configuration, then restart or reconnect the host MCP process after
changing its URL or explicit secret. The local bridge reads the credential files
for each request; it does not inherit other daemon `.env` settings.
<!-- AUTOGEN:troubleshooting END -->