> ## Documentation Index
> Fetch the complete documentation index at: https://docs.trytrellis.app/llms.txt
> Use this file to discover all available pages before exploring further.

# mem-recall

> Cross-platform AI conversation recall via trellis mem

mem-recall makes the AI invoke `trellis mem` whenever the user references past conversations, retrieve content from local Claude Code, Codex, Grok, OpenCode, Pi and ZCode session stores, and answer with session-id + verbatim quotation.

Trigger phrases include `last time`, `we discussed`, `what did I tell <Claude/Codex>`, `find ... last week`, `上次`, `之前`, and other references to prior dialogue.

Without the skill, the AI defaults to "I don't have that context" or speculative answers. The skill's frontmatter `description` field instructs the AI to run `trellis mem` in these cases, with a `search` → `context` two-step retrieval flow.

## Prerequisites

| Tool                                                                    | Purpose                 | Required     |
| ----------------------------------------------------------------------- | ----------------------- | ------------ |
| [Trellis CLI](https://github.com/mindfold-ai/Trellis) **0.6.0-beta.0+** | Provides `trellis mem`  | Required     |
| Claude Code, Codex CLI, Grok, OpenCode, Pi, ZCode                       | Source of past sessions | At least one |

```bash theme={null}
npm install -g @mindfoldhq/trellis@beta
trellis --version   # ≥ 0.6.0-beta.0
```

## Install

```bash theme={null}
npx skills add mindfold-ai/marketplace --skill mem-recall
```

Or install all marketplace skills:

```bash theme={null}
npx skills add mindfold-ai/marketplace
```

| Flag             | Description                             |
| ---------------- | --------------------------------------- |
| `-g`             | Install globally to `~/.claude/skills/` |
| `-a claude-code` | Target a specific agent                 |
| `-y`             | Non-interactive mode                    |

Ask the AI which skills are available; `mem-recall` should appear in the list.

## Trigger examples

No manual command needed. The following user messages trigger the skill:

* last time how did we solve the wait\_agent deadlock in #240?
* which project did I discuss the plugin design in?
* find what I told Claude about memory architecture last week
* 上次我们怎么处理 #240 的来着？

## Retrieval flow

The skill instructs the AI to execute two steps.

**Step 1 — Candidate search**

```bash theme={null}
trellis mem search "<keyword>" [--cwd <project>] [--since <date>]
```

Multi-token AND search across cleaned dialogue. Returns ranked sessions. Score formula: `(3 × user_hits + assistant_hits) / total_turns`. User-turn hits are weighted ×3 because user wording reflects topic intent more strongly than AI elaboration.

**Step 2 — Content extraction**

```bash theme={null}
trellis mem context <session-id> --grep <keyword> --turns 3 --around 1
```

Returns the top-N hit turns plus surrounding context. Default character budget ≤6000, adjustable via `--max-chars`.

## Cleaning before search

`trellis mem` strips the following before searching, so hits reflect actual dialogue:

* prompt injections: `<system-reminder>`, `<workflow-state>`, `<INSTRUCTIONS>`, `<environment_context>`, etc.
* Codex AGENTS.md preamble (first user message is dropped entirely)
* tool calls and tool results (only `text` blocks retained)
  Turns from before a compaction are kept, with a marker showing where the compaction happened. Content the platform does not store in readable form is reported instead of silently dropped: Codex encrypts messages between agents, and Grok keeps pre-compaction turns as rendered markdown.

## Data sources

Reads local files directly. No daemon, no index, no upload.

| Platform    | Storage                                                              |
| ----------- | -------------------------------------------------------------------- |
| Claude Code | `~/.claude/projects/<sanitized-cwd>/*.jsonl`                         |
| Codex       | `~/.codex/sessions/YYYY/MM/DD/rollout-*.jsonl`                       |
| OpenCode    | `~/.local/share/opencode/storage/{session,message,part}/...`         |
| Pi          | `~/.pi/agent/sessions/<encoded-cwd>/<timestamp>_<id>.jsonl`          |
| Grok        | `~/.grok/sessions/<url-encoded-cwd>/<session-id>/chat_history.jsonl` |

## Out-of-scope use cases

| Need                              | Tool             |
| --------------------------------- | ---------------- |
| Search code                       | `Grep` / `Read`  |
| Search commit history             | `git log` / `gh` |
| Search current-project files/docs | `Read` / `Glob`  |

mem-recall is for AI conversation history only, not file or code search.

## Performance

| Scope                        | Time    |
| ---------------------------- | ------- |
| Project-scoped 3-week search | \~0.85s |
| Global, no time filter       | \~3s    |

Stateless. Each invocation cold-reads from disk; OS page cache absorbs IO so warm and cold runs perform similarly.