Mem Recall

Cross-platform conversation memory for Claude Code, Codex CLI, Grok, Pi and ZCode. The trellis mem command reads each platform's local session storage, cleans the dialogue (strips system prompts, tool noise, hook injections, compact summaries handled correctly), and exposes a focused 5-command CLI for recall workflows. The OpenCode reader is unavailable — --platform opencode returns empty results and prints a one-shot stderr warning.

Prerequisite

Trellis CLI 0.6.14 or later installed globally:

npm install -g @mindfoldhq/trellis@latest
trellis --version

trellis mem ships bundled with the CLI; no extra setup. 0.6.14 adds Grok support and returns turns from before a compaction; earlier versions dropped them.

The OpenCode reader is unavailable: it needed a native SQLite dependency that failed to install on Windows, and was reverted. --platform opencode returns empty results and a one-shot stderr warning.

When to use this skill

Use proactively whenever the user asks any of:

• "我之前跟 Codex/Claude 讨论过 X，你了解下"
• "上次我们怎么解决 Y 的？"
• "翻一下历史看看"
• "我之前在 trellis 项目里聊过哪些 plugin 设计？"
• "find sessions about memory architecture"
• "what did I tell another AI about this last week"

Design-rationale flavour (favour --phase brainstorm for these — see below):

• "我们当时怎么决定 X 的？"
• "之前讨论过 X 的 trade-off"
• "为什么我们选了 X 而不是 Y"
• "what was the design discussion about Y"
• "the rationale for choosing X"
• "find the brainstorm where we picked Z over alternatives"

Don't second-guess. The user's intent of "use my past conversations as context" is what this skill serves. Cross-platform / cross-project recall has no alternative tool — git log only sees commits, your own memory has no record of what you said in another CLI.

The recall workflow (memorize this pattern)

Recall is a two-step drill-down, not a single query. Step 1 narrows to a session; step 2 pulls the actual content.

Step 1 (discover):  trellis mem search "<topic>" [--cwd <project>] [--since <date>]
Step 2 (drill):     trellis mem context <session-id> --grep <topic> --turns 3 --around 1

If the user is vague about which project, run trellis mem projects first to surface recently-active project cwds, then pick the most plausible one.

Commands

trellis mem projects — list active project cwds

Use first when the user references "the project" without saying which one. Shows distinct cwds across all platforms, ranked by last-active timestamp, with per-platform session counts. This is the AI-routing entry point.

trellis mem projects --since 2026-04-20 --limit 10

Output:

2026-05-04 03:42  sessions= 1 (claude:1)  ~/workspace/nb_project/mem-poc
2026-05-04 01:00  sessions=114 (claude:51 codex:63)  ~/workspace/.../Trellis
...

trellis mem search <keyword> — find candidate sessions

Multi-token AND search across cleaned dialogue. Returns ranked sessions with a chunk excerpt per match. Defaults to the current working directory; use --global to search across all projects.

trellis mem search "trellis memory" --cwd ~/workspace/.../Trellis --since 2026-04-13

Output per session:

[claude  ] 2026-04-20 11:39  4cda3c7f-8f9  ~/.../Trellis  score=6.027  hits=169 (u=27,a=142)  turns=37
    [user] 是我们的用户想要搞类似记忆系统的东西…
    [assistant] ## Memory plugin 调研结论…

How to read the score: (3 × userhits + assthits) / total_turns. Higher = the topic is concentrated AND the user themselves brought it up. User-turn hits weighted ×3 because user wording is the strongest topic signal — AI elaboration carries the same word repeatedly and inflates raw counts.

Excerpts are paragraph-aligned chunks, not char windows. They respect markdown / code-block boundaries, and prefer chunks that visibly contain ALL query tokens. When the query has multiple tokens far apart in a long turn, the chunk falls back to anchoring on the rarest token (more discriminating).

trellis mem context <session-id> — drill into a session

After search picks a candidate, use this to retrieve specific hit turns plus surrounding context. Token-budgeted for direct AI consumption.

trellis mem context 4cda3c7f --grep memory --turns 3 --around 1
# top-3 hit turns + 1 turn before/after each, ≤6000 chars total

trellis mem context 4cda3c7f --turns 5 --around 0
# no grep: returns the first 5 turns (lets you see how the session opens)

Default budget 6000 chars (~1500 tokens); per-turn cap is half that. Use --max-chars N to adjust.

trellis mem extract <session-id> — dump cleaned dialogue

Full conversation dump after platform-specific cleaning. Use for long-form inspection, not for budget-constrained recall.

trellis mem extract 4cda3c7f --grep memory   # filter to turns matching keyword
trellis mem extract 4cda3c7f --json          # structured output

trellis mem extract --phase brainstorm — slice the discussion portion

A Trellis session often opens with brainstorming (the user thinking aloud, the AI proposing options, alternatives being rejected, decisions being made), followed by implementation work once the user runs task.py start. The --phase flag slices the cleaned dialogue along that boundary so you can recover the discussion without the implementation noise — or vice versa.

The boundary: a brainstorm window is everything between task.py create ... and the matching task.py start ... Bash invocation in the same session. Multi-task sessions produce multiple windows.

Three values:

When to prefer --phase brainstorm: design-rationale questions ("为什么我们当时选了 X", "the trade-off discussion about Y", "what alternatives did we reject") have much higher signal density inside brainstorm windows than across the full session. Drop straight to extract --phase brainstorm instead of context --grep, which can land you in the middle of the implementation phase where the topic is just being referenced, not decided.

Examples:

# Single session, brainstorm only
trellis mem extract 4cda3c7f --phase brainstorm

# Multi-task session — output is split with separators:
#   --- task: my-feature ---
#   ## Human ...
#   --- task: another-task ---
#   ## Human ...
trellis mem extract 4cda3c7f --phase brainstorm

# Filter inside the brainstorm window only (--phase runs first, then --grep)
trellis mem extract 4cda3c7f --phase brainstorm --grep "trade-off"

# Structured output: get window metadata for downstream scripts / AI
trellis mem extract 4cda3c7f --phase brainstorm --json
# JSON adds:
#   "phase": "brainstorm"
#   "windows": [{ "label": "my-feature", "startTurn": 1, "endTurn": 14 }, ...]
#   "groups":  [{ "label": "my-feature", "turns": [...] }, ...]
#   "turns":   [...]   // flat concatenation, legacy-compatible

# The inverse: just the implementation work
trellis mem extract 4cda3c7f --phase implement

Platform support:

Edge cases handled gracefully:

• create found but no following start (still working on the task) → window stays open through end of session.
• start found but no preceding create (task created in an earlier session) → brainstorm window is [0, start).
• Neither found → full dialogue + stderr warning.

trellis mem list — enumerate sessions

Mostly for browsing/debugging. Project-scoped by default; --global to widen.

trellis mem list --since 2026-04-27

OpenCode child sessions show ↳ child of <parent-id> annotation (currently no-op — see OpenCode reader status above).

Flags reference

--platform claude|codex|grok|pi|zcode|opencode|all   default all
--since YYYY-MM-DD                     inclusive lower bound
--until YYYY-MM-DD                     inclusive upper bound
--global                               include all projects (default: cwd-scoped)
--cwd <path>                           override the project cwd
--limit N                              cap output (default 50)
--grep KW                              extract / context: filter turns by keyword
--turns N                              context: top-N hit turns (default 3)
--around N                             context: surrounding turns per hit (default 1)
--max-chars N                          context: char budget (default 6000)
--phase brainstorm|implement|all       extract: slice by [task.py create, start) (default all; Claude, Codex & Pi)
--include-children                     search / context: merge OpenCode sub-agent sessions into parent
--json                                 emit JSON
--help, -h                             show help

Run trellis mem help for the canonical flag reference.

Where data comes from (per platform)

The tool reads these locations directly. No daemon, no index, no upload.

Cleaning rules (what's stripped from raw data)

The tool extracts only real human-AI dialogue and strips:

• System / prompt injections: <system-reminder>, <workflow-state>, <INSTRUCTIONS>, <environmentcontext>, <permissions instructions>, <collaborationmode>, etc. (case-insensitive)
• Bootstrap turns: Codex injects AGENTS.md preamble as the first user message — entire turn is dropped, not just the tags
• Tool calls and their results: only text blocks are kept

Turns from before a compaction are kept, with a marker showing where the compaction happened (0.6.14 and later; earlier versions dropped them). Content a platform does not store readably is reported rather than silently omitted — Codex encrypts messages between agents, and Grok keeps pre-compaction turns as rendered markdown under <session>/compaction/.

This means search hits are reliable signals of "the actual conversation discussed this", not "the keyword appeared in some hook injection".

Cross-platform sub-agent semantics

--include-children only meaningfully changes behavior for OpenCode searches, so it is a no-op while that reader is unavailable.

Worked example: "what did I discuss about memory in Trellis last week?"

# 1. Confirm the project name (skip if user already named it explicitly)
trellis mem projects --since 2026-04-27
# → finds "~/workspace/.../Trellis" with 114 sessions

# 2. Find candidate sessions
trellis mem search "memory" \
  --cwd ~/workspace/.../Trellis \
  --since 2026-04-27

# → top: codex 019dcc75 (score 2.43, Codex memory subagent + Trellis hook)
#   then: claude 12d26622 (user interview about "项目记忆 4 形态")

# 3. Drill into the most relevant
trellis mem context 12d26622 --grep memory --turns 3 --around 1
# → returns the actual interview question block listing 4 memory archetypes

# 4. Now answer the user with concrete content recovered from past sessions

Don't run extract for recall unless the user explicitly wants the full session — it's expensive on token budget and rarely needed.

Citing recalled content

When you surface recovered content to the user, cite the session id + the actual quoted line. Don't say "I remember we discussed X" without backing it up — the user has no way to verify and may have meant a different conversation.

Format:

From session 12d26622 (claude, 2026-04-20):
> 是我们的用户想要搞类似记忆系统的东西…

You proposed four memory archetypes that day: …

When NOT to use this skill

• User wants to search code (use Grep / Read)
• User wants commit history (use git log / gh)
• User wants to search docs/files in current project (use Read / Glob)

This skill is specifically about recovering past AI-conversation content, not file content.

Performance notes

• Project-scoped 3-week search: ~0.85s on a typical Mac
• Global search no time filter: ~3s (whole-machine session corpus scan)
• Each invocation is stateless — no cache, no daemon. Cold runs and warm runs perform similarly because macOS / Linux page cache absorbs file reads
• For interactive use, prefer --cwd + --since to narrow the corpus