Agent skill

Trellis Session Insight

by mindfold-ai in mindfold-ai/Trellis

Reach into past AI conversation history through the trellis mem CLI.

AGPL-3.0Auto-check passedDevelopment

Install Trellis Session Insight

skills CLI
$ npx skills add mindfold-ai/Trellis --skill trellis-session-insight -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mindfold-ai/Trellis trellis-session-insight --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mindfold-ai/Trellis.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.pi/skills/trellis-session-insight .claude/skills/trellis-session-insight && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
trellis-session-insight
GitHub stars
15k
Used in
4 other repos
Token cost
~1.7k tokens
SKILL.md length
811 words
Files
3 (incl. references)
Skills in repo
8
Repo updated
First seen
Licence
AGPL-3.0

At a glance

Reach into past AI conversation history through the trellis mem CLI.

  • The user asks how did we solve X last time
  • SKILL.md covers What trellis mem is, When to reach for it, When NOT to reach for it and What to do with what mem returns, plus 3 more sections
  • Calls git
  • Have we discussed this before

What it does

Trellis Session Insight is an agent skill from mindfold-ai/Trellis. Reach into past AI conversation history through the trellis mem CLI. Use whenever the user asks 'how did we solve X last time', 'have we discussed this before', 'what was the decision on X', 'remind me what we did in this task', '上次怎么解的', '之前讨论过吗', '想起一段对话', or when starting a brainstorm that overlaps prior work, debugging a familiar bug, continuing a task across sessions, or doing a finish-work review. Returns raw past dialogue; decide for the moment whether to update spec, append to task notes, quote inline in…

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/cli-quick-reference.md` and `references/triggering-patterns.md`).

It sits in Development, covering Brainstorming and Debugging. The repository describes itself as: The best agent harness. The licence is AGPL-3.0.

When your agent uses it

  • The user asks how did we solve X last time
  • Have we discussed this before
  • What was the decision on X
  • Remind me what we did in this task

Example prompts

  • “how did we solve X last time”
  • “have we discussed this before”
  • “what was the decision on X”
  • “/trellis-session-insight”

What it can do on your machine

Read from SKILL.md and the folder at commit f089cb3. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Trellis Session Insight loads about 1.7k tokens when it runs, and up to ~4.4k if it reads all its reference files. Until then it costs about 144 tokens; SKILL.md has 811 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~144
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from mindfold-ai/Trellis at commit f089cb3, republished under its AGPL-3.0 licence (© mindfold-ai). 811 words, ~1,675 tokens.

Download SKILL.mdSave it as .claude/skills/trellis-session-insight/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
trellis-session-insight
description
Reach into past AI conversation history through the `trellis mem` CLI. Use whenever the user asks 'how did we solve X last time', 'have we discussed this before', 'what was the decision on X', 'remind me what we did in this task', '上次怎么解的', '之前讨论过吗', '想起一段对话', or when starting a brainstorm that overlaps prior work, debugging a familiar bug, continuing a task across sessions, or doing a finish-work review. Returns raw past dialogue; decide for the moment whether to update spec, append to task notes, quote inline in the answer, or just internalize.

Trellis Session Insight

This skill teaches an AI how to call trellis mem — the project's cross-session memory feedstock — and when reaching for it is the right move.

It is intentionally a capability skill, not a workflow. There is no fixed output file, no required write-back step, no "always run after finish-work" rule. What to do with what mem returns is a judgement call made in the moment of the conversation. The skill exists so the AI knows the capability is there and can decide.

What trellis mem is

A local CLI that indexes the user's past Claude Code, Codex, Devin CLI, Grok, OpenCode, Pi Agent, and ZCode conversation logs and lets you list, search, slice by Trellis task boundaries, and dump cleaned dialogue from them. Claude and Codex use ~/.claude/projects/ and ~/.codex/sessions/. Devin CLI (Cognition terminal agent, not trellis init --devin Desktop) uses ~/.local/share/devin/cli/sessions.db. Grok uses ~/.grok/sessions/. OpenCode uses ~/.local/share/opencode/opencode.db (zero-dependency SQLite reader). Pi uses its default or environment-configured session root, global ~/.pi/agent/settings.json, and the scoped project's .pi/settings.json; relative sessionDir values resolve from the settings file directory. Project-local Pi settings require project-scoped lookup through the current cwd or --cwd. ZCode uses ~/.zcode/cli/db/db.sqlite.

Nothing in mem is uploaded. All reads are local.

When to reach for it

The bar is "would a senior teammate ask 'didn't we already talk about this?'" — those are the moments. Some concrete patterns:

  • Brainstorm rerun risk. Starting a new task that touches an area the user has been in before, and you want to check whether a decision was already made — before re-asking the user.
  • Familiar-bug debugging. The current bug pattern feels like one the user reported / fixed before. Pulling the relevant past session can save a full debugging loop.
  • Cross-session continuation. The user resumes work after a gap and says "where were we" / "继续上次的" without being specific.
  • Decision retrieval. The user references "the decision we made about X" but the decision lives in an old brainstorm, not in any prd.md / spec/.
  • Finish-work retrospective. When the user explicitly asks for a wrap-up of what was decided / what hurt / what surprised them in this task — not as a forced step on every finish-work.
  • Pattern-spotting across past work. The user asks "do I keep making the same mistake on X" / "我每次都踩这个坑吗" — search across sessions answers that.

If none of these apply, don't call mem. It is a tool, not a ceremony.

When NOT to reach for it

  • The relevant context is already in the current turn, prd.md, design.md, recent git log, or the open files. mem is for stuff that has fallen out of immediate reach.
  • The user is asking about a fact in the code, not a fact from a past conversation. git log -p / grep / reading the file directly is faster and more authoritative.
  • You are in a sub-agent (trellis-implement / trellis-check) whose dispatch prompt already includes the curated implement.jsonl / check.jsonl context. Adding mem on top usually just clutters.
  • The user has explicitly said "don't dig through history, just answer what I asked".
Show full SKILL.md (315 more words)Show less

What to do with what mem returns

Treat the output as raw material, not a deliverable. Once you have it, decide based on the live conversation:

  • Quote inline in your reply if a specific past exchange answers the user's current question — and cite the session-id / phase so the user can verify.
  • Update <task>/prd.md or <task>/design.md if mem surfaced a load-bearing decision that should have been written down but wasn't. Surface the proposed edit to the user first.
  • Append to a task-local notes file (e.g. <task>/notes.md or extending an existing one) if the finding belongs to the current task's record but doesn't fit the PRD.
  • Update .trellis/spec/ if the finding is a project-wide convention or gotcha that would help future tasks. Run the trellis-update-spec skill for that — session-insight ends at the discovery.
  • Just absorb it for the next few turns and answer better, without writing anything. This is often the right move for one-off recall.

Trellis does not prescribe a single destination. Forcing every recall into a fixed file makes the file grow into noise. Let the situation decide.

How to call it

Full CLI reference is in references/cli-quick-reference.md. The 80% case is one of:

bash
# Find sessions whose contents mention a keyword (project-scope is default;
# add --global to search every project on this machine).
trellis mem search "<keyword>"

# Dump dialogue from one session, optionally filtered by phase or keyword.
trellis mem extract <session-id> --phase brainstorm
trellis mem extract <session-id> --grep "<keyword>"

# Drill into a session: top-N hit turns + surrounding context.
trellis mem context <session-id> --turns 3 --around 2

# When you do not know the session id yet, start with list + filter.
trellis mem list --cwd <project-path>
trellis mem projects   # → list active project cwds, then narrow

Phase slicing (--phase brainstorm|implement|all) cuts the session at task.py create and task.py start boundaries. For a finish-work review of the current task, --phase brainstorm recovers the planning discussion and --phase implement recovers the execution loop. Default is all.

Triggering patterns

references/triggering-patterns.md lists more verbatim user phrasings (English + Chinese) that should make you think "reach for mem" — keep that handy when training instinct.

Out of scope

  • mem does not edit code or update files. Any write-back is your decision in the moment.
  • mem is read-only on the platform JSONL stores. It does not push or sync to remote.
  • This skill does not replace trellis-update-spec (which is the right tool for promoting a finding into project-wide guidance) or the platform-native task / spec workflow.

© mindfold-ai, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in .pi/skills/trellis-session-insight of mindfold-ai/Trellis.

  • SKILL.md
  • references/cli-quick-reference.md
  • references/triggering-patterns.md

Open the folder on GitHubat commit f089cb3

Used in 4 other repositories

We found 15 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 4 other GitHub owners. This page covers the copy in mindfold-ai/Trellis, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Trellis Session Insight next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Trellis Session Insight compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Trellis Session Insight this skillmindfold-ai/Trellis15k4 repos~1.7kAutomated safety check: PassAGPL-3.0
Context FieldsNeoVertex1/context-field147—~1.3kAutomated safety check: PassNone
Brainstormingfeiskyer/claude-code-settings1.7k—~985Automated safety check: PassMIT
ScopeAlexZio00/sovereign-skills140—~4kAutomated safety check: PassMIT
Comet Hotfixrpamis/comet3.2k—~2.6kAutomated safety check: PassMIT
Odoo Workflowunclecatvn/agent-skills143—~4.7kAutomated safety check: PassMIT

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Questions about Trellis Session Insight

What does Trellis Session Insight do?

Reach into past AI conversation history through the trellis mem CLI. Trellis Session Insight is an agent skill from mindfold-ai/Trellis. Reach into past AI conversation history through the trellis mem CLI.

When should I use Trellis Session Insight?

Trellis Session Insight fits situations like: the user asks how did we solve X last time; have we discussed this before; what was the decision on X; remind me what we did in this task.

How do I install Trellis Session Insight in Claude Code?

Run `npx skills add mindfold-ai/Trellis --skill trellis-session-insight -a claude-code`. Or copy the skill folder (.pi/skills/trellis-session-insight in mindfold-ai/Trellis) into .claude/skills/trellis-session-insight in your project. Claude Code loads it when a task matches its description.

How do I install Trellis Session Insight in Codex?

Run `npx skills add mindfold-ai/Trellis --skill trellis-session-insight -a codex`. Or copy the skill folder (.pi/skills/trellis-session-insight in mindfold-ai/Trellis) into .agents/skills/trellis-session-insight in your project. Codex loads it when a task matches its description.

Can I use Trellis Session Insight in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mindfold-ai/Trellis --skill trellis-session-insight -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/trellis-session-insight, .gemini/skills/trellis-session-insight, .github/skills/trellis-session-insight and .opencode/skills/trellis-session-insight in your project.

What does Trellis Session Insight need to run?

Going by SKILL.md and its folder, Trellis Session Insight needs the command-line tools its instructions call (git).

Does Trellis Session Insight access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Trellis Session Insight safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Trellis Session Insight use?

Trellis Session Insight is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Trellis Session Insight use?

About 1.7k tokens (SKILL.md is roughly 6.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.8k tokens, read only when the agent opens those files.

What are the alternatives to Trellis Session Insight?

Skills that share tags, products or a category with Trellis Session Insight: Context Fields (NeoVertex1/context-field, 147 stars), Brainstorming (feiskyer/claude-code-settings, 1.7k stars), Scope (AlexZio00/sovereign-skills, 140 stars) and Comet Hotfix (rpamis/comet, 3.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Trellis Session Insight?

mindfold-ai (a GitHub organization) maintains it in mindfold-ai/Trellis, which has 14,901 GitHub stars. The repository holds 8 skills in this directory. The repository was last updated on September 29, 2026.

Source: mindfold-ai/Trellis on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.