Borrowing it
Nothing to install: this file belongs to tincopper/neeko. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/tincopper/neeko/main/.agents/skills/trellis-session-insight/SKILL.mdgit clone --depth 1 https://github.com/tincopper/neekoWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tincopper/neeko/trellis-session-insight)<a href="https://agentmods.dev/skills/tincopper/neeko/trellis-session-insight"><img src="https://agentmods.dev/badge/skills/tincopper/neeko/trellis-session-insight.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00140 | $0.01487 |
| Opus 5 | $0.00070 | $0.00744 |
| Sonnet 5 | $0.00028 | $0.00297 |
| Haiku 4.5 | $0.00014 | $0.00149 |
Grade A, and why
trellis-session-insight scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
92% identical to trellis-session-insight — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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, and Pi Agent conversation logs (the JSONL files each platform stores under ~/.claude/projects/, ~/.codex/sessions/, and ~/.pi/agent/sessions/) and lets you list, search, slice by Trellis task boundaries, and dump cleaned dialogue from them. OpenCode logs are not yet indexable (provider adapter pending) — when an OpenCode session is the obvious target, surface that limitation rather than guessing.
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.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 82 lines · 140 tokens per session scan A f6ad80eaca21
trellis-session-insight is a skill published in the GitHub repository tincopper/neeko (11 stars, last pushed 4d ago), licensed Apache-2.0. It adds 140 tokens to every session and 1,487 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to trellis-session-insight, differing in 4 lines, and is treated as a copy.
Other skills, from other repositories
persistent-notes
Save notes locally to /mnt/workspace/notes.json file. Use when user wants to "save a note" or "remember something".
session-summaries
What the chat right-panel session summary shows, what it costs, and how to make a session summarize well. Load when the user asks about the session summary panel, why a summary looks wrong or empty, or how to turn it on.
session-sync
Sync session transcripts, memories, settings and handoff documents between this machine and the same project on a remote host.
list-learned-actions
Explicit Codex workflow: List persisted reusable actions, UI skeletons, and legacy feedback memories before composing device primitives.
hindsight-architect
Expert memory architect. Understands your application, identifies where memory adds value, and produces an implementation plan with bank config, tag schema, and code.
hindsight-local
Store user preferences, learnings from tasks, and procedure outcomes. Use to remember what works and recall context before new tasks. (user).