Trellis is an engineering framework that stores project specifications, tasks, and working memory in a repository so coding agents can follow consistent development practices across sessions. Teams use it to organize AI-assisted planning, implementation, review, and validation across multiple coding platforms. The catalogue entries provide Trellis commands, agents, hooks, skills, instructions, and settings.
Borrowing it
Nothing to install: this file belongs to mindfold-ai/Trellis. 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/mindfold-ai/Trellis/main/.agents/skills/trellis-session-insight/SKILL.mdgit clone --depth 1 https://github.com/mindfold-ai/TrellisWrote 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/mindfold-ai/trellis/trellis-session-insight)<a href="https://agentmods.dev/skills/mindfold-ai/trellis/trellis-session-insight"><img src="https://agentmods.dev/badge/skills/mindfold-ai/trellis/trellis-session-insight/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mindfold-ai/trellis/trellis-session-insight"><img src="https://agentmods.dev/badge/skills/mindfold-ai/trellis/trellis-session-insight.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.01551 |
| Opus 5 | $0.00070 | $0.00776 |
| Sonnet 5 | $0.00028 | $0.00310 |
| Haiku 4.5 | $0.00014 | $0.00155 |
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 9d 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.
The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 9d ago First seen · 82 lines · 140 tokens per session scan A d20f1d20c694
trellis-session-insight is a skill published in the GitHub repository mindfold-ai/Trellis (14,527 stars, last pushed 12d ago), licensed AGPL-3.0. It adds 140 tokens to every session and 1,551 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
rgit-digest
Use when a research-git digest queue has pending units (rgit digest status) — after rgit init staged a history-digestion plan, or when the user asks to backfill, digest, or import git history into capsules. Batches run on the host session's subscription and progress is resumable, so partial sessions are fine.
rgit-recall
Use when the user wants to recall, resurrect, bring back, or re-apply a previously captured feature/idea onto today's codebase (e.g. "bring back the re-ranking retrieval step").
project-memory
GitHub-native persistence for project learnings, decisions, and patterns using issue labels and structured comments. Used when recording what worked, what failed, or architectural decisions that should persist across sessions.
wiki
LLM Wiki — persistent markdown knowledge base that compounds across sessions (Karpathy model).
handoff
Resume the most recent agent session for the current working directory, leading with any unanswered question. Use when the user says "where were we", "resume", "handoff", "pick up where I left off", or starts a session with no fresh context.
agentmemory-agents
How agentmemory wires into host coding agents via the connect command. Use when installing agentmemory into a specific agent, when asked which agents are supported, or when a connect adapter writes the wrong config path.