agentmods.dev

tm-save

A command that saves useful long-term details from the current session to Trailmem, a memory store for later sessions. It focuses on decisions, lessons, follow-up tasks, and lasting preferences.

In plain words
What is it for?
Use it at the end of a session to record durable decisions, known issues, open work, constraints, or user preferences while they are still available.
Why use it?
It preserves important context before the session ends, when a later automatic hook would no longer have access to the conversation.

Command

1 1mo ago A scan 13 tokens original MIT
View the source file amitnexTech/trailmem
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add commands/amitnextech/trailmem/tm-save
Clone the repo
git clone --depth 1 https://github.com/amitnexTech/trailmem
Per session 13 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 368 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00013 $0.00368
Opus 5 $0.00006 $0.00184
Sonnet 5 $0.00003 $0.00074
Haiku 4.5 $0.00001 $0.00037

Measured 2d ago against content hash 91adbc975943, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

tm-save 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 2d 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.

trailmem/commands/tm-save.md · 34 lines

What it actually says

Capture the durable memory from THIS session into trailmem now, while the full conversation is still in context. A host end-of-session hook cannot do this — it runs after you are gone and never sees the conversation — so this is the reliable capture point.

Do the following:

  1. Review the conversation and identify what is worth persisting across sessions:

    • decisions — a rule, tool choice, structure, or enforced behavior we settled on
    • lesson — a bug, mistake, or non-obvious thing learned (include the why)
    • task — concrete follow-up work that is still open
    • constraint / user_preference — a durable rule or personal preference Skip ephemeral chatter, things already stored, and anything derivable from code or git history.

  2. For each item, call trailmem_store with:

    • content in English (this is a hard rule even if we spoke another language),
    • the correct event_type from the list above,
    • a link to a related existing memory (link_to + edge_type) so it is not an orphan — query first if unsure what to link to.

  3. Respect dedup: if trailmem_store reports a near-duplicate, update the existing memory with trailmem_edit instead of forcing a second copy.

  4. If nothing this session is genuinely worth persisting, say so plainly ("nothing durable to save this session") — do NOT invent filler memories.

After saving, give a one-line summary of what you stored (ids + titles) so the user can confirm before exiting.

Changes

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.

  1. 2d ago First seen · 34 lines · 13 tokens per session scan A 91adbc975943

Subscribe to this mod's changes

tm-save is a command published in the GitHub repository amitnexTech/trailmem (1 stars, last pushed 1mo ago), licensed MIT. It adds 13 tokens to every session and 368 once invoked, about $0.0001 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 2026-08-31.

Memory and contextai-agentsclaudeembeddingsknowledge-graphlocal-firstmcp
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