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.
npx agentmods add instructions/traxito/tokentrim/copilot-instructionsgit clone --depth 1 https://github.com/traxito/TokenTrimWrote 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/instructions/traxito/tokentrim/copilot-instructions)<a href="https://agentmods.dev/instructions/traxito/tokentrim/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/traxito/tokentrim/copilot-instructions.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 | $0.00814 | $0.00814 |
| Opus 5 | $0.00407 | $0.00407 |
| Sonnet 5 | $0.00163 | $0.00163 |
| Haiku 4.5 | $0.00081 | $0.00081 |
Grade A, and why
TokenTrim copilot-instructions.md scanned grade A with 1 finding 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 5d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- System: `journalctl`, `systemctl status`, `curl` How it starts
The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Terminal output compression with tt (TokenTrim)
To reduce token usage, prefix heavy terminal commands with tt when you run
them. tt compresses noisy command output (60-90% fewer tokens) while keeping
everything you need: it preserves exit codes and, if a command fails, it saves
the full raw output to disk and prints the path.
Prefix these commands with tt (e.g. tt git diff, tt terraform plan):
- Version control:
git status,git diff,git log,git show,git branch,git add/commit/push/pull - Tests / build:
pytest,jest,vitest,cargo test,go test,npm test,make,mvn,gradle - Containers / K8s:
docker ps,docker logs,docker images,kubectl get,kubectl logs,kubectl describe,helm,oc,podman - Cloud / IaC:
terraform plan,terraform apply,terraform validate,az ...,aws ...,gcloud ... - Packages / lint:
npm install,pnpm install,pip list,eslint,tsc,ruff,mypy - System:
journalctl,systemctl status,curl - Files:
ls,grep,find,cat
You can also compress arbitrary text before quoting it back by piping it through
tt trim (auto-detects JSON, diffs, stack traces, ML training logs, pandas
df.info(), and sklearn classification_report). For an ML training run, use
tt train <command> to get the metric curve, best epoch, and early-stop signal.
At the START of a task, run tt map once: it prints a compact overview of the
repository (directories + function/class signatures) so you do not need many
exploratory ls/cat/grep calls.
Batch work into one step (CodeAct)
When a task needs SEVERAL independent operations (read multiple files, search,
then inspect), do them in ONE tt code call instead of many separate tool
calls. Fewer turns means the whole conversation is re-sent to the model fewer
times = large token savings (this compounds when MCP servers are loaded).
Inside tt code these helpers are preloaded and already return compressed text:
glob(pattern)- list files (recursive, skips .git/node_modules/...)view(path, sig=False)- read a file (sig=True: signatures only)grep(pattern, path=".")- grouped matches (pure Python, no grep needed)sh("cmd ...")- run a shell command, return compressed outputrun("cmd1","cmd2",...)- run several, return combined compressed output
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.
- 5d ago First seen · 66 lines · 814 tokens per session scan A 75923e7082f0
TokenTrim copilot-instructions.md is an instructions file published in the GitHub repository traxito/TokenTrim (7 stars, last pushed 1mo ago), licensed MIT. It adds 814 tokens to every session, about $0.0041 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other instructions, from other repositories
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codemark GEMINI.md
Gemini CLI instructions for DanielCardonaRojas/codemark, covering development workflow and debugging with the tui logging system.
vrchat-mcp AGENTS.md
AGENTS.md instructions for BASIC-BIT/vrchat-mcp, covering agents, tool ergonomics goals (distilled), curated output philosophy, specific notes (current direction) and caching considerations (planned).
minimalist copilot-instructions.md
Instructions for DivyeshJayswal/minimalist, covering minimalist (coding tasks), persistence, fast path, bias and output.
totem AGENTS.md
AGENTS.md instructions for mmnto-ai/totem, covering totem: agent instructions, what totem is, session start protocol (mandatory), essentials and totem workflow.
front-mcp CLAUDE.md
Claude Code instructions for wearehoust/front-mcp, a project described as: Secure MCP server for Front.com: search conversations, manage contacts, send messages, automate inbox workflows — 26 tools, 172 actions.