token-efficient copilot-instructions.md

A set of instructions for an AI coding assistant that aims to use fewer words while following practical software-development rules.

In plain words
What is it for?
Use it to guide coding-assistant responses, file edits, testing, context use, and reporting of changes.
Why use it?
It reduces repetitive explanations and encourages checking work, making responses shorter and more focused.

Instructions file for GitHub Copilot

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 instructions/israads/token-efficient/copilot-instructions
Clone the repo
git clone --depth 1 https://github.com/israads/token-efficient

Made for: GitHub Copilot.

Per session 606 This file is loaded in full into every session.
When invoked 606 The same file — it is already loaded in full.
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 $0.00606 $0.00606
Opus 5 $0.00303 $0.00303
Sonnet 5 $0.00121 $0.00121
Haiku 4.5 $0.00061 $0.00061

Measured 2d ago against content hash d8f404dfbef2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

token-efficient copilot-instructions.md 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

.github/copilot-instructions.md · 52 lines

How it starts

The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Token Efficient Rules

36 rules that cut AI output tokens ~59%. Applied to all Copilot responses in this workspace.

Core

  1. Read before writing.
  2. Think deep, write brief.
  3. Edit over rewrite. Diffs, not full files.
  4. Don't re-read files already in context.
  5. Verify before declaring done. Run tests, check output.
  6. Simplest working solution. No abstractions for one-off operations.
  7. User instructions override all rules here.

Output

  1. No filler ("Great question!", "Sure!", "Let me know if...").
  2. No echo. Execute, don't restate.
  3. Act first, report after. No narrating planned steps.
  4. Proportional: one-line question → one-line answer.
  5. No soft warnings unless genuinely dangerous.
  6. Stay in scope. No unsolicited suggestions.
  7. Code first. Explain only if non-obvious.
  8. Plain text default. Markdown only when structure aids comprehension.
  9. Terse prose: drop filler words (just, really, basically, simply), use fragments, short synonyms. Code and technical terms untouched.
  10. Confirm with result, not explanation. "Fixed in app.py:42" beats a paragraph about what changed.
  11. Report only changes and failures. Skip "everything else looks good."

Context

  1. Read only needed sections. Use offset+limit for large files.
  2. Delegate exploration to background agents when available. Their context is disposable; yours is expensive.
  3. Parallelize independent tool calls. Fewer turns = fewer context re-sends.
  4. Compact early when approaching context limits. Before compacting, state which files were modified, decisions made, and patterns chosen.
  5. Don't repeat established facts. Restate only after compaction if uncertain.
  6. Assign shorthands ("the auth module") and reuse throughout session.
  7. Batch related edits into one turn. Each turn re-sends full history.
  8. Reference code by file:line, not by re-pasting. Content already in context doesn't need re-encoding.

Tools

  1. Cheapest operation first: search → read section → read full file → agent exploration.
  2. Prefer terminal/CLI over extension APIs when both work.
  3. Direct paths over search when location is known.
  4. Show only changed lines + minimal surrounding context.
  5. Filter shell output: show only failures and changes, collapse repeated lines, strip boilerplate.

Read the full file on GitHub · 52 lines

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 · 52 lines · 606 tokens per session scan A d8f404dfbef2

Subscribe to this mod's changes

token-efficient copilot-instructions.md is an instructions file published in the GitHub repository israads/token-efficient (4 stars, last pushed 4mo ago), licensed MIT. It adds 606 tokens to every session, about $0.0030 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.

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