LearnLoop copilot-instructions.md

Project instructions for LearnLoop, a coaching and team-knowledge system used by a coding agent. They define when the agent should seek coaching, look up team conventions, or save new conventions.

In plain words
What is it for?
They guide the order of checks before code tasks, detect missing team knowledge, and record new teamwide conventions.
Why use it?
They keep coding work aligned with the team's recorded decisions and working methods instead of relying only on raw files or memory.

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/bogzx/learnloop/copilot-instructions
Clone the repo
git clone --depth 1 https://github.com/Bogzx/LearnLoop

Made for: GitHub Copilot.

Per session 1,767 This file is loaded in full into every session.
When invoked 1,767 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 $0.01767 $0.01767
Opus 5 $0.00883 $0.00883
Sonnet 5 $0.00353 $0.00353
Haiku 4.5 $0.00177 $0.00177

Measured 2d ago against content hash 97cb30a05585, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

LearnLoop 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.

.github/copilot-instructions.md · 155 lines

How it starts

The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Trailhead coaching — always on, never block

Five MCP tools: coach, wiki_lookup, wiki_save, wiki_bootstrap, wiki_proven_prompts.

These tools are NOT optional. They REPLACE native Read/Grep/Glob/file-search for any question that touches team conventions, prior decisions, or "how we do X here". Calling Read or Grep before wiki_lookup is a regression that loses the team's distilled knowledge — the wiki has already curated what raw source can only hint at.

Default order of operations on every code task

For any user message that asks you to fix, add, refactor, implement, change, debug, optimize, rename, design, plan, or explain code:

  1. coach — score and (if needed) loop. ALWAYS first.
  2. wiki_lookup — once you know the file or topic. ALWAYS before Read/Grep/Glob.
  3. Write the code, applying the team conventions surfaced in step 2.
  4. If the user states a new convention, call wiki_save.

If wiki_lookup is called for a file that obviously exists and returns no results, the wiki was never bootstrapped — call wiki_bootstrap once with no arguments and stop.

coach — call BEFORE answering any code task

A code task is any user message that asks you to fix, add, refactor, implement, change, debug, optimize, rename, design, plan, or explain code. Examples: "fix the retry", "add a webhook handler", "refactor handleAuth", "why is this failing", "make this faster", "what does this function do", "how should I structure X".

  1. Call coach({ prompt: <user's exact message>, file_path: <if known> }).
  2. The tool returns { proceed, text, next_round_inputs?, ... }.
  3. If proceed: true: if text is non-empty, relay it verbatim to the user. Then produce your answer. The server bakes the graduation banner ("Your prompt scored X/10 and joined your team's library…") into text itself when mode === "score" && overall >= 7, so relaying text verbatim is sufficient — you do not need to add the sentence yourself. Done.
  4. If proceed: false: relay text verbatim, wait for the user's reply, then call coach again with:
    • prompt: the user's reply concatenated to the previous prompt
    • mode: "score"
    • round_token: echo the next_round_inputs.round_token string verbatim. This single field replaces the legacy four-field echo (original_prompt, original_dimensions, previous_dimensions, round) — fewer slots for the LLM to drop. The four fields are still accepted as a fallback if you can't echo the token. Loop. The server enforces the cap (5 rounds) and bails on no-progress.

Read the full file on GitHub · 155 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 · 155 lines · 1,767 tokens per session scan A 97cb30a05585

Subscribe to this mod's changes

LearnLoop copilot-instructions.md is an instructions file published in the GitHub repository Bogzx/LearnLoop (4 stars, last pushed 15d ago), licensed MIT. It adds 1,767 tokens to every session, about $0.0088 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.