loopback AGENTS.md

A set of AGENTS.md instructions for working through Loopback, a repository’s queue of user feedback and product issues. It explains how to inspect, claim, investigate, fix, and report back on each item.

In plain words
What is it for?
Processing Loopback feedback one item at a time, prioritising severe issues, avoiding conflicting claims, examining failed responses, and recording the outcome for the reporter.
Why use it?
It gives coding agents a defined process for handling real user reports, including the captured network failures and supporting context.

Instructions file for CodexOpenCode

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/joshidikshant/loopback/agents-md
Clone the repo
git clone --depth 1 https://github.com/joshidikshant/loopback

Made for: Codex, OpenCode.

Per session 966 This file is loaded in full into every session.
When invoked 966 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 $0.00966 $0.00966
Opus 5 $0.00483 $0.00483
Sonnet 5 $0.00193 $0.00193
Haiku 4.5 $0.00097 $0.00097

Measured yesterday against content hash 655df1e92198, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

loopback AGENTS.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 yesterday.

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.

AGENTS.md · 73 lines

How it starts

The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agent instructions

Working the Loopback queue

Loopback is the feedback bus for this repo. Real product usage — pinned UI feedback, backend failures with response bodies, UX papercuts, feedback on AI features with their run metadata — lands in one queue. Your job is to close loops: fix what real usage surfaced, and write the outcome back so the reporter sees the pin turn green.

Project slug for this repo: loopback.

The loop

Work one item at a time, most severe first:

  1. loopback_list_feedback(project="loopback", status="open") — see what users actually hit. Check triaged too if the open queue is empty.
  2. loopback_claim_feedback(id, agent=<your name>) — claim as your own agent name: claude-code, codex, gemini, or your CLI's name. If the claim is rejected, another agent holds it — pick a different item. Never force-claim (force=true) without first stating why in a loopback_add_comment.
  3. loopback_get_feedback(id) — read ALL captured context before touching code. A pin is an anchor, not a scope: a pin on a form button often carries the backend root cause. Specifically read:
    • network and extra.failed_responses — failing calls with up to 2KB of response body (error codes, hints, stack fragments);
    • console and repro_steps;
    • extra.contextrun_id / model / trace_url when the feedback is on an AI or automation feature. Chase the trace, not just the DOM.
  4. Comment your root-cause diagnosis via loopback_add_comment before fixing — it is the audit trail that makes the queue trustworthy.
  5. Fix it in this repo. Smallest change that addresses what was reported.
  6. loopback_link_change(id, repo, branch, commit, pr_url, diff_summary) — the fix must be traceable from the feedback item.
  7. loopback_update_status(id, status="fixed", note=...).
  8. Verify for real, don't assume: UI issues → drive the running app with your browser tool/MCP; backend or logic → run the tests or hit the endpoint; usage/metric issues → check the metric or replay.
  9. Only after verification: loopback_resolve_feedback(id, resolution="verified", note=..., agent=<your name>). Pass agent — this is the write that turns the reporter's pin full green, and without it the trail records the default "agent" instead of you. Use wontfix with a reason when intentionally not fixing. Resolving flips the reporter's pin green — do not claim it until it is true.

Read the full file on GitHub · 73 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. yesterday First seen · 73 lines · 966 tokens per session scan A 655df1e92198

Subscribe to this mod's changes

loopback AGENTS.md is an instructions file published in the GitHub repository joshidikshant/loopback (0 stars, last pushed 1mo ago), licensed MIT. It adds 966 tokens to every session, about $0.0048 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.