Lychee AGENTS.md

A set of development instructions for LycheeOrg/Lychee, a software project. It defines how to resolve unclear requirements, record open questions, and keep specifications as the source of truth.

In plain words
What is it for?
Use it before planning or implementing changes, especially when requirements are unclear or several design options need to be compared.
Why use it?
It prevents agents from planning or coding against unstated assumptions and keeps decisions documented.

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/lycheeorg/lychee/agents-md
Clone the repo
git clone --depth 1 https://github.com/LycheeOrg/Lychee

Made for: Codex, OpenCode.

Per session 3,757 This file is loaded in full into every session.
When invoked 3,757 The same file — it is already loaded in full.
Security scan C 1 finding. 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 $0.03757 $0.03757
Opus 5 $0.01878 $0.01878
Sonnet 5 $0.00751 $0.00751
Haiku 4.5 $0.00376 $0.00376

Measured 2d ago against content hash 12d64f7df591, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade C, and why

Lychee AGENTS.md scanned grade C 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 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.

Recursive force deletehighDestructive command

rm -rf with a variable or a broad path is one typo away from removing the wrong tree.

- **No surprises.** Avoid destructive commands (e.g., `rm -rf`, `git reset --hard`) unless the user requests them. Stay within the repository sandbox.
AGENTS.md · 123 lines

How it starts

The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Before You Code

  • Clarify ambiguity first. Do not plan or implement until every requirement is understood. Ask the user, record unresolved items in docs/specs/4-architecture/open-questions.md, and wait for answers. Capture accepted answers by updating the relevant specification’s requirements/NFR/behaviour/telemetry sections so the spec remains the single source of truth for behaviour.
    • No-direct-question rule: Never ask the user for clarification, approval, or a decision in chat until the matching open question is logged (table row + Question Details entry). Treat violations as blockers—stop work, add the missing entry, then resume the conversation by referencing that question ID.
    • Whenever you present alternative approaches—whether for open questions or general solution proposals—first capture or update the entry in docs/specs/4-architecture/open-questions.md (summary row + Question Details section) so the ambiguity lives on disk, then present the stored text to the user inline in chat. Summarise the options directly in your reply (do not tell the user to open the file), call out the question ID (for example, Q013-01), and follow the numbered-heading/Options A, B, C format with pros/cons. Order the options by preference (Option A is always the recommended path, Option B the next-best, etc.) so the user sees our best advice first. Keep specifications/plans/tasks limited to that ID until the question is resolved.
  • Work in small steps. During planning, break every change into logical, self-contained tasks that are expected to complete within ≤90 minutes. Execution can take longer if required; the goal is to plan manageable increments, and commit with a conventional message for each finished slice.
  • Prime the knowledge map. Skim docs/specs/4-architecture/knowledge-map.md and the up-to-date module snapshot in docs/specs/architecture-graph.json before planning so new work reinforces the architectural relationships already captured there.
  • Template usage. Author new specifications, feature plans, and task checklists using docs/specs/templates/feature-spec-template.md, docs/specs/templates/feature-plan-template.md, and docs/specs/templates/feature-tasks-template.md so structure, metadata, and verification notes stay uniform across features.
  • ADR context. Before planning or implementation, skim ADRs under docs/specs/5-decisions whose related-features/specs entries reference the active feature ID so high-impact clarifications and architectural decisions are treated as required context alongside the roadmap, spec, plan, tasks, and knowledge map.

Read the full file on GitHub · 123 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 · 123 lines · 3,757 tokens per session scan C 12d64f7df591

Subscribe to this mod's changes

Lychee AGENTS.md is an instructions file published in the GitHub repository LycheeOrg/Lychee (4,267 stars, last pushed today), licensed MIT. It adds 3,757 tokens to every session, about $0.0188 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.