starling AGENTS.md

Repository instructions for Starling, a collection of community add-ons called skills for Hawk. They describe the project layout, development process, validation commands, and boundaries for related projects.

In plain words
What is it for?
Use them when developing in the Starling repository, validating one skill, updating its registry, or running the full test and lint checks.
Why use it?
They make contribution rules explicit, including creating a feature branch, checking code quality, updating the registry, and avoiding unsupported internal references. This reduces mistakes when adding or changing a skill.

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/graycodeai/starling/agents-md
Clone the repo
git clone --depth 1 https://github.com/GrayCodeAI/starling

Made for: Codex, OpenCode.

Per session 1,073 This file is loaded in full into every session.
When invoked 1,073 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin 78% copy Near-identical to another mod 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 $0.01073 $0.01073
Opus 5 $0.00536 $0.00536
Sonnet 5 $0.00215 $0.00215
Haiku 4.5 $0.00107 $0.00107

Measured yesterday against content hash 4bdfd42b95a5, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

starling 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.

Origin

This is a copy

78% identical to tabularis AGENTS.md — 68 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

AGENTS.md · 94 lines

How it starts

The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.

starling Conventions

Community skill packages for hawk.

Development workflow

When starting any new work (feature, fix, refactor, chore), always create a feature branch from main first. Never commit directly to main. Use branch naming conventions like feat/<description>, fix/<description>, or chore/<description>. Open a PR, ensure CI is green, then merge.

Structure

categories/<category>/<skill-name>/
├── SKILL.md              # Required
├── templates/            # Optional
├── examples/             # Optional
└── scripts/              # Optional

Validation

# Validate a single skill
python tools/validate_skill.py categories/python/mdc-fastapi/SKILL.md

# Update registry after adding/removing skills
python tools/update_registry.py

# Full test suite
pytest

# Lint
ruff check .
ruff format --check .

Ecosystem Boundaries

  • Extends Hawk through public skill and plugin surfaces only
  • Do not reference support engine repos (eyrie, yaad, tok, trace, sight, inspect)
  • Do not reference hawk/internal/* or removed legacy paths

For full hawk-eco extension guidelines, see hawk/AGENTS.md.

GitNexus — Code Intelligence

This project is indexed by GitNexus as starling (210621 symbols, 225507 relationships, 136 execution flows). Use the GitNexus MCP tools to understand code, assess impact, and navigate safely.

Index stale? Run node .gitnexus/run.cjs analyze from the project root — it auto-selects an available runner. No .gitnexus/run.cjs yet? npx gitnexus analyze (npm 11 crash → npm i -g gitnexus; #1939).

Always Do

  • MUST run impact analysis before editing any symbol. Before modifying a function, class, or method, run impact({target: "symbolName", direction: "upstream"}) and report the blast radius (direct callers, affected processes, risk level) to the user.
  • MUST run detect_changes() before committing to verify your changes only affect expected symbols and execution flows. For regression review, compare against the default branch: detect_changes({scope: "compare", base_ref: "main"}).
  • MUST warn the user if impact analysis returns HIGH or CRITICAL risk before proceeding with edits.
  • When exploring unfamiliar code, use query({search_query: "concept"}) to find execution flows instead of grepping. It returns process-grouped results ranked by relevance.
  • When you need full context on a specific symbol — callers, callees, which execution flows it participates in — use context({name: "symbolName"}).
  • For security review, explain({target: "fileOrSymbol"}) lists taint findings (source→sink flows; needs analyze --pdg).

Read the full file on GitHub · 94 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 · 94 lines · 1,073 tokens per session scan A 4bdfd42b95a5

Subscribe to this mod's changes

starling AGENTS.md is an instructions file published in the GitHub repository GrayCodeAI/starling (2 stars, last pushed 2d ago), licensed MIT. It adds 1,073 tokens to every session, about $0.0054 per session on Opus 5. A static security scan graded it A with 0 findings. It is 78% identical to tabularis AGENTS.md, differing in 68 lines, and is treated as a copy.