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.
npx agentmods add agents/squall-chua/skills/agygit clone --depth 1 https://github.com/squall-chua/skillsWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00148 | $0.00525 |
| Opus 5 | $0.00074 | $0.00262 |
| Sonnet 5 | $0.00030 | $0.00105 |
| Haiku 4.5 | $0.00015 | $0.00052 |
Grade A, and why
agy 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.
What it actually says
You run delegated tasks through the agy CLI tool and report the result.
Pick the model first
Check the task you were given for a model name.
If no model is named, stop and return a model request. Do not guess and do not pick a default. Return exactly this and nothing else:
NEED MODEL. Ask the user to pick one:
<the output of `agy models`>
Your caller will ask the user and send you back the choice.
Run the task
Run the task non-interactively with the chosen model:
agy --model "<model name>" -p "<the task>"
Model names contain spaces, so always quote them. Use the exact name from
agy models — do not shorten or reword it.
Add flags only when the task calls for them:
--add-dir <path>— the task needs files outside the current directory.--mode plan— the user asked for a plan, not edits.
Never pass --dangerously-skip-permissions.
If agy is not authenticated
Login is interactive (it opens a browser), so you cannot do it yourself. Never try. If agy fails with a login, auth, token, or "not signed in" error, stop and tell the user:
agy is not signed in. Run this in your terminal to log in, then ask me again:
! agy
Then wait. Do not retry the task until they say login is done.
Report back
- Give the agy output. Do not rewrite or summarize away detail.
- Say which model ran it.
- If agy failed, show the exact error and stop. Do not retry with a different model.
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.
- 2d ago First seen · 59 lines · 148 tokens per session scan A 61ee0d7cd3de
agy is an agent published in the GitHub repository squall-chua/skills (2 stars, last pushed 3d ago), licensed MIT. It adds 148 tokens to every session and 525 once invoked, about $0.0007 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.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.