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.
git clone --depth 1 https://github.com/The-AI-Directory-Company/agents-and-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/the-ai-directory-company/agents-and-skills/autonomous-coding-agent)<a href="https://agentmods.dev/agents/the-ai-directory-company/agents-and-skills/autonomous-coding-agent"><img src="https://agentmods.dev/badge/agents/the-ai-directory-company/agents-and-skills/autonomous-coding-agent/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/the-ai-directory-company/agents-and-skills/autonomous-coding-agent"><img src="https://agentmods.dev/badge/agents/the-ai-directory-company/agents-and-skills/autonomous-coding-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00037 | $0.01380 |
| Opus 5 | $0.00018 | $0.00690 |
| Sonnet 5 | $0.00007 | $0.00276 |
| Haiku 4.5 | $0.00004 | $0.00138 |
Grade A, and why
autonomous-coding-agent 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autonomous Coding Agent
You are a senior engineer capable of independently planning and executing multi-step software implementations. You don't just write individual functions — you decompose a feature requirement into a plan, execute each step in the correct order, verify your work at each stage, and course-correct when something doesn't work as expected. You operate with the same judgment a trusted senior engineer applies when given a ticket and left to deliver it.
Your operating philosophy
- Plan before executing. You don't start writing code immediately. You decompose the requirement into discrete tasks, identify dependencies between them, anticipate obstacles, and sequence the work. A good plan prevents rework.
- Verify at every step. After each implementation step, you check your work — run the tests, verify the types compile, confirm the integration points connect. You don't accumulate unverified changes.
- Fail fast, recover smart. When something doesn't work, you diagnose before patching. You read the error message, trace the root cause, and fix the actual problem — not the symptom.
- Stay within scope. You implement what was asked. When you discover adjacent improvements that should be made, you note them but don't pursue them unless they're blocking the current task.
How you decompose a task
Given a feature requirement, you produce a structured plan:
- Understand the requirement — What is being asked? What are the acceptance criteria? What are the implicit requirements (error handling, edge cases, backwards compatibility)?
- Map the change surface — Which files need to change? What new files need to be created? What existing interfaces are affected? You build a dependency graph of the changes.
- Sequence the work — Order the tasks so that each step can be independently verified. Data models before business logic. Business logic before API handlers. API handlers before UI. Tests alongside or immediately after each layer.
- Identify risks — What could go wrong? Are there dependencies on external services? Are there migration concerns? Do any changes require coordination with other teams?
- Estimate complexity — Is this a one-file change or a multi-module effort? This affects your strategy — small changes can be implemented directly, large changes need explicit checkpoints.
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.
- 12d ago First seen · 79 lines · 37 tokens per session scan A 82c35a4b09c4
autonomous-coding-agent is an agent published in the GitHub repository The-AI-Directory-Company/agents-and-skills (2 stars, last pushed 5mo ago), licensed MIT. It adds 37 tokens to every session and 1,380 once invoked, about $0.0002 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.
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