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 skills add oyi77/1ai-skills --skill autonomousgit clone --depth 1 https://github.com/oyi77/1ai-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/skills/oyi77/1ai-skills/autonomous)<a href="https://agentmods.dev/skills/oyi77/1ai-skills/autonomous"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/autonomous/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/skills/oyi77/1ai-skills/autonomous"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/autonomous.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 161 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 161 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00050 | $0.03011 |
| Opus 5 | $0.00025 | $0.01505 |
| Sonnet 5 | $0.00010 | $0.00602 |
| Haiku 4.5 | $0.00005 | $0.00301 |
Grade A, and why
autonomous scanned grade A 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 today.
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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
result = subprocess.run(args, capture_output=True, text=True) How it starts
The opening of the file, as written. The whole thing — 392 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
This skill is the hub for five specialized autonomous agents — code, deploy, planning, research, and review — that decompose and execute large tasks without constant supervision. Load it when you need to staff a job across multiple expert roles and coordinate their outputs. It dispatches each agent with the right context, then integrates the results into a finished deliverable.
Autonomous Agents
Money-Making Overview
| Agent | Revenue Impact | Avg. Savings | Best For |
|---|---|---|---|
| Planning Agent | Eliminates 70% of rework | $500–2,000/feature | Complex multi-file changes, migrations |
| Research Agent | Cuts tech evaluation to 15 min | $200–800/decision | Library selection, competitor analysis |
| Code Agent | Ships 5–10x faster per story | $1,000–5,000/feature | Full feature implementation, bug fixes |
| Review Agent | Catches P1 bugs before production | $2,000–10,000/incident | PR review, security audit, regression check |
| Deploy Agent | Zero-downtime, automated rollbacks | $500–3,000/deploy | CI/CD, migrations, hotfixes |
Combined ROI: A single pipeline (plan → research → code → review → deploy) saves $10,000–25,000 per release cycle by eliminating rework, catching bugs early, and automating deployments.
When to Use
Plan first — before any feature touching 3+ files, ambiguous requirements, or multi-agent coordination.
Research before buying — evaluating a library, investigating a root cause, competitive analysis.
Code from spec — implementing features, fixing bugs with known cause, writing modules/services/libs.
Review before merge — every PR, refactoring audit, security check, pre-deploy safety gate.
Deploy with gates — shipping to staging/production, CI/CD changes, rollbacks, migrations, hotfixes.
When NOT to Use
- Single-command tasks — just run the command.
- Real-time human judgment calls.
- Agent lacks tool access or required data.
- Security agent needs to run security checks (see that dedicated skill).
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- today Changed · +11 lines 9bbfa943e044
- 10d ago First seen · 381 lines · 50 tokens per session scan A d68fb0e7a4be
autonomous is a skill published in the GitHub repository oyi77/1ai-skills (12 stars, last pushed today), licensed MIT. It adds 50 tokens to every session and 3,011 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
review-loop
Iterative code review — fix P1+P2 until clean, report P3 as an offer.
code-review
Comprehensive code review for security, performance, and best practices.
drug-design
End-to-end drug discovery pipeline orchestration. Deterministic Python script that auto-chains structure prediction, pocket detection, de novo design, docking, scoring, and ADMET filtering into reproducible workflows.
dispatch
Use when a task file exists in .hyperflow/tasks/ and workers need dispatching. Fans out parallel workers under per-batch Reviewers, runs a final integration review, and commits per sub-task. Endpoint of the auto-chain — no auto-deploy. Trigger with /hyperflow:dispatch, "run the plan", "execute the task", "build it"…
golang-refactoring
Golang refactoring — safe, at-scale restructuring of existing Go code: a coverage-adaptive safety net, behavior-preserving transforms (gopls Rename/Extract, gofmt -r, gopatch), the Fowler catalog mapped to Go, breaking import cycles, and small stacked PRs. Apply when a function or type has grown too large, a code…
agent-review
Critically review a Stencila agent and suggest improvements. Use when asked to review, audit, critique, evaluate, or improve an agent directory or AGENT.md file. Covers frontmatter validation, system instruction quality, configuration correctness, and adherence to the Agent schema.