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 adonai-labs/agent-runway --skill autonomous-leadgit clone --depth 1 https://github.com/adonai-labs/agent-runwayWrote 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/adonai-labs/agent-runway/autonomous-lead)<a href="https://agentmods.dev/skills/adonai-labs/agent-runway/autonomous-lead"><img src="https://agentmods.dev/badge/skills/adonai-labs/agent-runway/autonomous-lead/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/adonai-labs/agent-runway/autonomous-lead"><img src="https://agentmods.dev/badge/skills/adonai-labs/agent-runway/autonomous-lead.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 | $0.00057 | $0.01914 |
| Opus 5 | $0.00028 | $0.00957 |
| Sonnet 5 | $0.00011 | $0.00383 |
| Haiku 4.5 | $0.00006 | $0.00191 |
Grade A, and why
autonomous-lead 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 — 266 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autonomous Lead
Apply shared policy: ../shared/caveman-skill-engineering.md
Invoke Command
/autonomous-lead <description of what to build>
Examples:
/autonomous-lead implement idempotency keys for payment webhooks/autonomous-lead add tenant filter enforcement across API and repositories
Purpose
Run implementation with minimal supervision while preserving full traceability.
Quality bar is identical to lead. The only difference is approval mode:
lead: interactive gatesautonomous-lead: non-interactive gates with mandatory evidence logs
Autonomy is allowed for execution decisions, but every material decision must be captured in repository artifacts.
Quality Parity Contract (with lead)
autonomous-lead must enforce the same technical rigor as the lead skill:
- same task classification discipline (trivial/standard/complex)
- same DRY analysis expectations before creating new components
- same incremental checks during implementation
- same self-review depth (SOLID, DRY, security, testing, scope control)
- same final validation expectations before handoff
If any lead quality gate would fail, autonomous execution must fail too.
Decision Governance (Mandatory)
Before implementation, classify risk with:
- Impact: low/medium/high
- Reversibility: reversible/hard-to-reverse/irreversible
- Uncertainty: low/medium/high
- Cost of error: low/medium/high
Contrarian trigger (any true):
- impact is high and reversibility is hard-to-reverse or irreversible
- uncertainty is high
- contract/public interface change with ambiguous outcomes
- architecture decision with multiple viable paths
Execution mode:
- Execution-only: low risk + reversible + low uncertainty
- Execution+validation: medium risk or multi-layer change
- Execution+contrarian: contrarian trigger active
Required Artifacts
Before code changes, create:
.agent-runway/logs/autonomous-runs/<run-id>.md- Recommended starting point:
.agent-runway/docs/examples/autonomous-run-log-template.md
What ships with it
1 file 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.
- 12d ago First seen · 266 lines · 57 tokens per session scan A ce80bf2a392d
autonomous-lead is a skill published in the GitHub repository adonai-labs/agent-runway (2 stars, last pushed 22d ago), licensed MIT. It adds 57 tokens to every session and 1,914 once invoked, about $0.0003 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 skills, from other repositories
next-action
Use when the user wants a next-action recommendation based on current state — reads handoff/git/lessons/STATE and proposes top-3 by impact. Trigger: '/next', 'what should I do next', 'next action'. Proposes only, never executes.
pull-request-automation
Audits and improves the pull request workflow for a GitHub repository. Covers PR description templates, auto-labelling, CODEOWNERS, PR size checks, and branch protection rules. Invoked when the user asks to improve the PR process, set up PR automation, or add a PR template.
PR Description Writer
Writes clear, thorough pull request descriptions from a diff or list of changes.
agent-code-simplifier
Simplifies and refines code for clarity, consistency, and maintainability while preserving behavior. Focus on recently modified code unless instructed otherwise.
andrej-karpathy
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
peon-ping-log
Log exercise reps for the Peon Trainer. Use when user says they did pushups, squats, or wants to log reps. Examples - "/peon-ping-log 25 pushups", "/peon-ping-log 30 squats", "log 50 pushups".