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 Owl-Listener/ai-design-skills --skill human-in-the-loopgit clone --depth 1 https://github.com/Owl-Listener/ai-design-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/owl-listener/ai-design-skills/human-in-the-loop)<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/human-in-the-loop"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/human-in-the-loop/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/owl-listener/ai-design-skills/human-in-the-loop"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/human-in-the-loop.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 | $0.00020 | $0.00570 |
| Opus 5 | $0.00010 | $0.00285 |
| Sonnet 5 | $0.00004 | $0.00114 |
| Haiku 4.5 | $0.00002 | $0.00057 |
Grade A, and why
human-in-the-loop 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 — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Human-in-the-Loop
Human-in-the-loop design defines when, where, and how humans intervene in automated workflows. Too little human involvement and the system makes dangerous mistakes. Too much and you've just built an expensive notification system.
Intervention Types
- Approval gates: The system pauses and waits for human approval before proceeding
- Review checkpoints: The system presents results for human review but can continue if no objection
- Correction opportunities: The system shows its work and the human can edit before it's finalised
- Override controls: The human can stop, redirect, or undo the system's actions at any time
- Monitoring dashboards: The human passively observes the system and intervenes only when needed
When to Require Human Intervention
- High stakes: Actions that are expensive, irreversible, or affect many people
- Low confidence: The system is uncertain about the right action
- Novel situations: The input or context is outside the system's training distribution
- Ethical judgments: Decisions that require moral reasoning or value trade-offs
- Legal requirements: Regulatory or compliance requirements mandate human review
- User request: The user explicitly asks for human involvement
Designing Intervention Points
For each intervention point:
- Trigger: What causes the intervention? (confidence threshold, stakes level, user request, policy requirement)
- Presentation: What does the human see? (summary, full context, recommendations, options)
- Time constraint: How quickly must the human respond? What happens if they don't?
- Decision options: What can the human do? (approve, reject, edit, escalate, defer)
- Feedback integration: How does the human's decision feed back into the system?
Avoiding Human Bottlenecks
Human intervention is expensive and slow. Design to minimise unnecessary intervention:
- Graduated autonomy: Start with more human oversight, reduce as the system proves reliable
- Batch review: Group similar decisions for efficient human processing
- Smart routing: Send interventions to the right human based on expertise and availability
- Default actions: If the human doesn't respond within a time window, take a safe default action
- Learning from interventions: Use human decisions to improve the system so it needs less intervention over time
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 · 40 lines · 20 tokens per session scan A 7595c6f82105
human-in-the-loop is a skill published in the GitHub repository Owl-Listener/ai-design-skills (173 stars, last pushed 3mo ago), licensed MIT. It adds 20 tokens to every session and 570 once invoked, about $0.0001 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 2026-08-30.
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