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 hajekim/agentic-design-patterns-skills --skill human-in-the-loopgit clone --depth 1 https://github.com/hajekim/agentic-design-patterns-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/hajekim/agentic-design-patterns-skills/human-in-the-loop)<a href="https://agentmods.dev/skills/hajekim/agentic-design-patterns-skills/human-in-the-loop"><img src="https://agentmods.dev/badge/skills/hajekim/agentic-design-patterns-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/hajekim/agentic-design-patterns-skills/human-in-the-loop"><img src="https://agentmods.dev/badge/skills/hajekim/agentic-design-patterns-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 | $0.00412 | $0.03622 |
| Opus 5 | $0.00206 | $0.01811 |
| Sonnet 5 | $0.00082 | $0.00724 |
| Haiku 4.5 | $0.00041 | $0.00362 |
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 10d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- human-in-the-loop — 100% identical, 3 lines differ
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.
Human-in-the-Loop (HITL) Pattern
Overview
The Human-in-the-Loop (HITL) Pattern integrates human judgment, oversight, and approval into agent workflows at critical decision points. Rather than running fully autonomously, HITL agents pause at defined checkpoints — to confirm high-stakes actions, request clarification, seek approval, or collect feedback — before proceeding.
Core Principle: Autonomy is earned, not assumed — build human oversight into the design, not as an afterthought.
When This Skill Applies
Activate this pattern when:
- Agent actions are irreversible (sending emails, deleting data, financial transactions)
- High-risk decisions require human judgment (medical, legal, financial advice)
- Ambiguous instructions need human clarification before proceeding
- Regulatory or compliance requirements mandate human approval
- Agent confidence is below acceptable threshold for autonomous action
- Actions affect third parties who have not consented to fully automated interaction
- The agent encounters situations outside its training distribution
Rule of thumb: If you'd want a human to review the action before it happens, build in a HITL checkpoint.
HITL Spectrum
Fully Autonomous ←————————————————————→ Fully Manual
| | | |
Auto-run Approve Review Step-by-step
(no review) high-risk all steps approval
Choose the appropriate level based on:
- Risk: Higher risk → more human involvement
- Trust: Proven, well-tested agents can operate more autonomously
- Context: Production vs. development, internal vs. external-facing
- Reversibility: Irreversible actions need pre-approval
DEFINE → PLAN → ACTION Workflow
DEFINE
Map the human oversight requirements:
- Which actions are irreversible or high-risk?
- Which decision points require human judgment (not just execution)?
- What information does the human need to make an informed approval?
- What are the response time requirements? (Synchronous vs. async approval)
- What happens if the human is unavailable?
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.
- 10d ago First seen · 392 lines · 412 tokens per session scan A f70bb91858dc
human-in-the-loop is a skill published in the GitHub repository hajekim/agentic-design-patterns-skills (4 stars, last pushed 5mo ago), licensed MIT. It adds 412 tokens to every session and 3,622 once invoked, about $0.0021 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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