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 curiositech/some_claude_skills --skill human-gate-designergit clone --depth 1 https://github.com/curiositech/some_claude_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/curiositech/some_claude_skills/human-gate-designer)<a href="https://agentmods.dev/skills/curiositech/some_claude_skills/human-gate-designer"><img src="https://agentmods.dev/badge/skills/curiositech/some_claude_skills/human-gate-designer/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/curiositech/some_claude_skills/human-gate-designer"><img src="https://agentmods.dev/badge/skills/curiositech/some_claude_skills/human-gate-designer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00117 | $0.01427 |
| Opus 5 | $0.00059 | $0.00714 |
| Sonnet 5 | $0.00023 | $0.00285 |
| Haiku 4.5 | $0.00012 | $0.00143 |
Grade A, and why
human-gate-designer 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 7d 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 — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Human Gate Designer
Designs human-in-the-loop review points in DAG workflows: what to present, how to collect feedback, how to route decisions back into the DAG.
When to Use
✅ Use for:
- Deciding WHERE in a DAG to place human gates
- Designing WHAT the human sees at each gate
- Defining HOW feedback routes back (approve/reject/modify)
- Balancing automation speed with human oversight
❌ NOT for:
- Runtime execution of human gates (use
dag-runtime+ Temporal signals) - General UI/UX design (use design skills)
- Chatbot conversation flow (different pattern)
Gate Placement Decision Tree
flowchart TD
A{Is the action irreversible?} -->|Yes| G1[Gate BEFORE the action]
A -->|No| B{Is output user-facing?}
B -->|Yes| G2[Gate AFTER generation, BEFORE delivery]
B -->|No| C{Cost > $0.50 for remaining nodes?}
C -->|Yes| G3[Gate at the cost threshold]
C -->|No| D{Confidence score < 0.7?}
D -->|Yes| G4[Gate on low-confidence outputs]
D -->|No| N[No gate needed]
Where to Place Gates
| Situation | Gate Position | Why |
|---|---|---|
| Irreversible action (deploy, send email, submit) | Before the action | Can't undo |
| User-facing deliverable (report, website, PR) | After generation, before delivery | Quality check |
| High cost remaining (>$0.50) | Before expensive phase | Budget confirmation |
| Low confidence output (<0.7) | After the uncertain node | Expert judgment needed |
| Ambiguous task decomposition | After planning, before execution | Validate the plan |
| First run of a new template DAG | After each phase | Build trust gradually |
Gate Presentation Design
What the Human Sees
┌──────────────────────────────────────────────────────┐
│ 🔍 Human Review: [Node Name] │
│ │
│ Context: [1-2 sentences: what happened so far] │
│ │
│ Output to Review: │
│ ┌──────────────────────────────────────────────────┐│
│ │ [The node's output, formatted for readability] ││
│ │ [Key decisions highlighted] ││
│ │ [Confidence: 0.82] ││
│ └──────────────────────────────────────────────────┘│
│ │
│ Cost so far: $0.08 / $0.50 budget │
│ Remaining nodes: 4 (est. $0.12) │
│ │
│ [✅ Approve] [✏️ Modify] [❌ Reject] │
│ │
│ If modifying, what should change? │
│ ┌──────────────────────────────────────────────────┐│
│ │ [text input for human feedback] ││
│ └──────────────────────────────────────────────────┘│
└──────────────────────────────────────────────────────┘
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.
- 7d ago First seen · 156 lines · 117 tokens per session scan A f0989a160c53
human-gate-designer is a skill published in the GitHub repository curiositech/some_claude_skills (218 stars, last pushed 4d ago), licensed MIT. It adds 117 tokens to every session and 1,427 once invoked, about $0.0006 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 2026-09-03.
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