human-in-the-loop-designer

human-in-the-loop-designer is a skill for Claude Code, Codex from khalilbenaz/claude-skills-collection. It costs 88 tokens per session (2,385 once invoked), scanned A, original, MIT.

A design guide for workflows in which an AI agent pauses for a person to approve, correct, or take over an action.

In plain words
What is it for?
Adding approval gates, escalation paths, audit records, and supervised operation to workflows such as payments, data deletion, email sending, deployments, and regulated decisions.
Why use it?
It helps place human checks around irreversible, costly, legally required, unfamiliar, or low-confidence actions.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Adding approval gates, escalation paths, audit records, and supervised operation to workflows such as payments, data deletion, email sending, deployments, and regulated decisions.

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Install with agentmods
npx agentmods add skills/khalilbenaz/claude-skills-collection/human-in-the-loop-designer
Install

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.

Any agent
npx skills add khalilbenaz/claude-skills-collection --skill human-in-the-loop-designer
Clone the repo
git clone --depth 1 https://github.com/khalilbenaz/claude-skills-collection

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for human-in-the-loop-designer

README.md
[![agentmods](https://agentmods.dev/badge/skills/khalilbenaz/claude-skills-collection/human-in-the-loop-designer/github.svg)](https://agentmods.dev/skills/khalilbenaz/claude-skills-collection/human-in-the-loop-designer)
Your own site
<a href="https://agentmods.dev/skills/khalilbenaz/claude-skills-collection/human-in-the-loop-designer"><img src="https://agentmods.dev/badge/skills/khalilbenaz/claude-skills-collection/human-in-the-loop-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.

agentmods 80×15 button for human-in-the-loop-designer

Your own site · 80×15
<a href="https://agentmods.dev/skills/khalilbenaz/claude-skills-collection/human-in-the-loop-designer"><img src="https://agentmods.dev/badge/skills/khalilbenaz/claude-skills-collection/human-in-the-loop-designer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 88 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,385 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, 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 Data Exfiltration · line 142
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00088 $0.02385
Opus 5 $0.00044 $0.01192
Sonnet 5 $0.00018 $0.00477
Haiku 4.5 $0.00009 $0.00238

Measured 12d ago against content hash 97d70f755c00, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

human-in-the-loop-designer 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 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

response = requests.post("/api/hitl/requests", json={
agent-skills/human-in-the-loop-designer/SKILL.md · 210 lines

How it starts

The opening of the file, as written. The whole thing — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Human-in-the-Loop Designer

Quand utiliser ce skill

Intègre un point de contrôle humain dès qu'une action de l'agent est :

  • Irréversible : suppression de données, envoi d'email, paiement, déploiement en production
  • Coûteuse : action dont le coût de correction dépasse le coût de la validation
  • Réglementairement obligatoire : conformité financière, médicale, légale
  • Hors-distribution : tâche inédite ou contexte jamais rencontré par l'agent
  • À faible confiance : score de confiance de l'agent sous le seuil calibré

Étape 1 — Cartographier les points de décision

Parcours le workflow de l'agent, identifie chaque nœud d'action, et classe-le :

Catégorie Exemples Mode HITL recommandé
Irréversible + haut risque Suppression DB, virement, envoi en masse approval gate systématique
Réversible + impact modéré Brouillon d'email, mise à jour de ticket exception escalation (si doute)
Basse criticité, haute fréquence Catégorisation, tagging, résumé shadow mode puis autonomie progressive
Obligation légale Signature, validation KYC approval gate systématique + audit trail

Étape 2 — Choisir le pattern HITL

approval gate — bloquer jusqu'à approbation explicite

# LangGraph interrupt pattern (SDK 0.2+)
from langgraph.types import interrupt, Command

def human_approval_node(state: AgentState):
    payload = {
        "action": state["proposed_action"],
        "context": state["context"],
        "risk_level": state["risk_level"],
        "estimated_impact": state["impact_summary"],
    }
    decision = interrupt(payload)  # suspend le graph, reprend après résumption
    if decision["approved"]:
        return {"approved_action": state["proposed_action"]}
    return {"approved_action": decision.get("correction", "__abort__")}

confidence threshold — escalade automatique selon le score

HIGH_RISK_THRESHOLD = 0.7   # ex : coût normalisé 0..1
CONFIDENCE_THRESHOLD = 0.82  # calibrer empiriquement

def should_escalate(task: Task, confidence: float) -> bool:
    risk = task.estimated_cost_normalized * (1 - task.reversibility)
    return risk > HIGH_RISK_THRESHOLD or confidence < CONFIDENCE_THRESHOLD

Read the full file on GitHub · 210 lines

Changes

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.

  1. 12d ago First seen · 210 lines · 88 tokens per session scan A 97d70f755c00

Subscribe to this mod's changes

human-in-the-loop-designer is a skill published in the GitHub repository khalilbenaz/claude-skills-collection (22 stars, last pushed 19d ago), licensed MIT. It adds 88 tokens to every session and 2,385 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.