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AB human-in-the-loop-design

Design the human approval surface for an agent system — which actions gate, how approvals batch without becoming rubber stamps, and what the audit trail must hold. Use when asked to add human oversight to an agent, design approval workflows for AI actions, decide what an agent may do autonomously, or fix approval fatigue in an existing loop. Produces an action-tier policy, approval UX spec, escalation rules, and audit-trail requirements. For specifying the whole agent use agent-spec; for the per-skill execution gates see the Execution-block pattern in SKILLSPEC §5.

mohitagw15856/pm-claude-skills Agent Skills author: mohitagw15856 MIT 1 file body ≈ 1 334 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Design the human approval surface for an agent system — which actions gate, how approvals batch without becoming rubber stamps, and what the audit trail must…

As a process B 74/100 · Nearly there — weak spots: when it triggers, running it twice

ProcedureAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
B
74/100
Nearly there
When it triggers w 12
20
Running it twice w 4
30
Failures and branches w 10
50
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: human-in-the-loop-design (mohitagw15856/pm-claude-skills)

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 74/100

    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 6 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 32 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1334 tokens
    • 100Progress reporting. Reports progress

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 571: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 32 items
    • +3Output format is stated explicitly

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.