human-in-the-loop
Skill3 starsChanged 38 days agoMITView on GitHub
Place proportional human review and approval gates in an AI system's execution path. Use when designing approval/escalation for agent actions with financial, external, destructive or permission impact, preview-before-commit flows, or uncertainty that warrants human judgment. Do not use for requesting routine coding confirmation, imposing approval on harmless reads, or deterministic authorization logic alone.
8 other projects use a version of this.
Adopted by
Most recently taken up by
- lauren-framework/lauren-aitheir own version, first seen Nov 1, 2026
- asmamest/Nexus-Skillstheir own version, first seen Nov 1, 2026
- BojanKocijan/design-forgetheir own version, first seen Oct 19, 2026
- bingyue/fde-skillstheir own version, first seen Oct 13, 2026
- BojanKocijan/bk-charterlinetheir own version, first seen Oct 13, 2026
- VoDaiLocz/kilo-kit-mcptheir own version, first seen Aug 21, 2026
Other versions8 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 8
- VoDaiLocz/kilo-kit-mcp★ 28same name
- bingyue/fde-skills★ 5same name
- BojanKocijan/bk-charterline★ 4same name
- BojanKocijan/design-forge★ 4same name
- hajekim/agentic-design-patterns-skills★ 4same name
- asmamest/Nexus-Skills★ 1same name
- hajekim/agentic-design-patterns-extension★ 1same name
- lauren-framework/lauren-ai★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository18 other files it gives its agents.
- agent-architectureSkill
- agent-cost-and-latencySkill
- agent-development-workflowSkill
- agent-evalsSkill
- agent-failure-recoverySkill
- agent-guardrailsSkill
- agent-observabilitySkill
- agent-orchestrationSkill
- agent-securitySkill
- agent-state-and-memorySkill
- agent-testingSkill
- deterministic-authoritySkill
- evidence-provenanceSkill
- prompt-engineeringSkill
- rag-engineeringSkill
- structured-output-designSkill
- tool-designSkill
- tool-evalsSkill
The file
We split this skill into the rules it gives an agent
2 rules. 0 are common, 2 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 4 edited versions we read keep these.
2
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 12
Use humans where a consequential decision needs their authority or judgment.
- Distinctive: line 24
Keep pending, approved, denied, expired and cancelled states explicit.
“Common” is measured: kept by at least 6 in 10 of the 4 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Human in the Loop
Use humans where a consequential decision needs their authority or judgment. Repeated approvals for harmless actions train users to click through; removing every approval also hides consequential choices.
Decide whether to gate
- For each action, record impact, reversibility, affected people/resources, uncertainty, existing user authorization and applicable policy. Do not ask again if a valid scoped authorization already covers the action. A preference remembered by the model is not authorization.
- Let deterministic policy decide which actions can proceed, must be denied, or require approval. Favor automatic execution for authorized low-impact reversible work. Gate meaningful financial commitments, external communications, destructive operations and permission changes when the user/policy has not already authorized their specific scope. A sandbox draft or preview can normally be prepared first.
- Escalate insufficient evidence, policy exceptions, novel high-impact actions or uncertainty beyond a calibrated threshold. Use objective missing evidence or measured uncertainty, not a model's unsupported confidence score. Hard invariant violations are denied; they are not solved by an approve button.
Prepare a concrete decision
- Complete authorized reversible preparation before asking. Present exact proposed action, targets/recipients, amount or scope, useful diff/preview, material risks, evidence and available alternatives. Keep the request understandable and proportional; do not dump a trajectory for the approver to reconstruct.
- Identify an approver with the necessary authority. Bind approval to principal, canonical arguments/action digest, resources, relevant state/policy version, expiry and use count. Store application-issued approval and decision evidence outside model prose. Reject self-approval by the acting model.
- Keep pending, approved, denied, expired and cancelled states explicit. A timeout or silence is not approval. While pending, continue independent authorized work without executing the gated action. Offer cancellation and safe draft retention.
- Recheck policy and binding before commit. Changed recipients, amounts, material diff, state or expired approval require a fresh decision. Prevent time-of-check/time-of-use substitution. Apply idempotency so retries do not duplicate approved effects.
Control friction and verify
- Batch related decisions only when users can understand and authorize the bounded group; allow item-level rejection. Define explicit standing grants with limits/expiry if appropriate, rather than assuming previous approvals authorize future actions. Measure approval frequency, abandonment, review time, erroneous approvals and escalation outcomes.
Deliver an action-to-gate table, one exact preview, approval state/binding contract, timeout/changed-action behavior and tests. Verify denied/expired grants prevent effects, changed payload invalidates approval, and harmless authorized reads proceed without repeated prompts.
deterministic-authority owns enforcement; this skill owns when and how a human decision enters that path. Ordinary development review and confirmation are out of scope; do not add a ritual approval step to every coding task.
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