AugmentClaude

Human In The Loop

Route decisions to humans with durable queues and attention tracking.

Installation

  1. Make sure Claude is on your device and in your terminal.

    Skills load from ~/.claude/skills/ when Claude Code starts up β€” so you need it on your machine first. If you don't have it yet, install it once with the command below, then run claude in any terminal to verify.

    One-time setup
    npm i -g @anthropic-ai/claude-code

    Already have it? Skip ahead.

  2. Paste into Claude Code or into your terminal.

    This copies the whole skill folder into ~/.claude/skills/human-in-the-loop-mvschwarz/ β€” the SKILL.md plus any scripts, reference docs, or templates the skill ships with. Safe default: works for every skill.

    Faster alternative (instruction-only skills)

    Skips the clone and grabs only the SKILL.md file. Don't use this if the skill ships Python scripts, reference markdowns, or asset templates β€” they won't be downloaded and the skill will fail when it tries to load them.

    Quick install (SKILL.md only)
    Sign up to copy
  3. Restart Claude Code.

    Quit and reopen Claude Code (or any other agent that loads from ~/.claude/skills/). New skills are picked up on startup.

  4. Just ask Claude.

    Skills auto-activate when your request matches the skill's description β€” no slash command needed. Trigger phrases live in the skill's own frontmatter; you can read them in the β€œWhat this skill does” section above.

Prefer to read the source first? Open on GitHub.

When Claude uses it

Use when classifying a slice closeout (auto-continue / human gate / park), routing a real decision to a human, or designing a human queue/dashboard surface. Treats humans as durable network participants with attention surfaces, queues, and decision records β€” escalation lands as a durable attention item, not a chat message. Approval is NOT required for every clean closeout; the default RSI conveyor continues unless an explicit human gate is reached.

What this skill does

Human In The Loop

The primitive that treats humans as durable network participants β€” attention surfaces, queues, decision records, routing semantics β€” not as ad-hoc chat receivers.

Autonomy is not the absence of humans; it is knowing when human judgment is needed and making that handoff crisp.

Use this when

  • A slice closeout needs classifying: auto-continue, human gate, or park
  • A real decision needs to land in front of a human (usage limits, provider auth, roadmap tradeoff, product-intent ambiguity)
  • Designing a human queue/dashboard surface
  • Returning a hot potato to orchestration after human approval

Don't use this when

  • The slice closeout is clean and PROGRESS.md already names the next safe slice. Default RSI conveyor continues; do NOT manufacture a human gate.
  • The escalation is just a status update. Humans are participants for decisions, not narration.
  • The next owner is another agent. Use queue-handoff, not human-in-the-loop.

The 3-class closeout classification

In a productized daemon-backed version, closeout classifies the next step BEFORE touching the human queue:

ClassWhenAction
auto-continueSlice closes cleanly, next named slice in workstream planMark closed; create next-owner qitem from plan
human gateGenuine decision needed (usage limits, provider auth, product-intent ambiguity, roadmap tradeoff)Create human queue item with proof + decision text + recommended default + action outcomes
parkIntentionally stop the conveyor (e.g., waiting on external)Stop with reason + resumption path

Failure modes (5)

  1. Human decision needed, but the rig only mentions it in chat. Decisions belong as durable attention items, not chat messages.
  2. Human queue item lacks enough plain-English context for a decision. Include proof + decision text + recommended default + action outcomes.
  3. Human response updates a file but does not wake the next owner. Approval should return the hot potato; feedback should create the next durable qitem.
  4. The dashboard shows too much raw rig state and hides the actual decision queue. Decision queue is the primary surface; rig state is secondary.
  5. A clean closeout is parked on the human even though PROGRESS.md already names the next safe slice. Don't manufacture human gates.

Proof standard (both paths)

A trustworthy human-in-the-loop system proves both directions:

  • Blocking gate path: real item routed to human β†’ human decision recorded through UI β†’ resulting hot-potato handoff wakes correct next owner
  • Non-blocking closeout path: proof inspectable by human, but orchestrator continues to next named slice without manufacturing a human gate

A primitive that only wakes humans is not trustworthy. It must also know when NOT to.

Product shape (SHIPPED β€” Mission Control, PL-005)

This surface has shipped as Mission Control (product UI, /mission-control route; actions via POST /api/mission-control/action). The seven verbs the human acts with:

  • approve (returns the hot potato to orchestration or the chosen owner)
  • deny (reject the item)
  • route (send to a different owner)
  • annotate (add context without action)
  • hold (intentional pause with reason)
  • drop (mark not-actionable)
  • handoff (hand to a specific next owner β€” creates the next durable qitem)

Approval returns the hot potato to orchestration or the chosen owner; feedback creates the next durable qitem rather than only mutating the source queue file β€” enforced by the shipped verbs (handoff/route create qitems). See docs/as-built/architecture/mission-control.md.

Long-term shape

Likely needs multiple humans with different scopes, not a singleton human attention feed. Different humans own different decision domains; queue items route by scope.

See also

  • queue-handoff skill β€” durable handoff via queue items; human-in-the-loop is the human-side complement
  • watchdog skill β€” when to wake (humans included) vs no-op
  • looping-workflows (convention) β€” the looping-workflows convention covers loop closeouts; human-in-the-loop is the escape hatch

Related skills