Show Me Your Work Decision Log
cursor/plugins
Keeps a TSV decision log for long or unattended agent runs, one row per decision with what, why, evidence and result, so a reviewer can check the work later.
A skill your agent uses when classifying a slice closeout (auto-continue / human gate / park), routing a real decision to a human, or designing a human queue/dashboard surface.
Install Human In The Loop
$ npx skills add mvschwarz/openrig --skill human-in-the-loop -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mvschwarz/openrig human-in-the-loop --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/mvschwarz/openrig.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/_canonical/core/human-in-the-loop .claude/skills/human-in-the-loop && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "human-in-the-loop" agent skill from https://github.com/mvschwarz/openrig/tree/main/skills/_canonical/core/human-in-the-loop into .claude/skills/human-in-the-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "human-in-the-loop", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/mvschwarz/openrig/tree/main/skills/_canonical/core/human-in-the-loopType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add mvschwarz/openrig --skill human-in-the-loop -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mvschwarz/openrig human-in-the-loop --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mvschwarz/openrig.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/_canonical/core/human-in-the-loop .agents/skills/human-in-the-loop && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "human-in-the-loop" agent skill from https://github.com/mvschwarz/openrig/tree/main/skills/_canonical/core/human-in-the-loop into .agents/skills/human-in-the-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "human-in-the-loop", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mvschwarz/openrig --skill human-in-the-loop -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mvschwarz/openrig human-in-the-loop --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mvschwarz/openrig.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/_canonical/core/human-in-the-loop .cursor/skills/human-in-the-loop && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "human-in-the-loop" agent skill from https://github.com/mvschwarz/openrig/tree/main/skills/_canonical/core/human-in-the-loop into .cursor/skills/human-in-the-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "human-in-the-loop", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/mvschwarz/openrig.git --path skills/_canonical/core/human-in-the-loop--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add mvschwarz/openrig --skill human-in-the-loop -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mvschwarz/openrig human-in-the-loop --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mvschwarz/openrig.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/_canonical/core/human-in-the-loop .gemini/skills/human-in-the-loop && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "human-in-the-loop" agent skill from https://github.com/mvschwarz/openrig/tree/main/skills/_canonical/core/human-in-the-loop into .gemini/skills/human-in-the-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "human-in-the-loop", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install mvschwarz/openrig human-in-the-loopInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add mvschwarz/openrig --skill human-in-the-loop -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/mvschwarz/openrig.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/_canonical/core/human-in-the-loop .github/skills/human-in-the-loop && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "human-in-the-loop" agent skill from https://github.com/mvschwarz/openrig/tree/main/skills/_canonical/core/human-in-the-loop into .github/skills/human-in-the-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "human-in-the-loop", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mvschwarz/openrig --skill human-in-the-loop -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install mvschwarz/openrig human-in-the-loop --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mvschwarz/openrig.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/_canonical/core/human-in-the-loop .opencode/skills/human-in-the-loop && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "human-in-the-loop" agent skill from https://github.com/mvschwarz/openrig/tree/main/skills/_canonical/core/human-in-the-loop into .opencode/skills/human-in-the-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "human-in-the-loop", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
human-in-the-loop- GitHub stars
- 5.9k
- Token cost
- ~1.3k tokens
- SKILL.md length
- 573 words
- Files
- 1
- Skills in repo
- 49
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
A skill your agent uses when classifying a slice closeout (auto-continue / human gate / park), routing a real decision to a human, or designing a human queue/dashboard surface.
- Works in 5 steps: Human decision needed, but the rig only… → Human queue item lacks enough… → Human response updates a file but does… → …
- Classifying a slice closeout (auto-continue / human gate / park)
- SKILL.md covers Use this when, Don't use this when, The 3-class closeout… and Failure modes (5), plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Human In The Loop is an agent skill from mvschwarz/openrig. 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 — 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.
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Agent Workflows, covering Human-in-the-loop approvals. The repository describes itself as: Build your own network of agents from Claude Code, Codex and Pi: persistent teams with roles, shared context and owned work. The licence is Apache-2.0.
When your agent uses it
- Classifying a slice closeout (auto-continue / human gate / park)
- Routing a real decision to a human
- Designing a human queue/dashboard surface
Example prompts
- “/human-in-the-loop”
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Human decision needed, but the rig only mentions it in chat. Decisions belong as durable attention items, not chat messages.
- Human queue item lacks enough plain-English context for a decision. Include proof + decision text + recommended default + action outcomes.
- Human response updates a file but does not wake the next owner. Approval should return the hot potato; feedback should create the next…
- The dashboard shows too much raw rig state and hides the actual decision queue. Decision queue is the primary surface; rig state is…
- A clean closeout is parked on the human even though PROGRESS.md already names the next safe slice. Don't manufacture human gates.
What it can do on your machine
Read from SKILL.md and the folder at commit 1f69831. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Human In The Loop loads about 1.3k tokens when it runs. Until then it costs about 118 tokens; SKILL.md has 573 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from mvschwarz/openrig at commit 1f69831, republished under its Apache-2.0 licence (© mvschwarz). 573 words, ~1,309 tokens.
Download SKILL.mdSave it as
.claude/skills/human-in-the-loop/SKILL.md (or your agent's skills folder).- name
- human-in-the-loop
- description
- 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 — 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.
Human In The Loop
The primitive that treats humans as durable network participants — attention surfaces, queues, decision records, routing semantics — 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.mdalready 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:
| Class | When | Action |
|---|---|---|
| auto-continue | Slice closes cleanly, next named slice in workstream plan | Mark closed; create next-owner qitem from plan |
| human gate | Genuine decision needed (usage limits, provider auth, product-intent ambiguity, roadmap tradeoff) | Create human queue item with proof + decision text + recommended default + action outcomes |
| park | Intentionally stop the conveyor (e.g., waiting on external) | Stop with reason + resumption path |
Failure modes (5)
- Human decision needed, but the rig only mentions it in chat. Decisions belong as durable attention items, not chat messages.
- Human queue item lacks enough plain-English context for a decision. Include proof + decision text + recommended default + action outcomes.
- 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.
- The dashboard shows too much raw rig state and hides the actual decision queue. Decision queue is the primary surface; rig state is secondary.
- A clean closeout is parked on the human even though
PROGRESS.mdalready names the next safe slice. Don't manufacture human gates.
Show full SKILL.md (237 more words)Show less
Proof standard (both paths)
A trustworthy human-in-the-loop system proves both directions:
- Blocking gate path: real item routed to human → human decision recorded through UI → 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 — 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 — 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 — 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-handoffskill — durable handoff via queue items; human-in-the-loop is the human-side complementwatchdogskill — when to wake (humans included) vs no-oplooping-workflows(convention) — the looping-workflows convention covers loop closeouts; human-in-the-loop is the escape hatch
© mvschwarz, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/_canonical/core/human-in-the-loop of mvschwarz/openrig.
Open the folder on GitHubat commit 1f69831
Compare with similar skills
Human In The Loop next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Human In The Loop this skillmvschwarz/openrig | 5.9k | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Show Me Your Work Decision Logcursor/plugins | 10k | 9 repos | ~1.6k | Automated safety check: Pass | None | |
| Darwin Skill Optimizeralchaincyf/darwin-skill | 6.2k | 1 repos | ~4.7k | Automated safety check: Pass | MIT | |
| Loop Constraints Enforcercobusgreyling/loop-engineering | 11k | 1 repos | ~475 | Automated safety check: Notes | MIT | |
| Ask User QuestionMemTensor/MemOS | 12k | — | ~1k | Automated safety check: Pass | Apache-2.0 | |
| Agentmemory Forgetrohitg00/agentmemory | 29k | — | ~612 | Automated safety check: Pass | Apache-2.0 |
Similar skills
- Official
Darwin Skill Optimizer
alchaincyf/darwin-skill
Scores SKILL.md files on a nine-dimension rubric, then improves them in a keep-or-revert loop with independent judge agents, test prompts, git history and human checkpoints.
Loop Constraints Enforcer
cobusgreyling/loop-engineering
Loads a project's loop-constraints.md before any other action and blocks pushes, edits or merges that violate the rules it defines.
Ask User Question
MemTensor/MemOS
Shows a question as a modal in the interface to clarify a task, collect a preference or get approval, since the user cannot see terminal output.
Agentmemory Forget
rohitg00/agentmemory
Deletes chosen memories from agentmemory only after showing the matches and getting an explicit yes, for privacy requests and cleanup of outdated notes.
PUA High-Agency Governance
tanweai/pua
Pushes an agent to keep verifying and changing approach after repeated failures, using a diagnosis line, evidence-based completion and confirmation before risky edits.
More from mvschwarz/openrig
All 49 skills in this repoOpenRig Upgrade Procedure
mvschwarz/openrig
Walks an agent through upgrading the OpenRig CLI and daemon one observed step at a time, keeping live seats alive and reconciling managed plugin files.
Agent Refocusing
mvschwarz/openrig
Re-grounds a long-running agent in the current product outcome by running a path-based trace to the root of its topology and work trees.
OpenRig Software Factory
mvschwarz/openrig
Helps set up a continuing agent software team for a real repository with OpenRig, choosing between manual work, queue handoffs and an explicit Workflow.
Addressable Markdown Loader
mvschwarz/openrig
Loads one section of a Markdown file by its path#h2-slug address with a bundled resolver script, for use outside OpenRig's context library.
Seat Continuity and Handover
mvschwarz/openrig
Separates a stable agent seat's identity from its changing occupant, and records honest, two-part provenance whenever one occupant replaces another.
Cross Host Rig Commands
mvschwarz/openrig
A skill your agent uses when addressing a registered remote OpenRig host, choosing its transport, or interpreting a cross-host result.
Related
Categories
Questions about Human In The Loop
What does Human In The Loop do?
A skill your agent uses when classifying a slice closeout (auto-continue / human gate / park), routing a real decision to a human, or designing a human queue/dashboard surface. Human In The Loop is an agent skill from mvschwarz/openrig. 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.
When should I use Human In The Loop?
Human In The Loop fits situations like: classifying a slice closeout (auto-continue / human gate / park); routing a real decision to a human; designing a human queue/dashboard surface.
How do I install Human In The Loop in Claude Code?
Run `npx skills add mvschwarz/openrig --skill human-in-the-loop -a claude-code`. Or copy the skill folder (skills/_canonical/core/human-in-the-loop in mvschwarz/openrig) into .claude/skills/human-in-the-loop in your project. Claude Code loads it when a task matches its description.
How do I install Human In The Loop in Codex?
Run `npx skills add mvschwarz/openrig --skill human-in-the-loop -a codex`. Or copy the skill folder (skills/_canonical/core/human-in-the-loop in mvschwarz/openrig) into .agents/skills/human-in-the-loop in your project. Codex loads it when a task matches its description.
Can I use Human In The Loop in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mvschwarz/openrig --skill human-in-the-loop -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/human-in-the-loop, .gemini/skills/human-in-the-loop, .github/skills/human-in-the-loop and .opencode/skills/human-in-the-loop in your project.
What does Human In The Loop need to run?
SKILL.md names no scripts, command-line tools or credentials: Human In The Loop is instructions for the agent only.
Does Human In The Loop access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Human In The Loop safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Human In The Loop use?
Human In The Loop is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Human In The Loop use?
About 1.3k tokens (SKILL.md is roughly 5.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Human In The Loop?
Skills that share tags, products or a category with Human In The Loop: Show Me Your Work Decision Log (cursor/plugins, 10k stars), Darwin Skill Optimizer (alchaincyf/darwin-skill, 6.2k stars), Loop Constraints Enforcer (cobusgreyling/loop-engineering, 11k stars) and Ask User Question (MemTensor/MemOS, 12k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Human In The Loop?
mvschwarz (a GitHub user) maintains it in mvschwarz/openrig, which has 5,854 GitHub stars. The repository holds 49 skills in this directory. The repository was last updated on October 8, 2026.
Source: mvschwarz/openrig on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.