Review and record one pending human checkpoint in a research Workspace; use when a HUMAN Unit is blocked on an `Approve C*` decision, and never treat silence or artifact existence as approval.
npx skills add https://github.com/WILLOSCAR/research-units-pipeline-skills --skill human-checkpoint
A checkpoint is consent, not a formatting step. It binds a named human
Decision to the exact Artifacts and constraints reviewed before execution may
continue.
DECISIONS.md.UNITS.csv and STATUS.md for the active checkpoint.DECISIONS.md.Inspect STATUS.md, UNITS.csv, and the active runner message. Select the
first blocked HUMAN Unit and its C* identifier. If multiple checkpoints
appear active or the checklist is missing, stop and repair the projection
before approving anything.
Completion criterion: exactly one pending checkpoint and its owning HUMAN Unit
are identified.
Read the locked Pipeline's checkpoint contract and inspect every named Artifact.
Record requested constraints or scope changes in the checkpoint block before
approval; do not silently modify reader-facing content as part of sign-off.
Completion criterion: the reviewer can name the Artifacts inspected and any
constraints attached to the Decision.
Use the Pipeline adapter so the Markdown checkbox and machine Decision ledger
remain synchronized:
uv run python scripts/pipeline.py approve \
--workspace workspaces/<name> \
--checkpoint <C*>
Do not infer approval from chat silence, a completed Artifact, or an existing
but unchecked checklist item.
Completion criterion: DECISIONS.md contains [x] Approve C* and the Run
ledger records checkpoint.approved for the same checkpoint.
Resume through the Pipeline adapter. The Harness may complete the HUMAN Unit
and expose the next eligible Unit; this Skill does not execute downstream
semantic work itself.
Completion criterion: the checkpoint is no longer the active blocker, or one
new specific blocker is visible in durable Workspace state.
pipelines/*.pipeline.md owns checkpoint purpose and requiredreview Artifacts.
DECISIONS.md is the human-readable Decision surface..harness/decisions.jsonl is the machine-readable history; update itthrough the adapter rather than by hand.
checkpoint-brief to recreate a post-route checkpoint review block.scripts/run.py is a runner-compatibility helper that only toggles theMarkdown checkbox. Prefer scripts/pipeline.py approve, which also records
the machine Decision.
with checkpoint-brief before approval. Use pipeline-router only for the
initial C0 route.
stale approval and request a new Decision.
Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.
Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.
Generates creative domain name ideas for your project and checks availability across multiple TLDs (.com, .io, .dev, .ai, etc.). Saves hours of brainstorming and manual checking.
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, research projects, or any task requiring >5 tool calls.
Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.
Comprehensive GitHub project management with swarm-coordinated issue tracking, project board automation, and sprint planning
Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".
Take willoscar/human-checkpoint from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.