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.
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Run score Not tested
See how to installReview the instructions the agent will receive before you install.
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 required
review Artifacts.DECISIONS.md is the human-readable Decision surface..harness/decisions.jsonl is the machine-readable history; update it
through 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 the
Markdown checkbox. Prefer scripts/pipeline.py approve, which also records
the machine Decision.checkpoint-brief before approval. Use pipeline-router only for the
initial C0 route.No reviews yet. Tell others what you thought.
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Internal template for creating or refactoring a skill into the repository's reference-first shape. **Trigger**: reference-first template, blueprint skill, create a reusable skill, refactor a script-heavy skill. **Use when**: you need a lean `SKILL.md`, explicit `references/`, machine-readable `assets/`, and a minimal deterministic `run.py`.
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Download a small corpus of open-access arXiv survey/review PDFs about agentic systems and extract text for style learning.
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Extract per-subsection “anchor facts” (NO PROSE) from evidence packs so the writer is forced to include concrete numbers/benchmarks/limitations instead of generic summaries.
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Curate reader-facing survey tables for the Appendix (clean layout + high information density), using only in-scope evidence and existing citation keys.
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Build a final, machine-readable argument snapshot from current H3 sections and refresh section fingerprints before merge.
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Audit one research Workspace for declared Unit outputs and Pipeline target Artifacts, writing `output/CONTRACT_REPORT.md`; use for mid-Run coverage snapshots or final delivery completeness, not deep provenance integrity.
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