skill-neuroanalytics-data-science-harness-checkpoint · by Install
$ agentstack add skill-neuroanalytics-data-science-harness-checkpoint ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Passed review? Show it. Paste this badge into your README — it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-neuroanalytics-data-science-harness-checkpoint) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming — see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps — measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Skill: checkpoint
Take a clean, described snapshot of the dataset. This keeps the provenance chain continuous (a dirty working tree produces misleading run records downstream). You delegate the save to the datalad doer.
> v1 note: checkpoint is on-demand — invoked by the user or suggested by the coordinator. An > automatic end-of-session datalad save (a Stop/session-end hook) is intentionally deferred: > how often vs. how long to trigger it is a latency/tuning question that likely varies per study > (plan gap B5), best decided after real use.
When to use
- The user is pausing/ending a session, or wants intermediate state recorded.
- Do NOT use to record a provenanced computation — that is
analyze/run-comparison
(container-run), which already commits its own outputs. Checkpoint captures hand edits, notes, and other loose changes.
Steps
- Inspect state — delegate to the datalad doer:
> "status: report modified/untracked files in this dataset and the current branch." If nothing is unsaved, tell the user there is nothing to checkpoint and stop.
- Compose a message — summarize what changed since the last save into a meaningful
-m
(e.g. "checkpoint: draft stats.py + participant notes"). Ask the user if the change set is ambiguous.
- Save — delegate to the datalad doer:
> "save: datalad save -m '' on the current branch."
- Log it — append to
project.yaml:
{ ts, op: checkpoint, stage: , note: "", branch: }.
- Report — the commit sha and current branch.
Constraints
- Delegate status and save to the datalad doer; never call datalad directly.
- Always use a meaningful message — never "wip"/"save"/placeholder.
- Keep
project.yamlappend-only. - Do not force-save over a comparison mid-run; if a
container-runis in progress, let it finish
(it commits its own outputs).
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: neuroanalytics
- Source: neuroanalytics/data-science-harness
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.