Install
$ agentstack add skill-neuroanalytics-data-science-harness-log-decision ✓ 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.
Verified badge
Passed review? Show it. Paste this badge into your README — it links to the public security report.
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: log-decision
Capture the why behind a choice so future-you (or a collaborator, or a reviewer) can reconstruct the reasoning, not just the result. Decisions are recorded as tracked log entries in the ledger — provenance for judgment, alongside the provenance for computation. You delegate the save to the datalad doer.
When to use
- A non-obvious methodological or design choice was made (a model, an exclusion criterion, a tool,
dropping/keeping a comparison) and its rationale should be preserved.
- Do NOT use to record a computation (
analyze/run-comparisonalready provenances that) or to make
a formal commitment (govern/preregister / govern/obligations).
Steps
- Capture the decision — get: the
decision(what was chosen), therationale(why), the
alternatives considered and why they were not chosen, and the scope (which branch/comparison/ product it affects, if any).
- Append to the log — one entry to
project.yamllog[]:
{ ts, op: log-decision, stage: manage, note: "DECISION: — WHY: — ALT: ", branch: }. Keep the note self-contained so the reasoning is legible from the log alone.
- Save — delegate to the datalad doer: "save:
datalad save -m 'log-decision: '." - Report — confirm the decision was recorded and on which branch.
Constraints
- Record the decision and its rationale and alternatives — a decision without its "why" is not
worth logging; do not reduce it to a bare statement.
- Record honestly: capture the actual reasoning, including trade-offs, not a post-hoc justification.
log:is append-only — a reversed decision is a new log-decision entry referencing the prior
one, never an edit of it. Keep the ledger schema-valid; delegate the save to the datalad doer.
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.
Reviews
No reviews yet — be the first.
Write a review
Versions
- v0.1.0 Imported from the upstream source.