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Generate the AI Canvas Block 1 (B1) "Pain Points" deliverable from a Case Study. Identifies specific, observable pain points, affected users/stakeholders, and quantified current costs (time, money, quality, risk). Writes a Markdown file titled "<Case Study Name> Pain Points.md" that includes a verbatim transcription of the Case Study so downstream Canvas skills (B2, B3, ...) can consume it. Use when the user provides a Case Study and asks for pain points, B1, Block 1, or the first AI Canvas phase.
Claude Skills are markdown + scripts loaded into Claude Code; scanned with the same pipeline.
We found this by crawling public sources and nobody has attested to it. That is how most of the registry gets here, and it is not a mark against the artifact — it only means no authenticated act of publication is on record. An artifact can be the vendor's own and still appear here.
Claude Skills install to a single target, no runtime picker.
nerlo install -- 'marcelovolta/bigd_uba_2026 (b1-pain-points)'
Manual alternative: copy the skill folder into ~/.claude/skills/ so Claude Code loads it on next launch.
cp -r -- 'marcelovolta/bigd_uba_2026 (b1-pain-points)' ~/.claude/skills/
Install respects the composite badge: Clean proceeds, Caution prompts for confirmation, and Flagged is refused. Write operations require an API token.
5 of 11 scanners examined this package. The rest do not apply to it — language, ecosystem and packaging decide which analyzers can say anything, and a scanner that cannot examine an artifact reports nothing rather than passing it.
v1.4.0
0 findings · 0.5s
v2.0.11
Timed out
0 findings · 300.0s
v0.3.74
2 findings · 8.3s
v0.1.0
0 findings · 3.6s
v0.1.0
0 findings · 3.6s
vv0.3.2
No Go packages were found in this artifact. What each scanner covers
0 findings · 0.1s
v0.1.0
This scanner found nothing in this artifact that it assesses. What each scanner covers
0 findings · 0.9s
No report in this scan: govulncheck, osv-scanner, trivy, trivy_image.
1 scan on record. Every scan's full results are retained immutably for 24 months.
| Completed | Composite | Change | Scanners | Status |
|---|---|---|---|---|
| Aug 22, 2026 | Scan Halted | — | agentshield: completecisco-skill-scanner ·!: timeoutagent-audit-kit: completenerlo-behavioral: completenerlo-install-instruction: completecapslock ·n/a: not applicablenerlo-multi-source ·n/a: not applicable | rejected |
Findings in files this artifact installs stand as reported; a model re-read the rest and said which ones it believes are false positives. This is a second opinion published beside the evidence, not a correction to it: the per-scanner reports above are unchanged, every dismissed finding is still listed there at its original severity, and the score and the Flagged badge are computed from those raw severities alone. Nothing below moved them.
Out of 2 findings across every scanner that examined this package.
Two keys had to turn. The model had to name a reason from a fixed list of six, and a deterministic check of ours — over which files this artifact actually installs — had to independently agree. Anything it could not corroborate is in “not reviewed” above, at full severity.
1 Test or fixture file — The match is in a test or fixture. Our own check confirmed the file is not part of what gets installed.
Rules: AAK-IPI-WILD-CORPUS-001 · Files: plinplin/app/test_scene_correction.py · reported by agent-audit-kit
These findings are in files this artifact installs and are not placeholder values. No false-positive basis can be corroborated for such a finding, so this review never dismisses one; they were not sent to the model and stand at their scanner's severity.
1 AAK-HEALTHCARE-AI-004
Files: parcial01/DIA26 - Parcial 1 - Caso_ Edificio Torre del Parque.en.md · reported by agent-audit-kit
Potential for sensitive data exposure or integrity issues
The artifact contains a medium severity finding related to healthcare AI data handling. This finding indicates a potential for sensitive data exposure or issues with data integrity within the installed code. Further investigation into rule AAK-HEALTHCARE-AI-004 is warranted to understand the specific risk.
Concern level assessed by the review: Medium. This is the review's scale, not the registry badge.
Reviewed by gemini-2.5-flash. The model sees the findings, not your code, and cannot change a score.