academic-deep-research
No security findings. Scanned 1 time, most recently 2026-02-05.
Second scanner: NVIDIA SkillSpector
Independent static run, no language model, over the same files as the Cisco scan above. How the two scanners compare across the whole corpus: Two scanners, one corpus.
What this skill does
Transparent, rigorous research with full methodology — not a black-box API wrapper. Conducts exhaustive investigation through mandated 2-cycle research per theme, APA 7th citations, evidence hierarchy, and 3 user checkpoints. Self-contained using native OpenClaw tools (web_search, web_fetch, sessions_spawn). Use for literature reviews, competitive intelligence, or any research requiring academic rigor and reproducibility.
- Package
- 4 files, 42 KB
- SKILL.md
- 3208 words, 12+ sections
- Code
- None. Instructions only.
SKILL.md covers: Academic Deep Research 🔬 · When to Use This Skill · Tool Configuration · Core Structure (Three Stop Points) · Phase 1: Initial Engagement [STOP POINT — WAIT FOR USER] · Phase 2: Research Planning [STOP POINT — WAIT FOR APPROVAL] · Phase 3: Mandated Research Cycles [NO STOPS — EXECUTE FULLY] · Error Handling Protocol · Empty or Insufficient Search Results · Contradictory Sources Cannot Be Resolved · Source Quality Concerns · Technical Failures
Findings
No findings. SAFE
What is in the package
| File | Kind | Size |
|---|---|---|
README.md | doc | 3.8 KB |
SKILL.md | doc | 23 KB |
example.md | doc | 12 KB |
quickref.md | doc | 2.9 KB |
External hosts referenced
doi.org github.com
Hostnames that appear in the skill's files. Referencing a host is not a finding; compare against the findings above and the skill's stated purpose.
Package contents recorded 2026-10-22. The scan above ran against the version current at scan time; the two can differ if the skill has since been updated.
What we checked
- Static analyzer. Pattern rules over SKILL.md and bundled files: known-bad commands, exfiltration URLs, secrets, obfuscation.
- Behavioral analyzer. Checks what the skill asks the agent to do against what its description claims it does.
- Llm analyzer. A language model reads the skill and reports intent-level issues the rules cannot express.
- Trigger analyzer. Looks for instructions that fire on conditions the user did not ask for.
- Meta analyzer. A second model pass that validates, deduplicates and re-scores the other analyzers' findings.
What we did not check
- Runtime behaviour. The skill was not executed; findings come from reading its files.
- Anything fetched at install or run time, including remote scripts, package dependencies, and URLs the skill tells the agent to open.
- Content the analyzers cannot read: images, binaries, and some non-English text.
- Known scanner bypass classes. See the methodology and limitations page.
Sources
- Repository: https://clawhub.ai/skills/academic-deep-research
- Raw scan result: academic-deep-research-scan.json
- Badge for your README:
- Request a re-scan