daily-meeting-update
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
Interactive daily standup/meeting update generator. Use when user says 'daily', 'standup', 'scrum update', 'status update', 'what did I do yesterday', 'prepare for meeting', 'morning update', or 'team sync'. Pulls activity from GitHub, Jira, and Claude Code session history. Conducts 4-question interview (yesterday, today, blockers, discussion topics) and generates formatted Markdown update.
- Package
- 3 files, 30 KB
- SKILL.md
- 1761 words, 12+ sections
- Code
- Python (1)
SKILL.md covers: Daily Meeting Update · Workflow · Phase 1: Detect & Offer Integrations · Step 1: Silent Detection · Step 2: Offer GitHub/Jira Integrations (if available) · Step 3: Pull GitHub/Jira Data (if approved) · Step 4: Offer Claude Code History · Phase 2: Interview (with insights) · Question 1: Yesterday · Question 2: Today · Question 3: Blockers · Question 4: Topics for Discussion
Findings
No findings. SAFE
What is in the package
| File | Kind | Size |
|---|---|---|
README.md | doc | 7.4 KB |
SKILL.md | doc | 12 KB |
scripts/claude_digest.py | script | 10 KB |
External hosts referenced
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 at commit b00cf8491de1. 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://github.com/softaworks/agent-toolkit
- Raw scan result: daily-meeting-update-scan.json
- Badge for your README:
- Request a re-scan