Install
$ agentstack add skill-jamestorrevillas-dev-skills-research-synthesis ✓ 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
Research & Synthesis
Research Process
1. DEFINE — What specific question am I trying to answer?
2. GATHER — Collect sources (docs, benchmarks, community, real usage)
3. EVALUATE — Assess source quality and recency
4. SYNTHESIZE — Extract patterns and insights across sources
5. DECIDE — Apply findings to your specific context
6. DOCUMENT — Record the decision and reasoning for future reference
Technology Evaluation Framework
For every technology comparison:
| Dimension | What to Evaluate | |---|---| | Fit | Does it solve your actual problem? | | Maturity | Production-proven or experimental? | | Community | Active maintenance, GitHub stars, Stack Overflow presence | | Performance | Benchmarks relevant to your use case | | Learning Curve | Team familiarity, documentation quality | | Ecosystem | Integrations, libraries, tooling | | Cost | License, infrastructure, ops overhead | | Exit Cost | How hard to migrate away if needed? |
Comparison Document Template
## Decision: [technology choice]
### Context
[What problem are we solving? What are our constraints?]
### Options Considered
| | Option A | Option B | Option C |
|--|---------|---------|---------|
| Fit | | | |
| Maturity | | | |
| Performance | | | |
| Team familiarity | | | |
| Long-term risk | | | |
### Decision
[What we chose and why]
### Trade-offs Accepted
[What we give up with this choice]
### Revisit If
[Conditions that would prompt reconsideration]
Source Quality Hierarchy
- Official docs — authoritative, but may be biased toward positives
- Benchmarks — verify the benchmark matches your use case
- Production case studies — most reliable, but rare
- Community discussions — useful for gotchas and real-world issues
- Blog posts — variable quality, check date and author credentials
Always check the date. Technology moves fast — 2-year-old articles may be outdated.
Using AI for Research
"Research [topic] and summarize:
1. What problem it solves
2. When to use it vs alternatives [A, B, C]
3. Known limitations and gotchas
4. Current community sentiment (2025-2026)
5. One concrete example of production usage"
Cross-reference AI output with official docs — AI can hallucinate library APIs or outdated information.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: jamestorrevillas
- Source: jamestorrevillas/dev-skills
- 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.