tech-advisor
Skill10 starsChanged 56 days agoMITView on GitHub
Advise a technology choice through current evidence. Use for choosing tech products for a concrete use case, or assessing whether a technology is mature enough to adopt.
In this repository8 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- article-auditSkill
- competitive-intelSkill
- deep-researchSkill
- fact-checkSkill
- geopolitical-analystSkill
- news-monitorSkill
The file
We split this skill into the rules it gives an agent
6 rules. About 0 are common, 4 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 24
Use maturity assessment for “is this ready?” and product comparison for “which should I choose?” If intent does not settle the mode, ask …
- Distinctive: line 29
Before using tools, read source-access.md completely.
- Very specific: line 31
Use available web search and URL-fetch tools.
- Very specific: line 40
If uvx cannot run packaged search, use ddgs without touching the current project environment:
- Distinctive: line 52
If packaged page fetch fails, use Jina Reader:
- Distinctive: line 69
Read every source used for a material comparison or recommendation.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Tech Advisor
Make a current, evidence-backed technology recommendation for a specific user or team.
Choose one mode
- Maturity assessment: one technology is being considered for adoption. Read maturity-assessment.md completely, then follow it.
- Product comparison: two or more products, or a product category, must be chosen for a concrete use case. Read product-comparison.md completely, then follow it.
Use maturity assessment for “is this ready?” and product comparison for “which should I choose?” If intent does not settle the mode, ask one focused question.
Tools and evidence
Before using tools, read source-access.md completely.
Use available web search and URL-fetch tools. Prefer callable names ending in
duckduckgo_search and web_fetch; client-added prefixes vary, so inspect the tools in
the session. Without session tools, run the packaged CLI in an isolated environment:
uvx --python '>=3.10,<3.14' web-forager search "your query" --max-results 8 --output-format json
uvx --python '>=3.10,<3.14' web-forager fetch "https://example.com" --format markdownIf uvx cannot run packaged search, use ddgs without touching the current project
environment:
uv run --no-project --python '>=3.10,<3.14' --with 'ddgs>=9.5.2' python - <<'PY'
from ddgs import DDGS
results = DDGS().text(query="your query", max_results=8)
for r in results:
print(r["title"], r["href"], r["body"])
PYIf packaged page fetch fails, use Jina Reader:
curl -s "https://r.jina.ai/https://example.com"For a YouTube caption failure, report the access gap without forwarding the video URL.
The workflow requires both search and fetch. If either capability is unavailable, state what is missing instead of recommending from stale knowledge.
Technology changes quickly. Use [current year] in queries, verify the exact current
model or version, and prefer recent first-party specifications plus current independent
evidence. Label estimates and inference.
Shared quality bar
- Read every source used for a material comparison or recommendation.
- Match evidence to the user's region, constraints, and use case.
- Include total cost and meaningful tradeoffs, not only headline specifications.
- Cite material claims near the text they support.
- Take a position when the evidence supports one; state what would change it.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

