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51 lines
2.7 KiB
Markdown
51 lines
2.7 KiB
Markdown
# CNCF Landscape
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Discover cloud-native technologies and turn the result into a decision-ready shortlist with evidence, trade-offs, and a validation plan.
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## Why Install This Skill
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When an architecture question starts with “what exists for this?”, an agent can easily return a familiar-name list or rank projects by stars. This skill gives it a live, source-grounded discovery path through the CNCF Landscape and a disciplined way to separate catalog facts from engineering judgment.
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It is useful for architects and engineers exploring a capability that is not yet in their stack. The bundled query tool handles the static API’s filtering and bounded JSON output; the skill then asks the questions the catalog cannot answer: who will operate it, what constraints matter, what evidence is missing, and what small experiment could falsify the recommendation.
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## What You Get
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| Path | Purpose |
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|---|---|
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| `SKILL.md` | Trigger boundaries, query workflow, evidence discipline, and completion criteria |
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| `scripts/landscape_query.py` | Read-only stdlib CLI for live project/member queries and local filtering |
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| `references/api.md` | Verified endpoint map, field semantics, and static-site caveats |
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| `references/decision-framework.md` | Candidate comparison and recommendation method |
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| `references/output-template.md` | Reusable decision artifact structure |
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| `tests/test_landscape_query.py` | Offline client and filter tests |
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| `evals/evals.json` | Six output-quality cases covering normal and failure paths |
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| `evals/trigger-queries.json` | Three should-trigger and two should-not-trigger routing probes |
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## Quick Start
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Requires Python 3.8+ and outbound HTTPS access. No API key is required.
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```bash
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python3 scripts/landscape_query.py \
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--category "Observability and Analysis" \
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--subcategory Observability \
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--search tracing \
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--maturity graduated \
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--has-license --has-release \
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--sort stars --limit 10
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```
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The command emits a JSON envelope containing the source endpoint, retrieval time, filters, counts, and matching records. Ask an Agent Skills-compatible assistant to interpret that evidence against your workload and constraints rather than treating the result as an automatic ranking.
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## Triggers
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Use when discovering or comparing CNCF/cloud-native projects, building a shortlist for an architecture decision, filtering technology candidates by maturity or repository evidence, or investigating what tools exist for a capability missing from the current stack.
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Do not use it for operating a named project, general architecture methodology, or legal/procurement conclusions.
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## Requirements
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- Python 3.8 or newer
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- Network access to `https://landscape.cncf.io`
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- No credentials or third-party Python packages
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