Dsh Tech Selection

dsh-tech-selection

**Stop letting your AI guess. Get a method that finds the truth.**

A **model-agnostic research protocol** for technology selection and solution comparison. Works with **any AI agent** — DSH, Claude Code, Cursor, Codex, or plain ChatGPT.

中文说明见 [README.zh-CN.md](README.zh-CN.md)

Why

AI is making technology decisions every day — choosing a database, comparing frameworks, vetting open-source options. Yet most agents research **unpredictably**: a strong model today, a weak model tomorrow, missing sources, trusting blog spam, no verification. The quality of the answer depends on the model's luck, not a method.

This skill fixes that: **the method, not the model, guarantees coverage.**

What

A fixed, model-agnostic 6-step protocol:

1. Requirement clarification — quantify constraints (scale/latency/availability/compliance/budget). No quantified constraints, no conclusion.
2. Multi-source retrieval — parallel queries; source tiers T1–T6 (primary docs > institutions > experts > editorial > community > content farms); GitHub API for live facts (stars/archived/updated).
3. Gap checklist — competitors × maintenance × ecosystem × local reachability × security/compliance × cost × migration × freshness.
4. Weighted evaluation matrix — weights aligned with the decision-maker before any verdict.
5. Traceable output — decision table + source tier & URL per key fact + "as of YYYY-MM" dates; no source, no conclusion.
6. Retro & iterate — capture gaps, backfill the protocol (CHANGELOG).

**Quality gates** between stages: no ≥2 independent sources → back to retrieval; weights not aligned → no verdict; facts not traceable → dropped.

**Failure modes checked**: SEO farms, early-retrieval contamination (57% of source errors happen in the first retrieval round), stale-as-current, survivorship bias, citation loops.

Install

**DSH (DeepSeek Harness):**

mkdir -p ~/.dsh/skills/tech-selection-research
cp tech-selection-resea