| name | auto-model-selection |
| tier | meta |
| description | Use Context7 CLI for up-to-date model facts (size, API format, context window) and the guidance below for "what kind of models are good for what part of a doc verification app". Consult whenever you need to pick a model for a tier slot, decide between provider alternatives, or sanity-check an existing tier assignment. Context7 gives you fresh facts; this skill gives you the heuristic that maps those facts onto KC's pipeline. Optional plugin (Context7 install: npm i -g context7).
|
Auto Model Selection
Model selection is not a frequently called skill — for most users, the tiers in workspace .env are already set sensibly, and a 4-tier cost-sensitive selection is overkill. This skill exists for two moments: when the conductor is bootstrapping fresh tier assignments (rare), and as a reference inside skill-to-workflow when a workflow needs to pick which worker LLM is right for its job.
The teaching below is empirical — what the author has found works in this domain. Treat it as a starting heuristic with a 3-6 month shelf life, since model families update quickly.
Worker LLM family — practical heuristic
- Qwen family — robust and cheap for general worker work. The flagship MoE in this family at any given time is usually one of the best workhorses for routine extraction / classification. Smaller sizes (3B-70B) are plentiful and reliable. Good default.
- DeepSeek — excellent on more complicated tasks. Worth reaching for when the rule involves multi-step reasoning, nested judgment, or anything where Qwen's shorter-context behavior shows strain.
- GLM and Kimi — also strong for the same complicated-task range as DeepSeek. Trade-off: they don't usually ship smaller variants (3B-70B), so they're tier1/tier2 only, not tier3/tier4.
Flagship-MoE shape and the tier1 baseline
The current generation of flagship MoE LLMs has a recognizable shape: total parameter count in the 200-400B range, ~20B of activated expert size per token. Examples (these will be stale within months): Qwen's flagship MoE in the 200-400B-A20B band, DeepSeek-V4-Flash, etc.
This shape is a good first-choice worker LLM — not necessarily the absolute best at tier1, but a reasonable benchmark to start from. When you're picking a tier1 model, start from one of these and only move if you have a specific reason.
Smaller LLMs — basically free below 30B
Once you drop below ~30B parameters, models are extremely cheap on most providers. Qwen ships a ton of choices in this range and they work well.
Two rules for picking in this range:
- Avoid "coder" variants (models with
coder / code in the name) at small sizes — these are mostly unreliable for general worker tasks. Use them only when the task is literally code-related.
- Prefer no-thinking-mode variants when available. Tasks assigned to small workers are simple and fixed; extra thinking buys nothing and adds latency + token cost.
VLM / OCR selection
First question: what's the visual task?
- Characters in scanned docs, seal stamps, handwriting — use a dedicated OCR model. Current strong choices (subject to change): Paddle-OCR family, GLM-OCR, DeepSeek-OCR. Previous versions still work fine. No need to use a larger general VLM for plain character recognition.
- Graphs, complex tables, structures with strange or no frame lines — try a larger and more expensive general VLM. The structure-understanding gap between OCR-specific models and general VLMs widens fast on these cases.
When in doubt, run the cheapest OCR option first and only upgrade if the cheap path misses structure information.
Context7 — model facts on demand
The heuristics above are about which kind of model to pick. For the specific facts (current model names, exact context window, pricing, API format), use Context7:
c7 library <provider-name>
c7 docs <libraryId> "available models"
Two commands. The first finds the provider's library ID, the second fetches up-to-date docs and code examples. Useful when:
- Workspace
model-tiers.json looks stale (KC hasn't been updated since the last model launch)
- User switched providers and needs model discovery
- The
/models endpoint on a new provider was empty or unhelpful
- You're sanity-checking that a model name in
.env is still served
Install: npm i -g context7. Verify: c7 library openai should return results.
When tier1/tier2 are picked
The tier assignment ends up driving cost. Cheapest model that meets the accuracy bar wins. Regex is the implicit "tier 0" and should be the first reach when the rule can be satisfied by pattern matching alone — see skill-to-workflow for when to escalate from regex to worker LLMs.
Not all tiers need to be filled. Blank tier3/tier4 slots are fine if the provider doesn't ship suitable small models. Record what works in AGENT.md so the next session inherits the choice.
Refresh cadence
This skill's heuristics will drift. Author plans to revisit every 3-6 months as new model generations land. If you find yourself applying advice here that contradicts what Context7 shows today, trust Context7 — the facts have moved.
