omp-plugin-dynamic-system-prompt

Scales system-prompt guidance to the model that will read it — less instruction for frontier models, more for the ones that need it.

An oh-my-pi plugin. It appends one ## Model tuning section to the system prompt on every turn, choosing the directives from the model actually in use.

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Why

A prompt written for a weak model wastes tokens and credibility on a strong one. "Stop searching once you have the answer" is worth saying to a model that keeps searching; on a frontier reasoner it is noise that displaces something useful.

So the guidance is inverted from the obvious direction: capability buys silence. A frontier flagship gets a light prompt. An older or small-tier model in the same family gets more. An unrecognized model gets the heaviest budget, on the assumption that it needs the most help.

gpt-5.6            -> light   (336 chars of 700)
claude-opus-4.5    -> medium  (286 chars of 1600)
claude-haiku-4.5   -> heavy   (286 chars of 3000)
glm-4.5-air        -> heavy   (298 chars of 3000)

The block never names the model, its version, or the resolved profile. Nothing tells the model it is being profiled.

Install

omp plugin install omp-plugin-dynamic-system-prompt

It works with no configuration. Every setting below has a default.

How it works

Three stages, one per module.

model id  ->  resolve axis  ->  select rules  ->  render block
              (model-axis)      (render)          (render)

1. Resolve the axis. The model id is normalized (bareModelId, lowercased, with suffixes like :thinking, :free, -high stripped) and classified into three independent dimensions:

Dimension	Values
family	openai anthropic gemini glm kimi deepseek grok qwen minimax unknown
version	numeric series — gpt-5.6 → 5.6, kimi-k2.7 → 2.7; null when unparseable
tier	flagship mid small

Parsing is delegated to @oh-my-pi/pi-catalog/identity rather than reimplemented, so new model ids are recognized as the host catalog learns them.

2. Derive the weight. weight falls out of how far the version sits behind the family's frontier, plus a penalty for small-tier models. unknown always lands heavy.

Distance from frontier	flagship / mid	small
current generation	light	medium
one generation back	medium	heavy
older, or unparseable	heavy	heavy

3. Select and render. Rules whose applies(axis) holds and whose minWeight fits are sorted by priority, reduced to one rule per concern, then trimmed from the lowest priority end until the rendered block fits CHAR_BUDGET[weight] — 700, 1600, or 3000 characters.

When no rule applies, nothing is appended and the prompt is returned untouched. This is the normal outcome for an unrecognized model: it resolves heavy, but every rule gates on a known family, so the block is empty.

Worked examples

model id	profile	weight	rules	block / budget
gpt-5.6	openai-5.6-flagship	light	2	336 / 700
gpt-5.5	openai-5.5-flagship	light	1	175 / 700
claude-opus-5	anthropic-5-flagship	light	3	498 / 700
claude-sonnet-5	anthropic-5-mid	light	2	337 / 700
kimi-k3	kimi-3-flagship	light	4	692 / 700
grok-4.5	grok-4.5-flagship	light	4	653 / 700
gemini-3-pro	gemini-3-flagship	medium	1	206 / 1600
claude-opus-4.5	anthropic-4.5-flagship	medium	2	286 / 1600
claude-haiku-4.5	anthropic-4.5-small	heavy	2	286 / 3000
deepseek-v4-pro	deepseek-4-flagship	light	2	341 / 700
deepseek-v4-flash	deepseek-4-small	medium	5	786 / 1600
glm-4.5-air	glm-4.5-mid	heavy	2	298 / 3000
grok-3-mini	grok-3-small	heavy	1	184 / 3000
claude-opus-6	anthropic-6-flagship	light	0	0 / 700
some-unlisted-model	unknown-flagship	heavy	0	0 / 3000

What glm-4.5-air actually receives:

## Model tuning

Settle one reading and act on it; reopen it only when a tool result
contradicts it. Re-deliberating the same evidence adds no information.

When the answer is already in context, return it. Skip filler verification
language and do not re-derive facts you settled earlier this turn.

Settings

Configurable in omp's settings UI, in ~/.omp/plugins/omp-plugins.lock.json, or by environment variable. The lockfile wins over the environment.

Setting	Env	Values	Default	Effect
enabled	OMP_MODEL_TUNING	boolean	true	Append the block at all.
weight	OMP_MODEL_TUNING_WEIGHT	auto light medium heavy off	auto	Override the derived weight. off disables the plugin.
showStatus	OMP_MODEL_TUNING_STATUS	boolean	true	Show the tuning weight and injection state in the omp footer.

With showStatus on, omp's footer carries one line: ● tuning heavy when the block was appended this turn, ○ tuning heavy when the resolved model matched no rule and nothing was appended. omp strips colour from extension status text, so the symbol is the whole signal. Switching models with /model updates it on the next turn, not immediately — omp emits no model_select event, so the axis is resolved when the turn starts.

Rules

Sixteen rules across twelve concerns. Only one rule per concern ever ships, so priority decides which of several competing directives a given model sees.

id	concern	priority	min weight
declared-stop	completion	8	light
verify-once	verification	9	light
real-surface-verify	verification	10	light
settled-reading	deliberation	11	light
reasoning-aim	deliberation	12	light
effort-calibration	deliberation	13	medium
no-filler-verification	verification	14	medium
injected-directive-authority	harness-contract	15	medium
search-stop	retrieval	16	medium
reflect-then-ask	ambiguity	20	light
confirmation-executed	re-litigation	21	medium
todo-live	todo	22	medium
recommendation-not-survey	reporting	25	light
scope-literalism	scope	30	light
compaction-persistence	context-budget	40	medium
house-style-counter	aesthetics	50	medium

The table is deliberately sparse. A rule earns its place only on evidence that a family needs the correction and that omp's base prompt does not already make it. Every light rule is also bounded to the series its evidence names, so a rule written for one generation cannot follow the family into the next — claude-opus-6 and gpt-6 resolve to an empty block for the same reason an unrecognized model does.

Development

bun install
bun test ./test                        # 74 tests across 6 files
bun x tsc --noEmit -p tsconfig.json    # typecheck

One peer dependency: @oh-my-pi/pi-catalog at >=16.0.5 with no upper bound, satisfied by the host omp. Every other import is a Node builtin.

Two invariants worth knowing before changing anything:

• Append-only. The before_agent_start handler never replaces omp's system prompt, and it checks for TUNING_HEADER before appending so the block cannot accumulate across turns.
• The block is anonymous. No model name, version, or profile string may appear in the rendered output.

AGENTS.md carries the full contract.

License

MIT