dsh-effort-router

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A DSH (DeepSeek Harness) plugin: context-aware per-step reasoning-effort routing, plus reasoning levels for hand-declared llm-pi-ai custom models.

• Task-shape classification: trivial / standard / engineering / hard from the brief itself, not from tool-call counts
• Evidence escalation: tool errors and identical-argument retries lift the class (capped per task)
• Hysteresis: upgrades apply immediately, downgrades wait for two consecutive quiet decisions
• Frugal signals: subagents, context pressure, or a session token budget demote one class
• Capability clamp: the scheduled tier maps onto the target model's advertised vocabulary (nearest rank, so gateway vocabularies like ultra work)
• Manual wins: a concrete level in the selector passes through verbatim; Auto hands the step to the scheduler

Why an Auto mask instead of a panel

The host's native model selector already is the per-session effort control. The plugin advertises an Auto level on every reasoning-capable model (injected into reasoning.efforts, resolved to a concrete wire level by the request interceptor before validation), so there is no second selector to keep in sync: Auto schedules, a concrete level passes through.

Custom models (llm-pi-ai)

Hand-declared custom models often ship no reasoningEfforts table, so the composer shows no effort selector at all. On startup and on settings changes the plugin scans the llm-pi-ai config and fills the default { off: null, high, max } table through a resolution gate: every table-less user-layer row is resolved with resolveModelInfo, and only models that resolve with NO thinking levels are filled — a model inheriting levels from its catalog is skipped, so catalog capability is never shadowed. An explicit table or an explicit false is never touched. Tables follow the pi-ai gate exactly (off: null omits the parameter on the wire — the shape the native Models page writes), and models filled on explicit openai-completions routes also gain compat.supportsReasoningEffort: true, the switch that protocol needs to actually send the effort.

The per-model level → gateway wire-value mapping (e.g. high → ultra) is edited under Settings → Effort Router and written straight into the llm-pi-ai config, gated by the official schema, effective on the next request. pi-ai serializes the table natively; no plugin-side translation layer.

Settings

Settings → Effort Router:

• Enabled / default level / subagent default
• Bounds: allow max (off by default, hard falls back to high), max fallback, frugal demotion, context-pressure threshold, token budget
• Class → tier route table (trivial/standard default low, engineering high, hard max)
• Advanced: downgrade hysteresis, escalation thresholds (apply on plugin reload)
• Custom-model editor: expand a provider, tick levels and enter wire values, or fill missing levels everywhere in one click

In-session visibility

• Composer badge (right of the model selector): the mode and effort of the session's last step (Auto · high), with class, score and reason in the tooltip. Read-only — control stays in the native selector.
• Tool-call captions: every tool call in the chat flow is annotated with the provider/model · effort that step actually ran with.

Safety and degradation

• The interceptor is one try/catch: any error passes the request through untouched (fail-open)
• Models without reasoning metadata never receive reasoningEffort — never an UNSUPPORTED_REASONING_EFFORT
• Automatic routing never emits off (thinking continuity: a tool call made with thinking off poisons every later thinking-enabled request); an already-poisoned history pins off with a warning
• Session reads go through deriveMessages() + sessionProjections only; an empty sample raises an explicit warning instead of silently degrading

Authorship & maintenance

• Author: kimi-k3 (an AI model, designing and coding through DeepSeek Harness).
• Producer / repository owner: KEI.NEO (@neo805) — the human publisher responsible for this repository.
• Maintenance scope: a personal hobby (vibe-coding) project, published for sharing. No maintenance or updates are promised in response to external demand.
• Contribution policy: this repository has Issues disabled and does not accept Pull Requests. Forks are welcome under the MIT license.
• Disclaimer: the software is provided "as is", without warranty of any kind (see LICENSE). Evaluate it yourself before production use.

Thanks & license

MIT. The policy core (classification / escalation / hysteresis / tier mapping / signal collection / projection / store) is adapted from @neptune810/dsh-model-router (MIT © Neptune810); the capability layer and Auto-mask pattern follow dsh-thinking-levels (MIT © drscrewdriver); the custom-model default-fill idea comes from @hytime/dsh-thinking-effort (MIT © hytime).