Plugins

Free-tier model aggregation for the DeepSeek Harness (dsh): registers the freeroute provider over a pool of free-quota upstreams (OpenCode Zen / B.AI / OpenRouter / SenseNova built in, plus remote catalogs and custom gateways), transparent failover before

21 read-only lifecycle governance tools in one self-contained DSH plugin: task tiering, state/plan/evidence validation, gate precheck and aggregation, release/install audits, context and progress analysis, critical path, resume reconciliation, and a mutation preview that always denies execution. Installs from npm as @240xu/dsh-tech-lead.

Composer stats strip for the DSH Web UI: official CNY pricing with auto-synced peak/off-peak tiers, per-model accounting, live timers, subagent-tree merging, DeepSeek account balance, budget alerts, and streaming cost estimation.

Adaptive model discovery, evaluation, tiering, and subagent routing for DeepSeek Harness.

Free-tier model routing for the DeepSeek Harness (dsh) - discover OpenRouter free models, apply the best free model as agent default, auto-rotate on rate limits; paid config untouched unless asked.

Product research skill: analyze market, technology and supply chain, then output three-tier product spec proposals.

DeepSeek account balance and model cost tracking with official peak/off-peak pricing, three-tier token billing (cache miss, cache hit, output), per-model and per-session breakdowns, a composer summary bar with hover details, CSV export, and a settings panel.

Tiered model routing for DeepSeek Harness: think/build model tiers, vision delegation (the vision model only describes images; the working model acts), image generation, and a high-impact guard.

Auto-approval permission guard: a middle tier between workspace-write and danger-full-access — auto-allows safe operations inside trust directories, always asks a human for destructive ones, with 11 per-category switches and an audit trail.

Commitment write-gate: operator-authored rules enforced before a tool call runs — a deterministic guard tier plus an LLM-judge tier, fail-closed by default, every block written to a contradictions log.

Mention a server with @ in a DeepSeek Harness session and run commands on it over SSH, behind a two-tier approval gate that fails closed.

An elevated Plugin list for DeepSeek Harness Web Settings — trust tiers, collapsible groups, and complete per-plugin detail

A project-agnostic multi-agent collaboration protocol for the DeepSeek Harness: loop guards that stop runaway agent loops, a six-field handoff format, risk-tiered review routing, and a fixed verify→commit→push→review→close sequence. One skill instead of the same rules copy-pasted into every agent's instructions.

Clusters arXiv, first-party lab, official artifact, hardware-vendor, and opt-in X signals into versioned release evidence bundles, then gates claim-level reproduction on commands, artifacts, metrics, and verifiers.

Two-tier model router for DeepSeek Harness — LLM-Judge routing, multi-model fallback chains, exponential-backoff failover, and task-level orchestration (DSH adaptation of pi-shift-router)

A middle permission tier for DeepSeek Harness between workspace-write and danger-full-access: auto-approves harmless commands and operations targeting configured trusted areas, beyond the current workspace.

Scheduled daily toast popups summarizing yesterday's conversations and managing two-tier pending TODOs.

Keep conclusions alive across context compaction: the model writes a task checkpoint and the plugin re-injects it into the system prompt's first node — the span compaction never touches, so it survives any number of compactions. Four-tier injection (0/95/175/≤12000 chars) scales with session relatedness; checkpoints live in the plugin's own folder, never in your workspace.

Drives the OpenCode client already installed on your machine through its loopback opencode serve API, so its free-tier models work in DSH — the child process starts on first use, and OpenCode executes no tools of its own: a call it proposes is handed to the DSH tool of the same name and run there, or refused when there is no equivalent. No API key is needed.

An Auto toggle next to the model selector that schedules reasoning_effort per request: complexity is scored by a MiniMax/OpenAI-compatible model with a zero-dependency heuristic fallback (fallbacks not cached), effort ladders adapt to each model real effort list (low/high/max, low/high/highx/max/ultra, ...), heavy tool calls upgrade the tier within the same session, the lit button and reply badges show the effort actually used, and every decision plus observed token usage lands in a JSONL ledger. A standalone-button fork of imkingjh999/dsh-adaptive-effort; do not install it and the original in the same profile.

Switch OpenAI models between Codex-style Responses WebSocket transport and SSE from the composer, inheriting DSH provider settings with an optional priority-tier toggle.

Five-tier reasoning protocol injected into the system prompt — off / light / standard / deep / multi-agent, 0 / 3 / 9 / 17 / 19 rules — with the tokens each tier adds per request shown beside it. Alongside it: a claim ledger that marks a load-bearing claim with no evidence or check as unsupported, read-only analyst briefs, JSON-declared custom tools that work in the same turn, one persistent PowerShell session, and re-injection of host context after a configurable message gap. Tier 0 injects nothing and mounts no tools. A derivative of husongan/dsh-deepseek-2027 (MIT); do not install both — each injects its own reasoning-protocol section into the system prompt.

Runs an approved OpenSpec change to completion while the model stays a mechanical forwarder: a bundled Python CLI computes the ticket DAG frontier, claims tasks atomically, prepares one git worktree per assignment, and returns exact actions. The session delegates each action with the DSH subagent tool and reconciles the completion notice against a hash-bound receipt produced by re-running verification commands on a clean, advanced HEAD, so free-text "done" never counts. Runtime scope changes are plain tasks.md edits. Requires Python 3.10+ on PATH and a git project that uses OpenSpec.

Measures one university department's research output: matches author affiliation strings against a college's known name variants (detecting same-named departments at other universities), backfills papers that only name the university by faculty author IDs, classifies each paper by journal tier (CAS partition with Top flag, JCR quartile, CCF, Chinese core via a CNKI export merger), splits staff from student first authors, and writes a workbook with a dashboard sheet, a filterable paper list, a teacher-by-direction matrix and per-teacher statistics. Student authorship is inferred from affiliation and staff lists, not verified against enrolment records. Requires Python 3.9+ with pandas and openpyxl (pip install -r requirements.txt); papers come from the OpenAlex API, whose free per-IP quota is limited, and Chinese-journal coverage needs a manual CNKI export.