智能工作流

Install via npm

The repo ships as an npm package and as a multi-IDE plugin. Use it without cloning:

```bash

install as a plugin for one (or many) coding agents

npx simplicio-prompt --target claude-code # → CLAUDE.md npx simplicio-prompt --target codex # → AGENTS.md npx simplicio-prompt --target hermes # → AGENTS.md npx simplicio-prompt --target opencode # → AGENTS.md (alias: openclaw) npx simplicio-prompt --target cursor # → .cursor/rules/*.mdc + .cursorrules npx simplicio-prompt --target copilot # → .github/copilot-instructions.md npx simplicio-prompt --target cline # → .clinerules/simplicio-prompt.md npx simplicio-prompt --target aider # → CONVENTIONS.md npx simplicio-prompt --target gemini # → GEMINI.md npx simplicio-prompt --install-all # → every target above

Install via PyPI (Python)

The reference kernel ships as a dependency-free Python package, so you can run real subagents on any OpenAI-compatible provider without cloning the repo:

pip install simplicio-prompt

```python from kernel.providers import resolve_provider_config, LLMProvider from kernel.subagent_runtime import SubagentRuntime from kernel.yool_tuple_kernel import build_default_space

Legacy `--install`

The original single-file installer still works for backwards compatibility:

npx simplicio-prompt --install CLAUDE.md
npx simplicio-prompt --install AGENTS.md
npx simplicio-prompt --install .cursorrules

How to use the prompt

Use simplicio-prompt as a canonical execution prompt for coding agents such as Claude, Codex, Hermes, Cursor, Cline, or any assistant that can read repository instructions.

1. Run npx simplicio-prompt --install CLAUDE.md (or paste the ## Prompt section from prompts/agent-runtime-execution-prompt.md into AGENTS.md, CLAUDE.md, .cursorrules, or a custom system prompt). 2. In the target repository, just ask for work in your own words. You do not need to start the message with Implement — any user input (a sentence, a bug description, a code snippet, a one-word request) is treated as the task X and routed through the same runtime. The only opt-outs are explicit stand-down phrases like "stop", "cancel", "exit runtime". 3. The agent will read the canonical files listed in the prompt, decompose the task into a Hilbert-indexed tuple graph, create a root tuple, route active work through tuple-space primitives, and use LaneWorkerPool plus the V2 safe-speed controls. 4. Status output is opt-in (default: silent). Enable with YOOL_TUPLE_STATUS=true (or status_output=true runtime flag). When on, the agent returns this shape:

[Tuple Space Snapshot]
[Active Agents/Subagents]
[Total Agents/Subagents]
[Next Yool to Execute]
[Partial Result]

Per-field toggles (default false): YOOL_TUPLE_STATUS_SNAPSHOT, YOOL_TUPLE_STATUS_ACTIVE, YOOL_TUPLE_STATUS_TOTAL, YOOL_TUPLE_STATUS_NEXT, YOOL_TUPLE_STATUS_PARTIAL.

For high-throughput local runs, set the runtime environment variables before starting the agent or scripts:

$env:YOOL_TUPLE_LANE_CONCURRENCY="32"
$env:YOOL_TUPLE_MAX_LANE_CONCURRENCY="64"
$env:YOOL_TUPLE_CPU_QUOTA_PCT="95"
$env:YOOL_TUPLE_QUEUE_MAXSIZE="8192"
$env:YOOL_TUPLE_COMPRESSION_THRESHOLD="1024"
$env:YOOL_TUPLE_CACHE_MAX_ENTRIES="16384"
$env:YOOL_TUPLE_CACHE_TTL_S="3600"
$env:YOOL_TUPLE_API_MAX_RETRIES="3"
$env:YOOL_TUPLE_API_BACKOFF_BASE_MS="100"
$env:YOOL_TUPLE_API_BACKOFF_MAX_MS="5000"
$env:YOOL_TUPLE_CIRCUIT_FAILURE_THRESHOLD="5"
$env:YOOL_TUPLE_CIRCUIT_COOLDOWN_S="30"
$env:YOOL_TUPLE_BATCH_SMALL_TASK_SIZE="32"
$env:YOOL_TUPLE_CONTEXT_COMPRESSION_CHARS="6000"

Run the reference kernel and tests:

python kernel/yool_tuple_kernel.py
python -m unittest discover -s tests -p "test_*.py"

offline cost projection / demo — no key, no network:

python kernel/subagent_runtime.py --provider deepseek --fanout-profile max \ --task "..." --dry-run --report-dir reports/fanout

Provider configuration

Per-provider env overrides (example for DeepSeek): DEEPSEEK_BASE_URL, DEEPSEEK_MODEL, DEEPSEEK_PROMPT_COST_PER_MTOK, DEEPSEEK_COMPLETION_COST_PER_MTOK. Cost defaults are illustrative — set the *_COST_PER_MTOK vars to your live contract pricing (e.g. 0.003) so the reported cost_usd matches your bill.

Optional Rust acceleration (PyO3)

For hot paths (prompt section extraction, multi-needle pattern scan, and guardrail validation) the repo ships an optional native extension written in Rust with PyO3 bindings. The default pip install simplicio-prompt remains pure-Python and dependency-free; the native extension is opt-in.

Build and install the extension locally:

pip install maturin
cd rust && maturin develop --release

That produces _simplicio_native in the current Python environment. kernel/utils/accel.py transparently picks it up on the next import:

from kernel.utils import accel

accel.backend_name()                              # "native" (or "python" w/o the wheel)
accel.extract_prompt_section(text)                # native if available, identical output
accel.find_patterns(text, ["needle-a", "needle-b"])
accel.validate_guardrails(["rule-a", "rule-b"], text)

The native path is parity-tested against the pure-Python fallback (tests/test_accel.py) so the output is identical byte-for-byte; pick whichever your install has. Measured speedups on local builds:

extract / ONESHOT (~75 lines)             ~3.0x
extract / BATCH   (~280 lines)            ~3.1x
extract / 5KB body                        ~7.0x
find_patterns / 4 needles in 5KB          ~2.2x
validate_guardrails / 6 rules in 5KB      ~7.4x

(50k iterations each, Rust 1.94, CPython 3.11.) Run python benchmarks/accel_benchmark.py to reproduce locally.

The native wheel is not published to PyPI yet — that requires a manylinux/macOS/Windows wheel matrix in CI. For now `cd rust && maturin develop` is the canonical install path.

CLI profiles resolve to an explicit API count; standard is 200.

report = SubagentRuntime(provider).run("Audit this module", subagents=200) print(report.format_summary()) # completed/failed, tokens, total cost in USD ```

CLI profiles resolve to an explicit API count; standard is 200.

report = SubagentRuntime(provider).run("audit this module", subagents=200) print(report.format_summary()) # completed/failed, tokens, total cost in USD

The install also exposes a console entry point for offline cost projection and
live fan-out:

default fan-out (200 subagents) — offline cost projection, no API key

simplicio-subagents --provider deepseek --task "..." --dry-run \ --report-dir reports/fanout

Multi-IDE plugin matrix

Claude Code plugin

plugins/claude-code/ is a full Claude Code plugin: drop it into ~/.claude/plugins/simplicio-prompt/ (or your project's .claude/plugins/) to get an always-on hook, three slash commands, and a runtime skill:

- UserPromptSubmit hook (hooks/hooks.json → hooks/user-prompt-submit.mjs) — injects the runtime contract (hooks/runtime-directive.md) on every prompt, unless the prompt is an explicit stand-down. This is the always-on invocation. - /simplicio <task> — run the next task through the Tuple-Space + Yool runtime. - /simplicio-install <target> — install the runtime contract into the current repo (claude-code, codex, cursor, copilot, gemini, all, …). - /simplicio-status on|off|<field>:on — toggle the opt-in status output. - simplicio-runtime skill — auto-activates in repos that vendor the spec.

Integration adapter

For orchestration repos that want the runtime without copying tuple boilerplate, use the Python adapter:

from examples.python.prompt_fanout import PromptFanout

fanout = PromptFanout(repo="my-service", authority="simplicio-sprint")
root, receipt = fanout.spawn_task(
    "review checkout edge cases",
    mapper_context={"target": "src/checkout.ts"},
    depth=2,
    branching=8,
)
fanout.record_tokens("analysis", prompt_tokens=1200, completion_tokens=300, cost_usd=0.02)
print(receipt.virtual_agents)
print(fanout.snapshot()["token_usage"])

simplicio-dev-cli can use the same adapter for internal verification reasoning when it already has structured mapper context.