qiongli

<div align="center"> <h1>Qiongli (穷理)</h1> <p><strong>Reviewable academic workflows for Codex, Claude Code, Claude Desktop, Antigravity, and Hermes.</strong></p> <p>Plan the work, search the literature, draft and review manuscripts, run research code, and keep the evidence trail in one place.</p> <p> <a href="https://www.npmjs.com/package/qiongli"><img alt="npm latest version" src="https://img.shields.io/npm/v/qiongli/latest?style=flat-square&amp;logo=npm&amp;label=npm%20latest"></a> <a href="https://www.npmjs.com/package/qiongli?activeTab=versions"><img alt="npm next version" src="https://img.shields.io/npm/v/qiongli/next?style=flat-square&amp;logo=npm&amp;label=npm%20next&amp;color=cb3837"></a> <a href="https://pypi.org/project/qiongli/"><img alt="PyPI latest version" src="https://img.shields.io/pypi/v/qiongli?style=flat-square&amp;logo=pypi&amp;label=PyPI%20latest"></a> <a href="https://pypi.org/project/qiongli/1.5.0b8/"><img alt="PyPI beta version" src="https://img.shields.io/badge/PyPI%20beta-1.5.0b8-3775A9?style=flat-square&amp;logo=pypi&amp;logoColor=white"></a> </p> <p> <a href="packages/qiongli-literature-mcpb/README.md"><img alt="Qiongli Literature Provider MCPB version" src="https://img.shields.io/github/package-json/v/jxpeng98/qiongli?filename=packages/qiongli-literature-mcpb/package.json&amp;style=flat-square&amp;label=MCPB%20literature&amp;color=0f766e"></a> <a href="packages/qiongli-zotero-companion/README.md"><img alt="Qiongli Zotero Companion version" src="https://img.shields.io/github/package-json/v/jxpeng98/qiongli?filename=packages/qiongli-zotero-companion/package.json&amp;style=flat-square&amp;label=Zotero%20companion&amp;color=cc2936"></a> </p> <p> <a href="README_CN.md">中文 README</a> · <a href="docs/index.md">VitePress Docs</a> · <a href="docs/zh/index.md">中文文档</a> · <a href="docs/quickstart.md">Quick Start</a> · <a href="docs/guide/install.md">Install</a> · <a href="docs/reference/cli.md">CLI</a> </p> </div> ## At A Glance Qiongli is for research work that needs a paper trail. It turns a broad academic request into a task flow with Task IDs, quality gates, role handoffs, and standard outputs under `RESEARCH/[topic]/`. Instead of asking an agent to improvise a whole paper in one pass, you get smaller steps that are easier to inspect, revise, and trust. Use it when you need to: - **Choose a paper route:** empirical, qualitative, systematic review, RCT/preregistration, theory, or code-first methods. - **Make literature work auditable:** plan searches, record diagnostics, materialize search bundles, deduplicate results, and prepare screening or snowballing. - **Keep writing tied to evidence:** map claims to sources, check citation risk, plan figures and tables, review limitations, proofread, and prepare rebuttals. - **Treat research code as research evidence:** move through Stage-I `I5 -> I6 -> I7 -> I8` for specification, planning, execution, and review. - **Coordinate more than one agent:** use solo, duo, or triad modes with explicit handoffs, disagreement records, and verification status. The public name is **Qiongli**, from the Chinese `穷理`: to keep asking what principle, evidence, and limit sit underneath a question. The full methodology name is **Qiongli Zhengche** (`穷理证澈`): make evidence chains, citation risk, assumptions, and claim boundaries clear enough to audit. ## Start Here | Need | Best entry | |---|---| | You want the full docs experience | Open the [VitePress docs](docs/index.md), or run `npm run docs:dev` locally | | You prefer Chinese docs | Start with [中文 README](README_CN.md) or [中文文档](docs/zh/index.md) | | You want Qiongli in one client | Use the [Install Guide](docs/guide/install.md) | | You are choosing a paper workflow | Use [Task Recipes](docs/guide/task-recipes.md) | | You need scriptable installs or upgrades | Use the [CLI Reference](docs/reference/cli.md) | | You want to understand the architecture | Read [Architecture](docs/architecture.md) | ## Latest Stable Downloads Current stable release: [v1.6.0](https://github.com/jxpeng98/qiongli/releases/tag/v1.6.0). These direct links cover the common install paths; use the download guide for subject-specific Desktop ZIPs and maintainer artifacts. | Need | Link or command | |---|---| | npm CLI | [`qiongli@1.6.0`](https://www.npmjs.com/package/qiongli/v/1.6.0): `npm install -g qiongli@latest` | | PyPI CLI | [`qiongli 1.6.0`](https://pypi.org/project/qiongli/1.6.0/): `pipx install qiongli` | | Claude Desktop/Web core skill | [`qiongli-claude-desktop-skill-core-v1.6.0.zip`](https://github.com/jxpeng98/qiongli/releases/download/v1.6.0/qiongli-claude-desktop-skill-core-v1.6.0.zip) | | Claude Desktop literature MCPB | [`qiongli-literature-provider-0.1.4.mcpb`](https://github.com/jxpeng98/qiongli/releases/download/v1.6.0/qiongli-literature-provider-0.1.4.mcpb) | | Zotero Desktop companion | [`qiongli-zotero-companion-0.2.2.xpi`](https://github.com/jxpeng98/qiongli/releases/download/v1.6.0/qiongli-zotero-companion-0.2.2.xpi) | | All release assets | [Download guide](https://github.com/jxpeng98/qiongli/releases/download/v1.6.0/qiongli-downloads-v1.6.0.md) and [GitHub Release](https://github.com/jxpeng98/qiongli/releases/tag/v1.6.0) | ## How It Works ```mermaid flowchart TD A["Research request<br/>topic, paper type, client, constraints"] --> B["Qiongli entrypoint<br/>skill, plugin command, CLI, or MCP tool"] B --> C["Task contract<br/>Task ID, stage, role, expected outputs, evidence rules"] C --> D{"Smallest runtime<br/>that fits the job"} D --> E["Skill / plugin only<br/>workflow guidance, prompts, templates, subject overlays"] D --> F["Literature MCP<br/>OpenAlex, Semantic Scholar, Crossref, PubMed"] D --> G["Zotero companion<br/>local library search, import files, reference review tags"] D --> H["Full CLI / orchestrator<br/>doctor, task-plan, preview task-run, optional agent run"] E --> I["Route the stage<br/>A framing, B literature, C design, F writing, I code, H rebuttal..."] F --> J["Collect search evidence<br/>queries, provider status, diagnostics, dedup and screening readiness"] G --> K["Sync reference context<br/>local Zotero results, CSL/RIS/BibTeX exports, review queues"] H --> L["Run controlled execution<br/>controller, primary, reviewer, verifier, solo/duo/triad mode"] I --> M["Quality gates<br/>claim boundary, citation risk, method fit, code review, verification status"] J --> M K --> M L --> M M --> N["Write artifacts<br/>RESEARCH/[topic]/ context, search logs, plans, drafts, code, reviews"] N --> O{"Human or agent review"} O -->|revise| C O -->|accepted| P["Auditable package<br/>evidence trail, decisions, outputs, and release/submission materials"] ``` ## Current Structure Qiongli is split into four surfaces so you can install only what the task actually needs: | Surface | What it provides | Does it launch local agents? | |---|---|---| | Skill / plugin package | Agent instructions, workflow commands, templates, standards, subject overlays, and effective skill markdown | No | | Literature MCP runtime | Local literature/provider tools such as `qiongli_literature_search`, `qiongli_config_status`, `qiongli_configure_provider`, and `qiongli_save_provider_config` | No | | Full CLI MCP runtime | Python-backed MCP tools including `qiongli_orchestrator_route`, `qiongli_orchestrator_doctor`, `qiongli_task_plan`, and `qiongli_task_run` | Only when explicitly enabled | | Shell / Python orchestrator | `doctor`, validators, `task-plan`, `task-run`, `team-run`, and code-build routes | Yes, when runtime auth is configured | This separation matters most on Desktop. A manual Skill ZIP gives Claude Desktop/Web the Qiongli skill and subject overlays. A `.mcpb` install adds local literature-provider tools. The full local agent runtime stays in the CLI/MCP surface, where it can be configured and audited separately. For Codex marketplace plugins, the bundled literature MCP is launched from the plugin bundle. Configure provider keys through `qiongli_config_status` and the local `qiongli_configure_provider`, not by putting secrets into `.mcp.json` or plugin manifests. The same setup tool is exposed by the Claude Desktop MCPB, Claude Code plugin runtime, and full CLI MCP server for other local MCP clients. ## Installation Decision Guide Start with the smallest surface that matches the job. In Qiongli, "full workflow" means the complete methodology, workflows, templates, quality gates, skill registry, and subject overlays. It does not automatically mean local provider calls or local agent execution. | Install path | Use it when | Advantages | Trade-offs | |---|---|---|---| | **Codex marketplace plugin**: `codex plugin marketplace add jxpeng98/skillsplace --ref main` | You use Codex and want Qiongli as a native skill/plugin. | Installs the Qiongli skill, subject packages such as `qiongli-economics`, and the bundled zero-dependency literature MCP registration/runtime. No Python needed for skill use or bundled literature MCP. | Full Python-backed orchestrator MCP still requires npm/pipx/bootstrap `full` plus `qiongli mcp serve --transport stdio`. | | **Claude Code marketplace plugin**: `claude plugin marketplace add jxpeng98/skillsplace@main` | You use Claude Code and want the full Qiongli workflow from the marketplace. | Installs the full `subject/complete` workflow package for `qiongli` and subject entries such as `qiongli-economics@skillsplace`; includes slash workflow commands like `/paper`, `/lit-review`, and `/code-build`, plus the same zero-dependency Node literature MCP runtime as Codex for provider, search, and status tools. No Python needed for skill/command use or bundled literature MCP. | Full Python-backed tools such as `qiongli_orchestrator_route` and `qiongli_task_run` still require npm/pipx/bootstrap `full` plus `qiongli mcp serve --transport stdio`. | | **Claude Desktop/Web Skill ZIP** | You want Qiongli in Claude Desktop or Claude.ai without a code environment. | No terminal required. Good for skill-guided paper planning, writing, review, and focused subject packages. | Focused package kept under Desktop upload limits; skill-only, no secrets, no provider calls, no local agent execution. | | **Claude Desktop Literature MCPB**: `qiongli-literature-provider.mcpb` | Desktop needs local OpenAlex/Semantic Scholar search and provider key configuration. | No Python or npm install; pairs cleanly with the Desktop Skill ZIP. | Literature/provider tools only. It does not install Qiongli skills and does not launch orchestrator agents. | | **npm / npx**: `npm install -g qiongli` or `npx qiongli@latest ...` | You want scriptable installs, upgrades, and prebuilt complete/focused subject payloads through Node. | Good default for cross-client asset installation; no PyPI dependency for skill payloads. | Advanced bridge commands such as `setup`, `doctor`, `task-run`, and `mcp` need Python 3.12+ with `PyYAML`. | | **Bootstrap `partial`** | You want portable workflow assets and command discovery across clients without Python. | Simple shell/PowerShell path for skills and workflow discovery links. | No runtime validation, no Python bridge, no local orchestrator execution. | | **Bootstrap `full` / pipx / pip Python CLI** | You need `doctor`, validators, local `task-plan`, `task-run`, `team-run`, or full CLI MCP. | Most complete runtime surface; enables local checks and Python-backed orchestration. | Requires Python 3.12+, model CLIs in `PATH`, and runtime auth for actual agent execution. | Claude Code marketplace status: yes, Claude Code can install the full Qiongli methodology through Skillsplace for core and subject `complete` packages. Codex and Claude Code both install the skill/command package plus the bundled zero-dependency Node literature MCP runtime for provider, search, and status tools. The full Python-backed orchestration MCP remains separate and still requires npm/pipx/bootstrap `full` plus `qiongli mcp serve --transport stdio`. Prerelease testing uses the separate `qiongli-next` marketplace entry. It installs only the core Qiongli workflow for Codex and Claude Code, keeps the bundled literature MCP runtime, and pairs with `qiongli-next-claude-desktop-skill-core-<tag>.zip` plus `qiongli-literature-provider-<version>.mcpb` for Claude Desktop. CLI prerelease testing uses `npx qiongli@next ...`. Detailed install instructions live in [docs/guide/install.md](docs/guide/install.md). The quickest route from nothing installed is [docs/quickstart.md](docs/quickstart.md). ## Install Snippets Native plugin installs: ```bash # Codex codex plugin marketplace add jxpeng98/skillsplace --ref main codex plugin marketplace list # Claude Code claude plugin marketplace add jxpeng98/skillsplace@main claude plugin install qiongli@skillsplace claude plugin install qiongli-economics@skillsplace # Prerelease channel claude plugin install qiongli-next@skillsplace ``` npm / npx installs: ```bash npm install -g qiongli qiongli install --target all --project-dir "$PWD" qiongli install --subject economics --target all --project-dir "$PWD" qiongli install --subject accounting --target all --project-dir "$PWD" qiongli install --subject economics-accounting --target all --project-dir "$PWD" qiongli install --subject economics --coverage focused --target all --project-dir "$PWD" # Prerelease testing npx qiongli@next install --target all --project-dir "$PWD" npx qiongli@next check --json # Remove CLI-installed global assets before switching install channels qiongli remove --target all --dry-run qiongli remove --target all ``` Bootstrap installs: ```bash # Linux / macOS curl -fsSL https://raw.githubusercontent.com/jxpeng98/qiongli/main/scripts/bootstrap_qiongli.sh | bash -s -- --profile partial --project-dir "$PWD" --target all curl -fsSL https://raw.githubusercontent.com/jxpeng98/qiongli/main/scripts/bootstrap_qiongli.sh | bash -s -- --profile full --project-dir "$PWD" --target all ``` ```powershell # Windows PowerShell 7+ pwsh -ExecutionPolicy Bypass -File .\bootstrap_qiongli.ps1 -Profile partial -ProjectDir "$PWD" -Target all pwsh -ExecutionPolicy Bypass -File .\bootstrap_qiongli.ps1 -Profile full -ProjectDir "$PWD" -Target all ``` Use `partial` for skills and workflow discovery. Use `full` when you also need the shell CLI, `doctor`, validators, or local orchestrator execution. ## Recommended CLI Setup Wizard After npm, pipx, pip, or bootstrap installs, use the setup wizard when you want help choosing an install or upgrade path: ```bash qiongli setup qiongli setup --dry-run qiongli setup --project-dir "$PWD" --no-doctor qiongli install --target all --project-dir "$PWD" ``` The wizard covers `install` and `upgrade`, runtime surface (`cli`, `codex`, `claude-code`, `antigravity`, or `multi-platform`), subject, coverage, `--mode copy|link`, install scope, CLI directory / shell CLI location, `--overwrite` / `--no-overwrite`, optional provider config, and doctor verification unless `--no-doctor` is used. On npm installs, `qiongli setup` delegates to the bundled Python bridge and therefore requires Python 3.12+ plus `PyYAML`. If you only need Node-based asset installation, use explicit `qiongli install ...` commands. Provider keys entered through `qiongli setup` use the same provider config as `qiongli provider setup` and `qiongli provider doctor`. Secrets are stored in provider configuration outside generated research artifacts. Setup configures credentials and runs doctor/capability checks; it does not by itself guarantee external search results. ## Remove CLI-installed Assets Use `qiongli remove` (aliases: `qiongli uninstall`, `qiongli delete`) to remove CLI-installed global workflow assets and discovery links. It does not uninstall marketplace plugins such as `qiongli` or `qiongli-next`; remove those through the Codex, Claude Code, or Claude Desktop plugin manager that installed them. ## Manual Desktop And MCP Boundaries Claude Desktop / Claude.ai can use Qiongli without a terminal: 1. Download a release Skill ZIP such as `qiongli-claude-desktop-skill-core-<tag>.zip` or `qiongli-claude-desktop-skill-economics-<tag>.zip`. 2. Upload it through the Desktop/Web Skills flow. 3. Enable the uploaded `qiongli` skill. The Desktop/Web ZIP uses `coverage=focused` to stay under the current 180-file upload budget. It is subject-specialized, not lower quality. It preserves workflows, prompts, templates, standards, selected profiles, `skills-summary.md`, `skills-core.md`, and selected effective skill markdown generated with layered overlays. This Desktop skill ZIP is **skill-only**: it stores no secrets and does not execute provider calls. To add local Desktop literature tools, install the separate Qiongli Literature Provider `.mcpb`: ```text qiongli-literature-provider.mcpb ``` The MCPB runs a zero-dependency Node stdio server for OpenAlex, Semantic Scholar, Crossref, and PubMed search, provider status, and provider key saving. It supports provider setup through the local wizard, query variants, finance/economics deep-search routing, pagination, retry diagnostics, and limited citation/reference metadata expansion. Desktop users need `qiongli-literature-provider` MCPB or platform-native search before claiming `provider_connected`; otherwise record the run as `strategy_only` and treat platform search or a user-supplied corpus as the evidence source. Finance/economics data APIs such as FRED and SEC EDGAR belong in a separate data MCP surface; see [Finance/Economics Data MCP Boundary](docs/advanced/finance-econ-data-mcp.md). The MCPB does not launch orchestrator agents. To expose the full Python-backed agent runtime through MCP, install the npm, pipx/pip, or bootstrap `full` CLI runtime and configure: ```bash qiongli mcp serve --transport stdio qiongli mcp config example --target codex --json qiongli mcp config example --target claude-code --json qiongli mcp config example --target hermes --json ``` The full CLI MCP server exposes: - `qiongli_config_status`, `qiongli_save_provider_config`, and `qiongli_collect_evidence` - `qiongli_orchestrator_route` - `qiongli_orchestrator_doctor` - `qiongli_task_plan` - `qiongli_task_run` Use `qiongli_orchestrator_route` from Codex, Claude Code, Antigravity, or another local MCP client when a natural academic request might need multi-agent coordination, independent review, handoff, strict gates, or auditable task-run artifacts. It returns a preview-first `doctor -> task_plan -> task_run` sequence. `qiongli_task_run` defaults to preview mode. It launches local runtime agents only when the MCP caller explicitly sends JSON boolean `run_agents: true` and the local runtime passes `doctor`. Project-local guidance uses `.qiongli/local_guidance.md` and `.qiongli/trace/`, which the first non-`off` task run initializes if missing; formal task outputs still belong under `RESEARCH/[topic]/...`. See [Cross-Platform MCP Server](docs/advanced/cross-platform-mcp.md) and [MCP Providers Setup](docs/advanced/mcp-providers-setup.md). ## Subject Packages And Overlays The user-visible skill name is `qiongli`. The installed directory remains `qiongli-workflow` for compatibility with existing clients and release artifacts. Current official subjects: - `core` - `economics` - `accounting` - `business` - `finance` - `political-economy` - `geoeconomics` - `economics-accounting` Default install means `core/complete`. CLI/npm specialized installs default to `coverage=complete`, so `--subject economics` means the full framework plus economics specialization, not a reduced package. Use `--coverage focused` only for deliberate slim packages and Desktop/Web ZIP-equivalent packages. Subject specialization is layered: - `core` owns shared workflow contracts, generic skills, templates, standards, and quality gates. - Subject packages add discipline depth through selected profiles, append overlays, declared section replacements, and subject-specific skills. - Effective packages are generated from `skill_refs`, subject overlays, layered section overrides, and optional local custom overlays. - Generic skill source files are not duplicated. Public Desktop ZIP subjects in this phase are `core`, `economics`, `business`, `finance`, `political-economy`, `geoeconomics`, and `economics-accounting`; there is no standalone accounting Desktop ZIP yet. Local customization is available for source/Python workflows: ```bash qiongli customize --subject economics --name my-econ-lab --out ./qiongli-custom/econ-lab python3 scripts/materialize_subject_package.py --subject economics --custom-dir ./qiongli-custom/econ-lab --source . --out /tmp/qiongli-workflow ``` npm runtime installs use pre-generated payloads and do not accept runtime `--custom-dir` in this phase. See [Subject Packaging Model](docs/advanced/subject-packaging-model.md). ## Workflow Runtime Qiongli writes durable research artifacts under `RESEARCH/[topic]/`. The most common runtime checks are: ```bash python3 -m bridges.orchestrator doctor --cwd . python3 -m bridges.orchestrator task-plan --task-id F3 --paper-type empirical --topic ai-in-education --cwd . python3 -m bridges.orchestrator task-run --task-id F3 --paper-type empirical --topic ai-in-education --cwd . ``` Useful controls: - `--execution-mode solo|duo|triad` - `--controller codex|claude|antigravity` - `--primary`, `--reviewer`, and `--verifier` - `--solo-role-gates strict|standard|off` - `--mcp-strict` and `--skills-strict` - `--research-depth deep` - `--only-target <id>` Full functionality requires a real Python runtime plus model CLIs in `PATH`: `python3`, `codex`, `claude`, and `antigravity`. You also need runtime authentication. `codex` can run with `OPENAI_API_KEY` or an existing ChatGPT/Codex login, `claude` uses `ANTHROPIC_API_KEY` or an existing Claude Code login, and `antigravity` uses the local Antigravity CLI login/configuration. Without these runtime pieces, you can still install assets and use shell `qiongli check|upgrade|align`, but `doctor`, validators, tests, and full orchestrator execution will be partial or unavailable. ## Academic Idea Boundary Qiongli includes an Academic Idea Funnel and Academic Grill Loop before early-stage paper outputs. This is an academic adaptation of Matt Pocock's `grill-me` interaction pattern, not a generic grill-me clone. The adaptation turns one-question-at-a-time clarification into academic idea-discovery: claim strength, evidence threshold, rival explanations, feasibility, venue/reviewer risk, and the handoff into `context/boundary_review.md`. The funnel artifact is: ```text RESEARCH/[topic]/context/idea_funnel.md ``` The boundary artifact is: ```text RESEARCH/[topic]/context/boundary_review.md ``` Source credit: [Matt Pocock's skills repository](https://github.com/mattpocock/skills). ## Repository Map | Path | Role | |---|---| | `content/workflow/` | Portable skill package source: `SKILL.md`, workflows, references, agents, standards | | `content/distribution/` | Canonical plugin metadata for generated Codex/Claude Code payloads | | `content/skills/` | Canonical academic capability cards and registry | | `content/subjects/` | Subject catalog, overlays, subject-specific skills, and materialization rules | | `content/templates/` | Research artifact templates | | `content/standards/` | Canonical contracts including `standards/research-workflow-contract.yaml`, `standards/mcp-agent-capability-map.yaml`, schemas, and quality gates | | `packages/python-qiongli/` | Python CLI/runtime package and bridge modules | | `packages/npm-qiongli/` | npm installer, bundled payload, and Python bridge entry | | `packages/qiongli-literature-mcpb/` | Claude Desktop literature-provider MCPB package source | | `docs/` | User, advanced, reference, architecture, and maintainer documentation | | `tests/` | Contract, materialization, MCP, runtime, package, and orchestration tests | Generated outputs are intentionally not normal feature-review targets. Normal feature PRs should update canonical source, tests, and documentation only. Release automation performs staged materialization into temporary roots, including `plugins/qiongli/`, `plugins/qiongli-next/`, `packages/qiongli-plugin/`, and `packages/qiongli-next-plugin/`. When adding or deepening a subject, update `content/subjects/catalog.yaml`, subject overlays, subject-specific registry/markdown, selected profiles, eval fixtures, specialization audit expected terms, materializer tests, npm package contract tests against staged materialization, and release validation if the subject has a Desktop/Web artifact. For agent and skill collaboration details, see [Agent-Skill Collaboration](docs/advanced/agent-skill-collaboration.md). The legacy shell installer source remains available at `scripts/install_qiongli.sh`; most users should prefer the install paths in [docs/guide/install.md](docs/guide/install.md). ## Verification Common local checks: ```bash uv run python -m unittest tests.test_mcp_cli tests.test_mcp_tool_handlers tests.test_mcp_stdio_server -v uv run python -m unittest tests.test_orchestrator_workflows tests.test_controller_agnostic_orchestration -v uv run python scripts/validate_research_standard.py --strict npm test --prefix packages/npm-qiongli git diff --check ``` For runtime readiness: ```bash uv run python -m bridges.orchestrator doctor --cwd . ``` ## More Documentation - [Quick Start](docs/quickstart.md) - [Install Guide](docs/guide/install.md) - [Using Agent Skills](docs/guide/using-agent-skills.md) - [Task Recipes](docs/guide/task-recipes.md) - [Multi-Agent Runtime Guide](docs/guide/multi-agent.md) - [CLI Reference](docs/reference/cli.md) - [Architecture](docs/architecture.md) - [Cross-Platform MCP Server](docs/advanced/cross-platform-mcp.md) - [Plugin-First Architecture](docs/advanced/plugin-first-architecture.md) - [Controller Modes](docs/advanced/controller-modes.md) - [Subject Packaging Model](docs/advanced/subject-packaging-model.md) ## Design Lineage Qiongli borrows useful ideas from existing agent-workflow projects while adapting them to academic research: - [fengshao1227/ccg-workflow](https://github.com/fengshao1227/ccg-workflow): strict phase separation, spec -> plan -> execute -> review, and constrained execution. - [GuDaStudio/skills](https://github.com/GuDaStudio/skills): reusable Claude-oriented collaboration skill packaging. - [Matt Pocock's `grill-me` skill](https://github.com/mattpocock/skills/blob/main/skills/productivity/grill-me/SKILL.md): one-question-at-a-time clarification, adapted here into the Academic Idea Funnel and Academic Grill Loop for defensible scholarly idea formation. ## Community Credit Thanks to the [linux.do](https://linux.do/) community for being an open Chinese-language space for practical AI tooling, developer workflow, and local-first productivity discussions. Qiongli will be shared there as the workflow matures, both to reach more users and to collect direct feedback.