Prism-Shadow
Organization🐧 Harness for RSI. Let AI Build AI. Multi-Agent Auto-Dev Platform. Everything is Transparent.
Categories
Indexed Skills (33)
agenthub-models
Call model APIs through @prismshadow/agenthub — streaming text generation, image generation, speech synthesis, embeddings and the supported-model registry with one client.
agent-creation
Create or configure an Agent State from a user requirement by writing AGENTS.md, setting identity metadata, and installing only needed Skills.
penguin-cli
Manage model API keys, default models and per-agent vault secrets with the penguin CLI.
software-engineering
Complete software-engineering tasks — investigate and review code, implement bug fixes, features and refactors with minimal scope, validate changes, and report verified outcomes.
penguin-sdk
Use whenever the user wants to build an agent application — their own program with an embedded agent, such as an AI app, an agentic app or a RAG app. This is writing application code on the Penguin Harness SDK, not configuring an Agent State inside PenguinHarness. Covers self-contained projects, the createSession/run streaming loop with thinking and image messages, wiring the user's existing tools in as CLI commands, and a complete RAG recipe that ingests documents into a knowledge base and answers with citations behind a web UI.
remote-claude-code
Run Claude Code on a remote host over SSH — a persistent expect-driven login session, headless claude -p with the stdin fix, the interactive TUI inside a remote tmux driven by send-keys/capture-pane (one keystroke at a time, capture-verified; relayed user messages go through verbatim), and multi-turn continuity via --session-id/--resume or stream-json; hosts and credentials are placeholders resolved at runtime from the user or the vault, never hardcoded.
ollama
Deploy and serve local models with Ollama — pull and run them, then expose the OpenAI-compatible endpoint to apps and agents.
vllm
Deploy and serve LLMs with vLLM behind an OpenAI-compatible endpoint, with tool calling enabled for agent workloads.
agent-initialization
Initialize an Agent's settings from a user requirement by writing AGENTS.md, setting identity metadata, and installing only needed Skills.
penguin-orchestration
Drive PenguinHarness itself from a shell — list and create agents and sessions, send and steer messages mid-flight, and query costs and scheduled tasks via the penguin CLI over the local server.
agent-evaluation
Run one specified Test Agent on one specified Benchmark Case exactly once, privately score that execution, and return one protocol result.
benchmark-design
Design and calibrate a multi-Case capability Benchmark and establish a traceable Formal Baseline.
data-analysis
Complete data-analysis tasks with bounded inspection, correct data semantics, native artifact handling, complete delivery, and risk-based verification.
skill-porting
Install skills from external ecosystems into this agent's agent_state/skills/ — resolve Claude Code plugin marketplaces, the Codex plugin repo, skills.sh registry names, GitHub repos, or local folders to their skill directories, review every file, and normalize SKILL.md frontmatter to the Penguin format.
humanizer
Rewrite or edit prose in any language so it reads like edited human writing in the register of books, newspapers and encyclopedias rather than default AI output. A small drafting core — vary every pattern, build density from anchored facts in whole grammar, cap the quotables, put a real writer with real material behind the text, let structure serve content, write each language from inside its idiom and typography, verify what you assert, and aim for the natural distribution of edited prose rather than a perfect scorecard — backed by a three-layer tell catalog, per-language cue files for six languages and a seven-round measured case study shipped as reference files for the diagnostic census.
web-design
Penguin visual language for generated web pages and app UIs — GitHub-style simplicity with a single blue accent, light and pure-black dark themes, design tokens, component and chat-interface recipes, plus an opt-in warm paper editorial theme.
firecrawl
Search the web and scrape pages into clean markdown with the Firecrawl API — query-based discovery, single-URL extraction including public PDFs, driven by curl with a vault-stored API key.
llamafactory
Fine-tune LLMs with LlamaFactory — register datasets, train via YAML configs, merge LoRA adapters and serve the result.
penguin-harness-dev
Use when developing PenguinHarness itself — changing packages/{core,server,web,cli,desktop,landing,docs,skills}, the built-in model catalog, the installers or the release workflow; writing or auditing changelog entries; writing a blog post or capturing release screenshots; running the test suite here or, when asked, on another machine; reading a prompt-cache regression out of the core suites; deciding what to do about data already on disk; or auditing prose that reads like a leaked authoring session. Covers the two-repo symlink layout, the CI-parity verification chain, the record-and-ship contract, where blog media is hosted, and the seams that are intentional.
penguin-harness-frontend
Use when changing the PenguinHarness Web App (`packages/web`) — adding or restyling any UI, picking a status colour, adding an icon, laying out a row or a form field, writing user-facing copy, or building a popup. Covers the semantic tone tokens, the icon size/stroke/gap scale, the semantic-versus-formatting rule for explanatory text, the two-dictionary i18n contract, and the portal-panel pattern with its Esc and scroll caveats.
penguin-harness-manual-test
Use when standing PenguinHarness up to try a change by hand — launching the Web App, the desktop shell, the landing page or the docs site to click through it, screenshot it, or reproduce a report. Covers the four dev entry points and their ports, which data root each writes to, and the four ways a healthy setup looks broken.
penguin-config
Manage model API keys, default models and per-agent vault secrets with the penguin CLI.
unified-llm-api
Call model APIs through @prismshadow/agenthub — streaming text generation, image generation, speech synthesis, embeddings and the supported-model registry with one client.
company-ceo
Run a PenguinHarness organization as its CEO — turn the mission into a ticket tree, hire HR and finance first, partition the shared workspace, schedule the calendar, open a channel per stream, review tickets and report to the board in the all-hands channel.
company-employee
The protocol every employee of a PenguinHarness organization follows — read the handbook first, act on [org_trigger] work runs, schedule tickets from the desk session and do the work in ticket sessions, block instead of idling, ask the board in the all-hands channel before anything heavy, costly, irreversible or outside the workspace, keep channel and budget discipline, and drive it all with penguin org.
company-finance
Run finance for a PenguinHarness organization — set and adjust monthly budgets along the reporting line, audit spend daily with penguin org finance and penguin cost, explain budget alerts in the all-hands channel with savings proposals, and handle budget pauses.
company-hr
Run HR for a PenguinHarness organization — guarantee every employee has an enabled calendar event, hire and offboard employees (channels included), evaluate and improve them (with the agent-tuning plugin's agent-optimization skill), and keep the handbook's role conventions current.
company-mirror
Run a PenguinHarness organization that mirrors a real company — one digital twin per real colleague, each twin's desk bound to that colleague's chat bot, twins answering what the handbook already knows and relaying the rest to one another instead of hiring, scheduling and filing tickets.
company-setup
Create a company-mode organization together with the user — collect its id, name, mission, shared workspace, model and CEO budget one question at a time, confirm the whole thing in one summary, then run penguin org create and hand the user over to company mode.
company-research
Run experiments and papers in a PenguinHarness research organization — fix the harness and the metric first, run an autoresearch-style loop (one editable surface, the same time budget per experiment, a results log, keep only improvements) inside a resource envelope the board approved in the all-hands channel, and put every claim through adversarial review by a reviewer who is not its author.
agenthub-dev
Fixed workflow for developing AgentHub itself — adding or updating model support. Use when asked to support a new model or protocol version in this repository, sync llmsdk_docs, or implement a provider client. Covers doc syncing, live API capture, paired Python/TypeScript implementation, and model-scoped e2e testing.
agenthub-python
Guidance for using the AgentHub Python SDK (`agenthub-python`). Use when developing agents that call different LLM APIs, need a unified interface for LLM providers, mention AgentHub, request `agenthub-python`, or already import it.
agenthub-typescript
Guidance for using the AgentHub TypeScript SDK (`@prismshadow/agenthub`). Use when developing agents that call different LLM APIs, need a unified interface for LLM providers, mention AgentHub, request `@prismshadow/agenthub`, or already import it.
Bio shown is the top-scored skill's repo description as a fallback — real GitHub bios land in a future update.