# Lc New

> Use this skill whenever the user starts a new ASTRA analysis from a research question — scoping the question, structuring inputs and outputs, identifying decisions through literature, and landing astra.yaml + project CLAUDE.md. Triggers on verbs (`new`, `start`, `scope`) combined with nouns (`analysis`, `project`, `question`, `research`) — e.g. "new analysis", "start project", "scope research que…

- **Type:** Skill
- **Install:** `agentstack add skill-lightconeresearch-lightcone-cli-lc-new`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [LightconeResearch](https://agentstack.voostack.com/s/lightconeresearch)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** BSD-3-Clause
- **Upstream author:** [LightconeResearch](https://github.com/LightconeResearch)
- **Source:** https://github.com/LightconeResearch/lightcone-cli/tree/main/claude/lightcone/skills/lc-new
- **Website:** https://docs.lightconeresearch.org/

## Install

```sh
agentstack add skill-lightconeresearch-lightcone-cli-lc-new
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# /lc-new

Create a new ASTRA analysis project through conversation. Build the spec iteratively -- write to `astra.yaml` after each phase so the user sees progress. Literature search and decision identification happen in distinct phases -- talk first, then extract papers, then identify decisions informed by both conversation and literature.

## Setup

1. Read `astra.yaml` if it exists (to understand context or avoid overwriting)
2. Note the analysis directory for later

---

## Phase 1: Research Question

Stage banner: RESEARCH QUESTION

> "What are you trying to learn? Describe the question in your own words."

Then sharpen:
- "What would a clear answer look like?" (sharpens the description)
- "Why does this matter?" (context for decisions)

**Update astra.yaml** — set `name`. (`astra init` scaffolded a placeholder example decision/input/output plus a TODO `description`; the placeholder structure is replaced in Phases 2–3, and the `description` is filled in Finalize once structure has settled — written too early it goes stale.)

---

## Phase 2: Analysis Structure

Stage banner: ANALYSIS STRUCTURE

> "Walk me through your analysis step by step. What goes in, what comes out at the end?"

**Guidance on sub-analyses:** Analyses should only be split into multiple sub-analyses if each sub analysis genuinely has materially different inputs and outputs, and if the scope may be too broad if there is just one analysis; we overall want a sub-analysis to feel like it should genuinely be a self-contained product. For example, training + evaluation would typically be one analysis, because the product would be the trained and validated neural network estimator. When in doubt, opt for a single analysis at this stage. If it does need to be multi-stage, ask the user for confirmation and how to split it. For multi-stage analyses, make sure you confirm stage boundaries. Invoke `/astra` for YAML structure and sub-analysis guidance.

**One output per output.** Each output should be a single metric, a single plot, or a single artifact. Do not bundle multiple metrics into one output (e.g., "performance_metrics" containing accuracy, F1, and AUC). Each of those is its own output. Same for plots -- one figure per output.

**Update astra.yaml** with `inputs` and `outputs` (extending the spec from Phase 1).

---

## Phase 3: Deep Dive

Stage banner: DEEP DIVE -- [SECTION NAME]

Ask the user if they want to do a literature deep dive for this section. If not, skip straight to decision identification.

### Paper Collection

Ask if the user has specific papers they want to look into. Also search with WebSearch for highly relevant papers -- keep it limited, only papers that directly bear on the analysis. Use AskUserQuestion to present the list with a one-line description of each paper and why it's relevant. The user can check off which ones to extract and add any others.

### Extraction

For each approved paper: `astra paper add `, `astra paper path `, then spawn one `lc-extractor` agent per paper. The agent definition already contains extraction instructions, output format, and verification logic -- you just fill in the paper-specific context.

**Spawning each agent:** Use `Agent(subagent_type="lc-extractor", prompt="...")`. In the prompt, provide:
- **Analysis context**: the analysis description and decisions this paper might inform
- **Paper details**: DOI, version (arXiv only), PDF path (from `astra paper path`)
- **Target decisions**: each decision ID, label, and options with descriptions
- **Timestamp**: current time in ISO 8601

Spawn all in a single message (parallel). Show progress as results come in:

```
  ✓ Ba et al. 2016 -- 3 prior insights
  ○ Wu & He 2018 (reading...)
```

Write extracted prior insights to astra.yaml immediately. Synthesize them by topic for the user.

### Decision Identification

Use the conversation and literature to identify decisions. Apply the decision criteria from `/astra` (Decisions section):

- What could be done differently and still be defensible?
- Where did papers disagree or compare alternatives?
- Where did the user express uncertainty?

Write candidate decisions to astra.yaml as a batch for user review. Keep chat output concise (summary + decision IDs), and avoid dumping full decision details in chat.

**Probe for blind spots** -- analysts over-focus on methods and neglect data handling. Probe 1-3 areas: data exclusion, variable operationalization, inference criteria.

### Decision Review

During review, confirm or set each decision's `default`, keep option structure and evidence links, and remove any decisions the user rejects.

---

## Checkpoint

> "Anything else that should inform this analysis?"

Review the spec with the user. Update astra.yaml with any additions.

---

## Finalize

Stage banner: FINALIZING

### Validate

1. `astra validate astra.yaml` -- fix errors, iterate until clean
2. If prior insights exist: `astra validate astra.yaml --verify-evidence`

### Generate Baseline Universe

```bash
astra universe generate -n baseline
```

Generate only `baseline` unless the user explicitly asks for additional universes.

### Populate Description

Replace the TODO `description:` in `astra.yaml` with a short one-or-two-paragraph orientation now that structure is stable — what the analysis is and how its pieces fit together. Keep it brief; per-element prose lives on each Input/Output/Decision/Option via `description`/`rationale`. 

### Populate CLAUDE.md

Read the existing `CLAUDE.md` (created by `lc init`). Fill the `## Project Notes` section per the inline guidance there — context from the conversation that's not in `astra.yaml` and would be lost after `/clear`. The spec is the source of truth for structure, decisions, and evidence.

### Review with User

> "Anything you'd like to change? Otherwise the specification is ready."

If edits requested, apply, re-validate, and update CLAUDE.md.

---

## Done

Stage banner: SPECIFICATION COMPLETE

Show summary table:

```
| Section       | Decisions | Outputs | Prior Insights |
|---------------|-----------|---------|----------|
| (top-level)   | 3         | 2       | 5        |
| sub_analysis  | ...       | ...     | ...      |
```

Then tell the user the spec is ready and they can begin implementation. Recommend running `/clear` first — the scoping conversation consumes significant context, and everything needed to continue is captured in `astra.yaml` and `CLAUDE.md`.

---

## Restrictions

**You are a specification agent, not an implementation agent.**

You MUST NOT write Python, R, or other implementation code.

You MUST ONLY create/modify: `astra.yaml`, `universes/*.yaml`, `CLAUDE.md` (Finalize only).

You MUST NOT fabricate quotes -- all evidence must pass `astra validate --verify-evidence`.

You MUST spawn `lc-extractor` agents for paper processing. One paper per agent. Never read a PDF in the main agent context.

---

## Anti-Patterns

- **Waiting to write** -- Update astra.yaml after each decision crystallizes, not in bulk at the end
- **Accepting vague goals** -- "Analyze this data" is not a research question; push back
- **Method-only decisions** -- Actively probe for data handling and exclusion criteria, not just method choices
- **Literature as afterthought** -- Do not defer all literature to the end. Collect paper candidates during conversation (Phases 1-2) and extract them before identifying decisions (Extraction before Decision Identification in Phase 3)
- **Too many papers** -- ~2 papers per topic area, max 10 per section; do not try to be exhaustive
- **Background interruptions** -- Never spawn search or extraction subagents during conversation phases. Collect candidates first, then process them during Phase 3 Extraction
- **Reading PDFs in main context** -- Always delegate to subagents; PDFs consume too much context
- **Chat dump of decisions** -- Do not dump full candidate decision content in chat; write decisions to astra.yaml for review
- **Skipping verification** -- If quotes were extracted, always run `astra validate --verify-evidence`

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [LightconeResearch](https://github.com/LightconeResearch)
- **Source:** [LightconeResearch/lightcone-cli](https://github.com/LightconeResearch/lightcone-cli)
- **License:** BSD-3-Clause
- **Homepage:** https://docs.lightconeresearch.org/

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-lightconeresearch-lightcone-cli-lc-new
- Seller: https://agentstack.voostack.com/s/lightconeresearch
- Browse the marketplace: https://agentstack.voostack.com/browse

---
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