Synthesize stored papers into a research landscape — grouped directions with evidence claims — and import it for validation. Use after papers are stored, when the user wants directions, themes, or a map of the literature.
Scanned 9/2/2026
Install to Claude Code
npx -y skills add forger-labs-hq/researchforge --skill researchforge-landscape --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: researchforge-landscape
description: Synthesize stored papers into a research landscape — grouped directions with evidence claims — and import it for validation. Use after papers are stored, when the user wants directions, themes, or a map of the literature.
---
# Build the research landscape
This is a handshake: the engine exports structured context, you write the
synthesis as YAML, the engine validates it field-by-field before anything is
stored. Your synthesis must be grounded in the exported papers only.
## 1. Export context
```bash
researchforge research context --json
```
This writes `.researchforge/synthesis/context.json`: the objective, the
stored papers (id, title, abstract, ranking signals), and — critically — the
embedded JSON Schema the artifact must conform to. Read the whole file.
## 2. Write the landscape
Write `.researchforge/synthesis/landscape.yaml` conforming exactly to the
embedded schema. Requirements the validator will enforce:
- every `cited_paper_ids` entry must be a stored paper id from the context;
- every direction needs evidence claims that distinguish **claims made by a
paper** from **your interpretation** (`evidence_type`), each with an
`extraction_confidence`;
- do not cite papers for statements their abstracts cannot support.
## 3. Import (engine validates)
```bash
researchforge research landscape --import .researchforge/synthesis/landscape.yaml --json
```
If validation fails, the `--json` error payload lists field-level errors
(path, message, offending value). Re-read the error payload, fix exactly
those fields in the YAML, and re-import. Never bypass validation by editing
stored files directly.
Next: the researchforge-hypotheses skill turns directions into testable
hypotheses.
## Rules
- The Python engine is the boundary: never work around a validation error, a
protected path, or an approval gate — fix the artifact or ask the user.
- Approvals belong to the user: never pass `--yes` or type a confirmation
unless the user explicitly approved that step in this conversation.
- Ground every summary in stored data: quote only numbers returned by
`--json` output or files under `.researchforge/` — never invent metrics.
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