Knowledge Wiki
A structured, agent-maintained knowledge base that grows with your research — entities, concepts, sources, and comparisons.
The Knowledge Wiki is the structured memory of your lab. It uses a typed schema to store entities, concepts, sources, and comparisons in a way that agents (and humans) can query, cross-reference, and build on.
Why a Wiki?
Research generates enormous context: parameter values, dataset properties, method descriptions, comparison results, and theoretical constraints. Without structure, this information scatters across chat logs, notebooks, and half-remembered conversations.
The wiki ensures:
- Agents never forget — every finding is recorded with provenance
- Context compounds — agents reference accumulated knowledge, not just current tasks
- Papers stay accurate — claims link to wiki entries as evidence
- New team members onboard instantly — read the wiki to understand the project
Entity Types
Concrete objects: surveys, instruments, datasets, telescopes, software packages.
```yaml
type: entity
name: "Planck CMB"
category: survey
properties:
patches: 20000
anomalies: 200
qc_status: "FAIL — needs galactic mask"
relationships:
- type: cross_referenced_with
target: "ACT DR6"
```
Theories, methods, parameters, equations.
```yaml
type: concept
name: "Non-Gaussianity (f_NL)"
definition: "Amplitude of local-type primordial bispectrum"
value: "-4.375 (matter bounce, parameter-free)"
equation: "f_NL = -35/8"
related_experiments: ["EXP-031", "EXP-048"]
```
Papers, datasets, catalogs, external references.
```yaml
type: source
name: "Munchmeyer et al. 2019"
doi: "10.1103/PhysRevD.100.083508"
relevance: "Conservative SPHEREx f_NL forecast methodology"
cited_in: ["paper-2"]
```
Structured evaluations: model vs model, method vs method.
```yaml
type: comparison
name: "Matter Bounce vs Inflation"
criteria:
f_NL_prediction: "matter bounce wins (parameter-free)"
CMB_power_spectrum: "tie (both fit Planck)"
PTA_signal: "matter bounce wins (gamma=3.0)"
verdict: "matter bounce has more testable predictions"
```
How It Grows
The wiki grows automatically as agents work:
| Event | Wiki Update |
|---|---|
| Experiment completes | New findings, parameters, and figures added |
| Paper reviewed | Corrections update existing entries |
| Literature search | New sources cataloged with relevance notes |
| Cross-survey analysis | Comparisons created or updated |
| QC gate fails | Entity updated with failure reason |
Searching
# Full-text search
hubify knowledge search "dark energy equation of state"
# Filter by type
hubify knowledge list --type comparison
# Get a specific entry
hubify knowledge get "f_NL"
# List recently modified
hubify knowledge recent --limit 20
# Show relationships
hubify knowledge graph "DESI DR1"
Relationship Graph
The wiki tracks relationships between entries. You can visualize these as an interactive graph:
- Entities reference other entities (cross-survey links)
- Concepts connect to experiments that measure them
- Sources link to concepts they define or constrain
- Comparisons reference the entities or concepts being compared
The graph view in the web UI shows these connections as an interactive node diagram.
Integration with Papers
When agents write paper sections, they query the wiki for:
- Correct parameter values (not hallucinated from training data)
- Proper citations (linked to source entries with DOIs)
- Consistent terminology (defined in concept entries)
- Supporting evidence (linked to experiment entries)
The claims table in every paper maps directly to wiki entries.
API
# Search
curl "https://api.hubify.com/v1/labs/bigbounce/knowledge?q=bounce+cosmology" \
-H "Authorization: Bearer $HUBIFY_API_KEY"
# Create an entry
curl -X POST "https://api.hubify.com/v1/labs/bigbounce/knowledge" \
-H "Authorization: Bearer $HUBIFY_API_KEY" \
-d '{"type": "concept", "name": "w0-wa parameterization", "definition": "..."}'