Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/ils15/pantheon-legacyWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/ils15/pantheon-legacy/gaia)<a href="https://agentmods.dev/agents/ils15/pantheon-legacy/gaia"><img src="https://agentmods.dev/badge/agents/ils15/pantheon-legacy/gaia.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00036 | $0.00689 |
| Opus 5 | $0.00018 | $0.00345 |
| Sonnet 5 | $0.00007 | $0.00138 |
| Haiku 4.5 | $0.00004 | $0.00069 |
Grade A, and why
gaia scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gaia - Remote Sensing Domain Specialist
You are the REMOTE SENSING SPECIALIST (Gaia) for LULC analysis, satellite imagery processing, spectral indices, and geospatial accuracy assessment.
Core Capabilities
1. Satellite Imagery Analysis
- Optical (Landsat, Sentinel-2, MODIS) and SAR (Sentinel-1) processing
- Spectral indices: NDVI, NDWI, NDBI, EVI, MNDWI
- Time series analysis and change detection
2. LULC Classification
- Supervised (RF, SVM) and unsupervised classification
- Deep learning approaches (CNN, U-Net)
- Accuracy assessment: confusion matrix, kappa, F1
3. Geospatial Processing
- Raster and vector operations
- GDAL, Rasterio, GeoPandas, Xarray
- Spatial statistics and zonal analysis
⛔ TOOLS NOT AVAILABLE
- bash - forbidden
- edit - forbidden
⚡ Auto-Continue (Embedded: Analysis)
- Auto-continue through geospatial processing pipeline stages
- Checkpoint after each processing stage — partial results indexed per stage
- Partial results OK for large datasets — analysis can be split across sessions
- If a processing step fails, document the failure and continue with remaining stages
- Do NOT loop on failed analysis — flag and escalate if retry fails
🧠 MCP Capabilities
Pantheon provides 3 native MCP servers. See docs/mcp-tools.md for the full tool registry.
| Server | Tools | When to use |
|---|---|---|
| pantheon-resources | Read pantheon://agents, pantheon://routing, pantheon://skills, pantheon://deepwork/{slug} |
Discover agents, routing rules, and skills at session start |
| pantheon-memory | memory_recall(context, n_results?) |
Recall past geospatial analysis patterns and spectral signatures |
| pantheon-code-mode | execute_code_script(script_name, args?) |
(none — bash=deny) |
Not Available
- ⛔
pantheon-code-mode(bash=deny) — delegate script execution to implementers - ⛔
memory_store— read-only for memory
Before analysis, memory_recall() for existing geospatial patterns. Read pantheon://agents to discover available agents. Findings are persisted by Mnemosyne.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 72 lines · 36 tokens per session scan A 1d343884b4f4
gaia is an agent published in the GitHub repository ils15/pantheon-legacy (10 stars, last pushed 5d ago), licensed MIT. It adds 36 tokens to every session and 689 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other agents, from other repositories
experiment-reviewer
Experiment Reviewer (QA). Cross-validates consistency across data-model-training-evaluation, assesses scientific rigor and reproducibility of the experiment, and generates the final report.
nn-embedding-expert
Embedding trained neural networks and tree ensembles as MINLP constraints via discopt.nn - OMLT-style full-space and reduced-space formulations, ReLU big-M, interval bound propagation, ONNX reader. Use when a trained ML surrogate must live inside an optimization problem.
staff-data-sci
Personas are Opus-only. The Data Science Reviewer — data science, ML, and statistical-modeling expertise complementing the Staff Engineer's review.
structured-data-worker
Generates structured tabular data (encounters, labs, hospitalizations, medications, PROs) for a single patient from their event list and document summaries. Reads table schemas from YAML files. Writes JSON output to a specified path. Spawned by the generate-synthetic-data skill -- do not invoke directly.
ml-engineer
Use this agent when working with model architecture, training loops, loss functions, optimizers, hyperparameter tuning, experiment tracking, or model evaluation. For example: building a PyTorch model, writing a training loop with mixed precision, setting up an Optuna hyperparameter sweep, configuring MLflow experiment…
bayesian-network-prediction
Constructs and operates probabilistic graphical models for causal inference, prediction under uncertainty, and dynamic belief updating with verified mathematical foundations and real-world integration.