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.
npx agentmods add agents/kinncj/maple/data-sciencegit clone --depth 1 https://github.com/kinncj/MAPLEWhat 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 | $0.00020 | $0.00176 |
| Opus 5 | $0.00010 | $0.00088 |
| Sonnet 5 | $0.00004 | $0.00035 |
| Haiku 4.5 | $0.00002 | $0.00018 |
Grade A, and why
data-science 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 yesterday.
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.
The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- yesterday First seen · 27 lines · 20 tokens per session scan A 7e9bc3269922
data-science is an agent published in the GitHub repository kinncj/MAPLE (23 stars, last pushed 1mo ago), licensed AGPL-3.0. It adds 20 tokens to every session and 176 once invoked, about $0.0001 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 2026-08-30.
Other agents, from other repositories
validator
Validate molecular identifiers (SMILES strings, nucleotide sequences, amino acid sequences, CAS numbers) found in epistract extraction results. Uses RDKit for chemistry and Biopython for sequences. Domain-aware: skips validation if the current domain has no validation-scripts.
gpd-research-synthesizer
Synthesizes research outputs from parallel researcher agents into SUMMARY.md. Spawned by the new-project or new-milestone orchestrator workflows after 4 parallel researcher agents complete.
ma-numerics-consultant
Engage when the task turns on a number that must be right: evaluate a formula to a value, independently reproduce a claimed number from its inputs, an order-of-magnitude or ratio check (Γ/M, a suppression), a unit conversion, uncertainty propagation — or a load-bearing constant (mass, coupling, PDG value) about to be…
gpd-explainer
Explains a physics concept, method, notation, or paper rigorously in project context, with scoped literature references the user can open. Spawned by the explain workflow.
physics-expert
Particle physics reasoning — e.g., theory, phenomenology, simulation setup validation.
tracelens_analyst
Generate a TraceLens performance report for the rank-0 PyTorch trace and produce structured claims.json mapped to the bottleneck taxonomy.