Borrowing it
Nothing to install: this file belongs to katopz/katgpt-rs. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/katopz/katgpt-rs/develop/.agents/skills/research/SKILL.mdgit clone --depth 1 https://github.com/katopz/katgpt-rsWrote 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/skills/katopz/katgpt-rs/research)<a href="https://agentmods.dev/skills/katopz/katgpt-rs/research"><img src="https://agentmods.dev/badge/skills/katopz/katgpt-rs/research.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 | $0.00160 | $0.14655 |
| Opus 5 | $0.00080 | $0.07327 |
| Sonnet 5 | $0.00032 | $0.02931 |
| Haiku 4.5 | $0.00016 | $0.01465 |
Grade C, and why
research scanned grade C with 2 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 4d 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
5. **Cleanup is part of the task:** when the verdict/note/plan is committed, `rm -rf .raw/<repo>` (or the whole `.raw/`). A finished research task with a live `.raw/` entry is an unfinished task. If a session dies mid-ta Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| `katgpt-rs/crates/katgpt-dec/src/` | `operators.rs` (`exterior_derivative` d, `codifferential` δ, `hodge_laplacian` Δ), `hodge.rs` (`hodge_decompose`, `betti_numbers`, `harmonic_projector`), `flow.rs` (`DecFlowField`), How it starts
The opening of the file, as written. The whole thing — 358 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Workflow — Modelless Inference, Freeze/Thaw, Latent-to-Latent
This repo (katgpt-rs) + riir-ai (freeze/thaw runtime + adaptive NPCs + game systems) + riir-chain (neuro-symbolic chain transport, LatCal) + riir-neuron-db (NeuronShard, BLAKE3/Merkle, freeze/thaw envelope) ship runtime + latent-space operations. Training-method research lives in riir-train. If a paper's value is its training loop → riir-train (see §3.5 Path 0.5 — applicable training papers get a Plan, not a lazy redirect). If its value is a latent-space insight, a routing trick, a freeze/thaw pattern, a chain-commitment bridge, a neuron-shard primitive, or a modelless inference primitive → distill here.
When to use
Reading/fetching/summarizing ML/AI/systems papers · deciding which repo a paper belongs to · creating .research/ notes or .plans/ files · implementing modelless inference primitives · designing freeze/thaw cycles, adapter hot-swap, runtime adapter routing · designing latent-to-latent ops (dot-product projections, sigmoid gating, manifold geometry, spectral methods) · designing MMORPG-scale game AI (thousands of concurrent NPCs, 20Hz tick, fog-of-war, emergent social/economic behavior).
Do NOT activate for: pure refactor, bug fixes with no research angle, or ordinary feature work.
Repos
katgpt-rs/— public MIT engine. Generic modelless inference primitives. No game/chain/shard IP.riir-ai/— private game product. Freeze/thaw runtime, self-learn, game systems. Hosts the.docs/moat book.riir-chain/— private neuro-symbolic chain transport. LatCal,riir-chaind, economics, asset lifecycle,catchup/(Turso/libSQL, quorum). Re-exportsriir-neuron-dbunderneuron_dbfeature; canonical shard source isriir-neuron-db/.riir-neuron-db/— private leaf.NeuronShard(Pod, zero-copy mmap),ShardIndex(lock-free papaya), genericMerkleTree/MerkleProof,MerkleFrozenEnvelope, MAPE-K, Raven/δ-Mem consolidation, AnyRAG gateway, vibe KG triples, spectral init,ShardCompactor, dendritic LoRA branch. No chain dep — usable standalone.riir-train/— private training vault. As of 2026-08-06: actively pursued, not lazily redirected. Applicable training papers get a Plan inriir-train/.plans/per §3.5 Path 0.5.read_file riir-train/.docs/02_pipelines/training_data_pipeline.mdbefore any training-paper verdict.riir-game-sdk/— downstream consumer; rarely a distillation target. (riir-armageddon/sat here until it was retired 2026-09-02, owner act — the directory is gone; do not route to it.)
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago Changed · +50 lines scan A → C 03d9a347a5c2
- 8d ago First seen · 308 lines · 160 tokens per session scan A 239774a35038
research is a skill published in the GitHub repository katopz/katgpt-rs (98 stars, last pushed yesterday), licensed MIT. It adds 160 tokens to every session and 14,655 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it C with 2 findings (recursive force delete, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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