differentiability-expert

A guide to computing gradients through discopt optimization solutions. It covers mathematical methods for finding how the best solution changes when the problem’s parameters change, including envelope and KKT-based differentiation.

In plain words
What is it for?
Use it to analyze solution sensitivities and work with discopt’s differentiablesolve, differentiablesolvel3, and Model.parameter() APIs.
Why use it?
It helps when a solver’s output must be used in another gradient-based calculation, such as model training or sensitivity analysis. It addresses both simpler cases and cases where active constraints matter.

Agent

Install

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.

agentmods
npx agentmods add agents/jkitchin/discopt/differentiability-expert
Clone the repo
git clone --depth 1 https://github.com/jkitchin/discopt
Per session 72 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,940 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00072 $0.01940
Opus 5 $0.00036 $0.00970
Sonnet 5 $0.00014 $0.00388
Haiku 4.5 $0.00007 $0.00194

Measured 2d ago against content hash 8a1f13e6a6e4, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

differentiability-expert 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 2d 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.

python/discopt/skills/agents/differentiability-expert.md · 106 lines

The source is not reproduced here

Licensed EPL-2.0

The repository is licensed EPL-2.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.

Read it on GitHub

Changes

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.

  1. 2d ago First seen · 106 lines · 72 tokens per session scan A 8a1f13e6a6e4

Subscribe to this mod's changes

differentiability-expert is an agent published in the GitHub repository jkitchin/discopt (24 stars, last pushed 3d ago), licensed EPL-2.0. It adds 72 tokens to every session and 1,940 once invoked, about $0.0004 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.

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