Evograph

Organization

skillberry-ai

evograph

Deprecated agent-mode algorithm (evo-graph port): a weakness-graph search that dispatched one solver agent per failure cluster and reverted a whole round on regression. Do not start new runs with it — its per-weakness fan-out is already `agent-optimize`'s sibling fan-out, done behind the honest val significance gate that evograph never applied, and everything else it did (failure clustering, rejected-edit memory, budget-aware fan-out, free-text stop condition) lives in `agent-optimize` + `phases/diagnose`. Use when reading or repairing an existing evograph run dir, or when writing the run-dir `wiki/` format the dashboard's Weakness-graph tab reads — and to see what to select instead: `agent-optimize` for agent-mode search, `hill-climb`, `gepa`, or `skillopt` for a deterministic loop.

Overview

Publisher	skillberry-ai
Repository	`cap-evolve`
Skill name	`evograph`
Stars	67
Forks	18
Bundled files	9
License	Apache-2.0
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
9 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by skillberry-ai on GitHub. Read the source before you install it.

Installation

Install the Evograph AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/skillberry-ai/cap-evolve/tree/main/skills/algorithms/evograph
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/skillberry-ai/cap-evolve.git /tmp/cap-evolve
mkdir -p .claude/skills
cp -r /tmp/cap-evolve/skills/algorithms/evograph .claude/skills/evograph

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Evograph in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Evograph on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Evograph is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

evograph — DEPRECATED

Do not select algorithm_skill: evograph for a new run. Use agent-optimize (agent mode) or hill-climb / gepa / skillopt (deterministic) with memory_skill: wiki if you want THIS run's weakness-graph format. This file stays so an existing evograph run dir is still readable.

Why it is deprecated

evograph advertised one distinctive capability — a collaborative weakness graph with one solver agent per weakness, merged into a shared candidate each round. Measured against its four siblings, that capability is not distinctive and the part that was distinctive was a defect:

The fan-out already exists, gated properly. agent-optimize fans out N sibling candidates from the same parent, one diagnosed failure cluster each, every sibling in its own working copy (a git worktree when the capability is in git), gated one at a time with a re-gate after each accept so several fixes accumulate into one lineage honestly. That is evograph's round, minus the flaws below. The clustering itself is phases/diagnose's job in both cases.
Acceptance was never held out. evograph kept a merge on a raw delta over a frozen 3-task subset of train, self-reported by the solver subagent that made the edit — no val split, no standard error, no Δ > k·SE. Between baseline and finalize an evograph run took no held-out measurement at all, so the sealed test number was the first honest signal anyone saw. Whole-round revert existed only as a one-round-late substitute for the gate it lacked; wire the real gate and there is nothing left for it to catch.
What remains unique is an output format, not a search strategy. The run-dir wiki/ is genuinely useful, but the dashboard renders the Weakness-graph tab from wiki/ presence alone, for any algorithm that writes the format (core/cap_evolve/dashboard.py). An output contract does not earn a second agent-mode algorithm that users must choose between — it has since moved to skills/memory/wiki/SKILL.md, a standalone memory_skill any algorithm can select (#400, #404), so it no longer needs evograph to stay alive.

There is no deterministic engine

There never was one, and scripts/run.py is a tombstone, not a stub: invoked deterministically it exits 2 with an agent-mode only payload rather than faking a loop. So evograph is also the one algorithm that could not be routed through the shared per-iteration record — see the hill-climb skill's references/run-step.md, which owns the shared iteration mechanics (parent selection, val gate, commit, iteration record) every other algorithm routes through. Read it if you are reconstructing what an evograph round should have done.

Reading an existing evograph run

The run dir is authoritative. <run_dir>/wiki/ holds the weakness nodes, solution cards and per-round results; <run_dir>/runs/round-<N>/agents/<slug>.log holds solver progress. The formats are in references/dashboard.md — load it if you need to write or parse that wiki. Treat any per-weakness "kept / new record" number in it as a train-subset self-report, not a gated result; only finalize's sealed test number and any gate decision recorded in events.jsonl are honest.

scripts/now.py is the one-clock timestamp stamper those formats require; it is correct and still used by anything writing the wiki.

Removal is a separate decision

Deprecation is reversible; removal is not. The wiki format contract now has a real owner (skills/memory/wiki/SKILL.md), so what's left to decide (maintainer): stop dashboard.py inferring algorithm = "evograph" from wiki/ presence alone (any memory_skill: wiki run now writes it too), then delete this directory.

References

skills/memory/wiki/SKILL.md — the wiki format contract's current home: weakness-node/solution-card schemas, generalized for any algorithm via memory_skill: wiki.
references/dashboard.md — the same file formats, kept here for reading an existing evograph run dir's branch/round-revert specifics the generalized skill dropped.
references/clustering.md — weakness-node schema and the affected_tasks freeze rule, kept for reading historical run dirs.
references/graph.md — solution-card schema, branch layout, whole-round revert, kept for the same reason.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Evograph AI skill do?

Why use Evograph on TypingMind?

Because you install it once and use it with any model. Evograph is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Evograph in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/skillberry-ai/cap-evolve/tree/main/skills/algorithms/evograph. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Evograph?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Evograph?

As many as you like. As long as a model supports skills, you can use Evograph with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Evograph AI skill free?

Yes. It is published on GitHub by skillberry-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

evograph — DEPRECATED

Why it is deprecated

There is no deterministic engine

Reading an existing evograph run

Removal is a separate decision

References

Bundled files

Frequently asked questions

Related AI Skills

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