Agent skill

AI Crystallize

by Abilityai in Abilityai/cornelius

Autonomous AI crystallization - synthesizes converged thinking topics into ai-inferred notes in a dedicated folder.

MITAuto-check: notesKnowledge Management

Install AI Crystallize

skills CLI
$ npx skills add Abilityai/cornelius --skill ai-crystallize -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Abilityai/cornelius ai-crystallize --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Abilityai/cornelius.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/ai-crystallize .claude/skills/ai-crystallize && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ai-crystallize
GitHub stars
109
Token cost
~2.9k tokens
SKILL.md length
1,102 words
Files
1
Skills in repo
51
Repo updated
First seen
Licence
MIT

At a glance

Autonomous AI crystallization - synthesizes converged thinking topics into ai-inferred notes in a dedicated folder.

  • Works in 7 steps: Get Date and Load Converged Topics → Deduplicate (Idempotency) → Select the Batch → …
  • Tasks that involve Knowledge bases
  • SKILL.md covers Design principle, State Dependencies, Prerequisites and Configuration, plus 6 more sections
  • Calls git

What it does

AI Crystallize is an agent skill from Abilityai/cornelius. Autonomous AI crystallization - synthesizes converged thinking topics into ai-inferred notes in a dedicated folder. Never touches the human-curated permanent knowledge base and never changes a topic's status, so manual crystallization stays available to the user.

Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Knowledge Management, covering Knowledge bases. The repository describes itself as: AI-powered second brain template for Claude Code + Obsidian. The licence is MIT.

When your agent uses it

  • Tasks that involve Knowledge bases

Example prompts

  • “/ai-crystallize”

Requirements

  • Pre-approved tools (allowed-tools): Read, Write, Grep, Glob, Bash

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Get Date and Load Converged Topics
  2. Deduplicate (Idempotency)
  3. Select the Batch
  4. Extract the Converged Conclusion
  5. Draft the AI Crystallization Note
  6. Update the Crystallization Index
  7. Write Session Changelog

What it can do on your machine

Read from SKILL.md and the folder at commit fd5e9a4. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Grep
    • Glob
    • Bash

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

AI Crystallize loads about 2.9k tokens when it runs. Until then it costs about 70 tokens; SKILL.md has 1,102 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~70
When it runs · the whole SKILL.md, loaded when a task matches
~2.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Write, Grep, Glob, Bash

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Abilityai/cornelius at commit fd5e9a4, republished under its MIT licence (© Abilityai). 1,102 words, ~2,884 tokens.

Download SKILL.mdSave it as .claude/skills/ai-crystallize/SKILL.md (or your agent's skills folder).
name
ai-crystallize
description
Autonomous AI crystallization - synthesizes converged thinking topics into ai-inferred notes in a dedicated folder. Never touches the human-curated permanent knowledge base and never changes a topic's status, so manual crystallization stays available to the user.
allowed-tools
Read, Write, Grep, Glob, Bash
automation
autonomous
schedule
0 8 * * 1
user-invocable
true
argument-hint
[topic-slug] (optional - processes all un-crystallized converged topics if omitted)

AI Crystallize

Autonomous companion to the human crystallization path. Each run synthesizes converged thinking topics into standalone notes written to a dedicated, segregated folder - never Brain/02-Permanent/.

Why this exists: Converged topics pile up faster than a human can crystallize them. This playbook captures the AI's synthesis of each converged topic so the conclusion is not lost - while preserving the one boundary the second brain must never cross autonomously: nothing enters the endorsed permanent knowledge base without an explicit endorsement act by the user.

The boundary, stated plainly:

  • This playbook writes ONLY to Brain/05-Meta/AI Crystallizations/, tagged provenance: ai-inferred, excluded from semantic indexing.
  • It NEVER writes to Brain/02-Permanent/, Brain/AI Extracted Notes/, or Brain/05-Meta/Reports/.
  • It NEVER changes a topic's status in the Thinking Registry (the topic stays converged), so /manage-thinking-topics crystallize remains fully available to the user for the real, endorsed crystallization.
  • Its output is a candidate for human review, not a permanent note.

Design principle

Automate the synthesis, segregate the artifact, gate the endorsement. The AI may draft what it concluded; only the human may promote a conclusion into the knowledge they will reason FROM.

State Dependencies

SourceLocationReadWriteDescription
Thinking RegistryBrain/05-Meta/Thinking/THINKING-REGISTRY.md✓Find converged topics (read-only - never mutated here)
Thinking FilesBrain/05-Meta/Thinking/[topic-slug].md✓Source of the converged conclusion (read-only)
AI CrystallizationsBrain/05-Meta/AI Crystallizations/[topic-slug].md✓✓Output notes (the only write target for content)
Crystallization IndexBrain/05-Meta/AI Crystallizations/INDEX.md✓✓Manifest of what has been AI-crystallized
Session ChangelogsBrain/05-Meta/Changelogs/✓Run log

Prerequisites

  • Thinking Registry exists at Brain/05-Meta/Thinking/THINKING-REGISTRY.md
  • Output folder Brain/05-Meta/AI Crystallizations/ exists (create if missing)
  • At least one topic with status: converged (otherwise clean exit)

Configuration

  • Per-run cap: process at most 5 un-crystallized converged topics per run (reliability + 45-minute budget). Remaining topics are picked up on the next run. The cap is a soft limit; if fewer than 5 remain, process all.

Process

Step 1: Get Date and Load Converged Topics
bash
date '+%Y-%m-%d'

Read Brain/05-Meta/Thinking/THINKING-REGISTRY.md. Collect every topic listed in the Converged (Ready for Crystallization) table (or any topic whose thinking-file frontmatter is status: converged).

If a specific [topic-slug] was passed as argument, restrict to that single topic (verify it is converged; if not, log and exit).

If there are no converged topics, write a brief changelog entry and exit cleanly.

Step 2: Deduplicate (Idempotency)

For each converged topic, check whether Brain/05-Meta/AI Crystallizations/[topic-slug].md already exists.

  • Exists -> already AI-crystallized; skip.
  • Does not exist -> eligible for this run.

This file-existence check is the dedup mechanism. The playbook never mutates the Thinking Registry or thinking files, so it is fully idempotent and safe to retry.

Step 3: Select the Batch

From the eligible (un-crystallized) converged topics, take up to the per-run cap (5), oldest-converged first where the converged date is available; otherwise registry order. Convergence-date proxy: thinking files carry no converged_on frontmatter field, so use the frontmatter updated: date (the converging run stamps it) as the oldest-first sort key - cheap to extract in one pass with grep -m1 '^updated:' across the Thinking folder. Note in the run log any eligible topics deferred to a later run.

Step 4: Extract the Converged Conclusion

For each selected topic, read Brain/05-Meta/Thinking/[topic-slug].md and extract the conclusion. Prefer the canonical block, fall back gracefully:

  1. Look for ## Final Synthesis - the canonical block appended by the incubation loop at convergence. If present, this is authoritative. Extract: Conclusion, Confidence + leading hypothesis, Key evidence, Convergence path, Remaining uncertainty, Related topics. Note: legacy topics instead carry an inline **Final Synthesis:** / **Final converged position:** line (not a ## header) near the end - a grep '## Final Synthesis' will miss these. Match the lowercase inline form too, or just go straight to the tail per step 2.
  2. If absent (legacy topics), read only the last ~120 lines of the file (some are 5,000+ lines). Extract: central question (frontmatter topic), the trailing "Final Synthesis" / "Final converged position" / "Current Best Answer", the last "Updated Hypotheses" table, and any "Open Questions". The convergence-assessment block (run count, leading-hypothesis streak, confidence delta) sits just above and gives the Convergence Path.
  3. Read the full file only if the last 200 lines contain no conclusion.

Do not run KB search or web search - this playbook synthesizes from the already-converged reasoning, not new evidence. Keep it fast and self-contained.

Show full SKILL.md (412 more words)Show less
Step 5: Draft the AI Crystallization Note

Write to Brain/05-Meta/AI Crystallizations/[topic-slug].md:

markdown
---
created: YYYY-MM-DD
updated: YYYY-MM-DD
created_by: [model-name]
updated_by: [model-name]
agent_version: 01.25
provenance: ai-inferred
source_topic: "[[topic-slug]]"
status: ai-crystallized
---

> 🤖 **AI Crystallization - not endorsed knowledge.** Synthesized autonomously from a converged thinking topic. Carries `provenance: ai-inferred` and lives outside `Brain/02-Permanent/`. This is a **candidate for human review**, not a permanent note. To promote it, the user runs `/manage-thinking-topics crystallize [topic-slug]`.

# [Topic Title]

**Central Question:** [exact question from the thinking file]

## Conclusion
[1-2 paragraphs - the substantive answer, in neutral third-person AI-synthesis voice. NOT first-person your voice - this is ai-inferred, not his endorsed thinking.]

## Confidence
[X% on H[N] (and any co-leading hypotheses)]

## Key Evidence
- [[Note A]] - [why it mattered]
- [[Note B]] - [why it mattered]
- [[Note C]] - [why it mattered]

## Convergence Path
[Standard | Framework exhaustion] after [N] runs

## Remaining Uncertainty
[1 paragraph - what is known to be unknown; what could change this]

## Source
Crystallized from [[topic-slug]] (`Brain/05-Meta/Thinking/[topic-slug].md`), converged [date].
[Related topics, if any: [[other-slug]]]

Voice discipline: write the Conclusion in neutral analytical voice, never first-person as the user. This artifact is ai-inferred; it must never read as if the user authored or endorsed it. Per CLAUDE.md's guarded boundary, only an explicit endorsement act by the user (via manual crystallization) may set provenance to endorsed or originated.

Step 6: Update the Crystallization Index

Append a row to Brain/05-Meta/AI Crystallizations/INDEX.md (create the file with a header if missing):

| [topic-slug] | [one-line conclusion summary] | [confidence] | YYYY-MM-DD |
Step 7: Write Session Changelog

Write to Brain/05-Meta/Changelogs/CHANGELOG - AI Crystallize YYYY-MM-DD.md:

markdown
---
created: YYYY-MM-DD
updated: YYYY-MM-DD
created_by: [model-name]
updated_by: [model-name]
agent_version: 01.25
---

# AI Crystallize Session: YYYY-MM-DD

## Crystallized This Run
| Topic | Conclusion Summary | Confidence | Status |
|-------|-------------------|------------|--------|
| [slug] | [summary] | X% | ai-crystallized (awaiting human review) |

## Deferred (over per-run cap)
[List of eligible converged topics not processed this run, or "None"]

## Note
These are AI-inferred candidates in `Brain/05-Meta/AI Crystallizations/`. The source topics remain `status: converged` and available for the user's manual crystallization via `/manage-thinking-topics crystallize [slug]`.

What This Playbook Must Never Do

  • ❌ Write to Brain/02-Permanent/, Brain/AI Extracted Notes/, or Brain/05-Meta/Reports/
  • ❌ Set status: crystallized on a topic (that marks human, endorsed crystallization)
  • ❌ Set provenance to endorsed or originated
  • ❌ Write any note in the user's first-person voice
  • ❌ Mutate the Thinking Registry or thinking files

If any of these would be required, stop and log - the topic belongs to the human path.


Error Handling

ErrorRecovery
Registry missingLog to changelog, exit cleanly
No converged topicsLog to changelog, exit cleanly
Output folder missingCreate Brain/05-Meta/AI Crystallizations/, proceed
Thinking file missing for converged topicLog warning, skip topic
No Final Synthesis and no conclusion in last 200 linesRead full file once; if still none, skip topic and log
Run approaches 45 minutesStop after current topic, log remaining as deferred

Completion Checklist

  • Registry read - converged topics identified
  • Each converged topic deduped against the AI Crystallizations folder
  • Batch selected within per-run cap; deferred topics logged
  • Each processed topic: conclusion extracted (Final Synthesis preferred)
  • Each processed topic: note written to Brain/05-Meta/AI Crystallizations/ with provenance: ai-inferred
  • No write touched 02-Permanent/, AI Extracted Notes/, or Reports/
  • No topic status changed in the registry
  • Index updated
  • Session changelog written

Relationship to Other Skills

SkillRelationship
incubation-loopProduces the converged topics this playbook reads
manage-thinking-topics crystallizeThe human, endorsed path - writes to 02-Permanent/ / Reports/, gated by approval. This playbook never overlaps with it; it is the AI-only, segregated companion
synthesize-insightsthe user's manual synthesis tool - unaffected

Self-Improvement

After completing this skill's primary task, consider tactical improvements:

  • Review execution: Were there friction points, unclear steps, or inefficiencies?
  • Identify improvements: Could extraction, the note template, or the cap be sharper?
  • Scope check: Only execution changes - NEVER relax the segregation boundary (the never-touch-permanent rule is load-bearing, not tactical)
  • Apply improvement (if identified):
    • Edit this SKILL.md with the specific improvement
    • Keep changes minimal and focused
  • Version control:
    • git add .claude/skills/ai-crystallize/SKILL.md
    • git commit -m "refactor(ai-crystallize): <brief improvement>"

© Abilityai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/ai-crystallize of Abilityai/cornelius.

Open the folder on GitHubat commit fd5e9a4

Compare with similar skills

AI Crystallize next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

AI Crystallize compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
AI Crystallize this skillAbilityai/cornelius109—~2.9kAutomated safety check: NotesMIT
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Project CairniBlinkQ/project-cairn2352 repos~861Automated safety check: PassMIT
LLM Wiki Knowledge GraphEgonex-AI/Understand-Anything86k1 repos~1.5kAutomated safety check: PassMIT
Find And Citeoutline/outline41k—~537Automated safety check: PassCustom licence
Xhs Virtual Productchenjin-cmd/xhs-virtual-product727—~862Automated safety check: PassMIT

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Questions about AI Crystallize

What does AI Crystallize do?

Autonomous AI crystallization - synthesizes converged thinking topics into ai-inferred notes in a dedicated folder. AI Crystallize is an agent skill from Abilityai/cornelius. Autonomous AI crystallization - synthesizes converged thinking topics into ai-inferred notes in a dedicated folder.

When should I use AI Crystallize?

AI Crystallize fits situations like: tasks that involve Knowledge bases.

How do I install AI Crystallize in Claude Code?

Run `npx skills add Abilityai/cornelius --skill ai-crystallize -a claude-code`. Or copy the skill folder (.claude/skills/ai-crystallize in Abilityai/cornelius) into .claude/skills/ai-crystallize in your project. Claude Code loads it when a task matches its description.

How do I install AI Crystallize in Codex?

Run `npx skills add Abilityai/cornelius --skill ai-crystallize -a codex`. Or copy the skill folder (.claude/skills/ai-crystallize in Abilityai/cornelius) into .agents/skills/ai-crystallize in your project. Codex loads it when a task matches its description.

Can I use AI Crystallize in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Abilityai/cornelius --skill ai-crystallize -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ai-crystallize, .gemini/skills/ai-crystallize, .github/skills/ai-crystallize and .opencode/skills/ai-crystallize in your project.

What does AI Crystallize need to run?

Going by SKILL.md and its folder, AI Crystallize needs the command-line tools its instructions call (git). Its frontmatter pre-approves these tools: Read, Write, Grep, Glob, Bash.

Does AI Crystallize access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is AI Crystallize safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does AI Crystallize use?

AI Crystallize is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does AI Crystallize use?

About 2.9k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to AI Crystallize?

Skills that share tags, products or a category with AI Crystallize: Capture Conversation (outline/outline, 41k stars), Project Cairn (iBlinkQ/project-cairn, 235 stars), LLM Wiki Knowledge Graph (Egonex-AI/Understand-Anything, 86k stars) and Find And Cite (outline/outline, 41k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains AI Crystallize?

Abilityai (a GitHub organization) maintains it in Abilityai/cornelius, which has 109 GitHub stars. The repository holds 51 skills in this directory. The repository was last updated on September 22, 2026.

Source: Abilityai/cornelius on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.