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.
git clone --depth 1 https://github.com/axiomantic/spellbookWrote 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/commands/axiomantic/spellbook/crystallize)<a href="https://agentmods.dev/commands/axiomantic/spellbook/crystallize"><img src="https://agentmods.dev/badge/commands/axiomantic/spellbook/crystallize/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/axiomantic/spellbook/crystallize"><img src="https://agentmods.dev/badge/commands/axiomantic/spellbook/crystallize.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00036 | $0.11643 |
| Opus 5.5 | $0.00014 | $0.04657 |
| Sonnet 5.5 | $0.00007 | $0.02329 |
| Haiku 4.5 | $0.00004 | $0.01164 |
Grade A, and why
crystallize 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 1,085 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MISSION
Improve and compress instructions into high-density prompts that preserve ALL capability while reducing token overhead.
Scope
This command is the only path that enforces the Rules / General-Instructions
split. Other compression-adjacent commands (/simplify for code,
/sharpen-improve for prompt ambiguity, /optimizing-instructions for skill
token reduction) operate on different content domains or with different
contracts. To protect rules across passes, use /crystallize.
/optimizing-instructions interlocks with this command: it refuses to
operate on inputs that already contain a canonical ## Rules section,
routing the operator to /crystallize instead. See
skills/optimizing-instructions/SKILL.md for the guard.
Invariant Principles
-
Understand Before Touching: Read entire content. Map structure. Identify purpose. Catalog cross-references. Only then consider changes.
-
Compress First, Then Fill Gaps: Compress redundancy aggressively to establish a tight baseline. Then fill only the gaps identified in Phase 2 analysis. MEDIUM/LOW gap fills must be net-neutral (offset by equal compression). Only CRITICAL/HIGH gaps may add net content.
-
Preserve Behavior, Not Word Count: Pseudocode logic, data structure fields, error paths, and calibration failure modes must survive — but their phrasing can be compressed. An example trimmed to 3 lines that still anchors the behavior beats 8 lines of padding. A calibration note condensed to 1 sentence that still names the failure mode beats a paragraph.
-
Emotional Anchors Are Strategic: Opening, closing, and critical junctures need emphasis. Reducing 10 CRITICALs to 3 well-placed ones is refinement. Removing all is destruction.
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.
- 1mo ago First seen · 1,085 lines · 36 tokens per session scan A dccdc2d36d0d
crystallize is a command published in the GitHub repository axiomantic/spellbook (10 stars, last pushed 4d ago), licensed MIT. It adds 36 tokens to every session and 11,643 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
Other commands, from other repositories
speckit.specjudge.recommend
Recommend the model that fits this feature's tasks, with the fragment of the spec behind every level.
prompt
Convert a rough ask into a structured XML-tagged prompt using the factory-prompting vocabulary.
dev-ai-integration
Integration of language models (LLM) and AI APIs into applications.
dare-llm-integration
Integração segura e eficiente com LLMs (Gemini, Claude, OpenAI, Ollama) em projetos DARE.
promptkitty-assemble
Assemble a task-specific prompt with PromptKitty.
prompt-history
Manage history of created and optimized prompts.