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.
npx skills add eric-tramel/moraine --skill crystallizegit clone --depth 1 https://github.com/eric-tramel/moraineWrote 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/eric-tramel/moraine/crystallize)<a href="https://agentmods.dev/skills/eric-tramel/moraine/crystallize"><img src="https://agentmods.dev/badge/skills/eric-tramel/moraine/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/skills/eric-tramel/moraine/crystallize"><img src="https://agentmods.dev/badge/skills/eric-tramel/moraine/crystallize.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 58 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00089 | $0.01283 |
| Opus 5.5 | $0.00036 | $0.00513 |
| Sonnet 5.5 | $0.00018 | $0.00257 |
| Haiku 4.5 | $0.00009 | $0.00128 |
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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Crystallize
Overview
Use this skill to convert rough input into an implementation plan that another agent can execute without needing the original discussion. The output is a single ignored local Markdown file under the gitignored plans/ directory.
Do not implement the plan while crystallizing. Inspect, research, and reason; only write the plan artifact.
Plan Artifact
Create plans/ if it does not exist:
mkdir -p plans
Choose a unique filename:
- Derive a lowercase hyphenated slug from the rough input.
- Prefix it with a UTC timestamp from
date -u +%Y%m%d-%H%M%S. - Use
plans/<timestamp>-<slug>.md. - If the file already exists, append
-2,-3, etc.
Keep plans/ and every plan file ignored and untracked. Do not add them to git, do not create a tracked placeholder, and do not include plan files in commits.
Research Wave
Launch parallel subagents before drafting the plan. Give each subagent the raw user input, any linked issue or document text, and explicit permission to inspect the workspace. Do not include your intended solution.
Use these research roles unless a role is plainly irrelevant:
CodebaseResearch: identify affected crates, modules, APIs, tests, docs, and local patterns. Return concrete file paths and constraints.HistoryResearch: use Moraine MCP tools directly when prior or active agent context may matter. Search past sessions by keywords, inspect relevant sessions, and return only actionable history.PublicPriorArtResearch: research public implementations, official docs, standards, libraries, and comparable approaches. Prefer primary sources and include links when external sources influence the plan.RiskResearch: identify correctness, security, performance, migration, compatibility, rollout, and testing risks.
Subagent prompt shape:
Research this rough implementation idea for Moraine.
Focus only on <role>.
Return evidence, concrete file paths or source links, constraints, and recommended plan implications.
Do not draft the whole plan and do not assume my preferred approach.
Rough input:
<raw user input>
What ships with it
1 file 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.
- 1mo ago First seen · 157 lines · 89 tokens per session scan A dd11e9771560
crystallize is a skill published in the GitHub repository eric-tramel/moraine (116 stars, last pushed 12d ago), licensed Apache-2.0. It adds 89 tokens to every session and 1,283 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
web-search
A web-search and information-retrieval skill for finding pages, current information, and news online.
foundry-hosted-agent-validation
Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.
python-feature-lifecycle
Guidance for package and feature lifecycle in the Agent Framework Python codebase, including stage meanings, feature-stage decorators, feature enums, and how to move APIs from one stage to the next.
python-development
Coding standards, conventions, and patterns for developing Python code in the Agent Framework repository. Use this when writing or modifying Python source files in the python/ directory.
security-conductor
Operating procedure for the kirocrew-security-conductor agent. Use when a security conductor session is seeded, inspected or debugged. Run vulnerability discovery on one target as a supervised fleet — one auditor per attack surface, an independent verifier per finding, two human gates.
artifact-deploy
One-click deploy a user's pre-built app/artifact into their OWN AWS account and get a global public HTTPS link (Vercel-like), with a default TTL and promote-to-persistent. Use when the user says "deploy this", "ship this demo", "give me a public link", "share this externally", or "deploy to AWS".