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 charlieviettq/awesome-agent-skill --skill grad-casgit clone --depth 1 https://github.com/charlieviettq/awesome-agent-skillWrote 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/charlieviettq/awesome-agent-skill/grad-cas)<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/grad-cas"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/grad-cas/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/charlieviettq/awesome-agent-skill/grad-cas"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/grad-cas.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 | $0.00103 | $0.01016 |
| Opus 5 | $0.00051 | $0.00508 |
| Sonnet 5 | $0.00021 | $0.00203 |
| Haiku 4.5 | $0.00010 | $0.00102 |
Grade A, and why
"grad-cas" 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 9d 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.
This is a copy
92% identical to grad-cas — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Complex Adaptive Systems (CAS)
Overview
Complex Adaptive Systems are composed of diverse, autonomous agents that interact locally according to simple rules, producing emergent global behavior that cannot be predicted from individual components. CAS exhibit self-organization, co-evolution with their environment, and operate at the edge of chaos — the zone between rigid order and random disorder where adaptation and innovation are maximized.
When to Use
- Analyzing systems where aggregate behavior cannot be predicted from component behavior
- Understanding why top-down control fails in certain organizational or market contexts
- Modeling innovation ecosystems, markets, or organizational change as adaptive processes
- Explaining sudden phase transitions or tipping points in social or economic systems
When NOT to Use
- When the system is genuinely simple and decomposable (use linear models)
- When precise quantitative prediction is required (CAS yields patterns, not point forecasts)
- When the research question is about individual agent psychology rather than system-level emergence
Assumptions
IRON LAW: In a CAS, system behavior EMERGES from local interactions
and CANNOT be predicted by analyzing individual components — the whole
is fundamentally different from the sum of parts.
Key assumptions:
- Agents are heterogeneous, autonomous, and adaptive (they learn and change rules)
- Interactions are local and nonlinear — small causes can produce large effects
- There is no central controller — order emerges from decentralized interaction
- The system co-evolves with its environment — fitness landscapes shift as agents adapt
Methodology
Step 1: Identify the System and Its Agents
Define system boundaries. Identify the diverse agents, their decision rules, and their local interaction patterns.
Step 2: Map Interaction Topology
Describe how agents interact: network structure, feedback loops (positive and negative), information flows, and resource dependencies.
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.
- 9d ago First seen · 102 lines · 103 tokens per session scan A 3a351ca83cfb
"grad-cas" is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (26 stars, last pushed 1mo ago), licensed MIT. It adds 103 tokens to every session and 1,016 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to grad-cas, differing in 8 lines, and is treated as a copy.
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