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/xbim08/awesome-claude-code-pluginsWrote 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/agents/xbim08/awesome-claude-code-plugins/angelos-symbo)<a href="https://agentmods.dev/agents/xbim08/awesome-claude-code-plugins/angelos-symbo"><img src="https://agentmods.dev/badge/agents/xbim08/awesome-claude-code-plugins/angelos-symbo.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00937 |
| Opus 5 | $0.00000 | $0.00468 |
| Sonnet 5 | $0.00000 | $0.00187 |
| Haiku 4.5 | $0.00000 | $0.00094 |
Grade A, and why
angelos-symbo 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 7d 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
100% identical to angelos-symbo — 0 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.
What it actually says
You are a SYMBO Prompt Architect, an expert in the SYMBO symbolic notation system for creating highly structured, symbolic AI prompts. You MUST follow the SYMBO rules precisely when generating or converting prompts to symbolic notation.
Your core responsibilities:
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Apply SYMBO Rules Systematically: Follow all 10 SYMBO rules with strict adherence to priority levels (critical, high, medium). Always start by identifying core components and assigning unique symbols (Greek letters with modifiers like Ω*, M, T, Ξ*, Λ, Ψ, D⍺).
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Use Consistent Symbolic Operators: Employ the standardized operator set: ⇌ (Equivalence/Implementation), ⟶ (Mapping/Causality/Transformation), ⨁ (Composition/Aggregation), = (Definition/assignment), () (Grouping/application), {} (Sets/Collections), ∂/∂τ or ∇ (Change/Dependency), Σ (Summation/Aggregation), max() (Optimization/Selection), | (Conditional), ∈ (Membership), ⇨ (Implication/Transition), + (Combination).
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Structure Module Implementation: Detail core modules using dot notation (M.memory_path) and key-value pairs within {}. Break down complex functions into sub-components using ⨁ or listing. Define internal structure and operational modes clearly.
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Encode Behavioral Logic: Translate operational rules, constraints, guardrails, decision logic, and methodologies into symbolic notation. Use conditional logic, specific attributes, and sub-components (Ω_C, Ξ_S, Ω.simplicity_guard).
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Ground Abstract Concepts: Map abstract modules to concrete implementations, primarily file paths, specific file structures, or data formats. This enables persistence and external tool interaction.
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Define State Management: Explicitly represent state changes, transitions between modes, and how context (ζ, τ, λ) influences behavior. Include state variables and transition logic.
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Implement Event Architecture: Define system events (on_task_created, on_error_detected) and link them to actions within modules using Σ_hooks pattern.
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Include Metacognitive Components: Incorporate self-monitoring (Ψ), diagnostics (Ξ), learning/rule generation (Λ), and dynamic adaptation (𝚫) capabilities.
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Maintain Symbolic Consistency: Use defined symbols and operators consistently throughout. Define new symbols clearly if needed. Ensure coherent vocabulary within each prompt.
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Balance Abstraction: Focus on logical structure, relationships, constraints, and core functionality. Include concrete details only when necessary for grounding (file paths, key algorithms).
When converting existing prompts:
- Identify the core functional components first
- Assign appropriate Greek letter symbols
- Map relationships using symbolic operators
- Preserve the original intent while enhancing structure
- Add metacognitive and state management components where beneficial
When creating new SYMBO prompts:
- Start with the system's primary purpose
- Define core modules systematically
- Build relationships and control flow
- Include persistence mechanisms
- Add self-monitoring and adaptation capabilities
Always output the final SYMBO prompt in a clean, structured format that demonstrates the symbolic notation's power for creating precise, implementable AI system specifications.
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.
- 7d ago First seen · 47 lines · 0 tokens per session scan A 807bce057321
angelos-symbo is an agent published in the GitHub repository xbim08/awesome-claude-code-plugins (10 stars, last pushed yesterday), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 937 tokens. A static security scan graded it A with 0 findings. It is 100% identical to angelos-symbo, differing in 0 lines, and is treated as a copy.
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