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 agentmods add skills/tjmustard/hypergraph-coding-agent-framework/hyper-discovernpx skills add tjmustard/Hypergraph-Coding-Agent-Framework --skill hyper-discovergit clone --depth 1 https://github.com/tjmustard/Hypergraph-Coding-Agent-FrameworkWhat 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 | $0.00018 | $0.00415 |
| Opus 5 | $0.00009 | $0.00208 |
| Sonnet 5 | $0.00004 | $0.00083 |
| Haiku 4.5 | $0.00002 | $0.00042 |
Grade A, and why
discover 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 yesterday.
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.
What it actually says
ROLE: The Discovery Agent
Your objective is to perform a recursive scan of the existing repository to map its topological structure into the architecture.yml hypergraph schema.
CRITICAL RULES
- Top-Down Discovery: Start with the directory structure to identify "Module" nodes. Then drill into files to identify "Atomic" nodes (functions, classes, components).
- Dependency Mapping: Pay strict attention to imports and exports. These define the depends_on edges in the YAML.
- Semantic Analysis: Do not just list files. Read the code to provide a concise description of the node's semantic purpose.
EXECUTION PHASES
[PHASE 1: Directory Mapping]
- Identify the major folders and map them to "Module" dimension nodes in the hypergraph.
[PHASE 2: Atomic Extraction]
- For each major file, identify the core functions or classes. Map these to "Atomic" dimension nodes.
- Link them to their parent Module via the implements edge.
[PHASE 3: Edge Detection]
- Trace the data flow. If File A imports File B, File B is a dependency. Map this to the depends_on edge.
OUTPUT
Update or create spec/compiled/architecture.yml. Set all newly discovered nodes to status: clean. Output a summary of the system's topological density (number of modules vs. atomic nodes).
Final Action:
- If creating the file from scratch, save the compiled YAML to
spec/compiled/architecture.yml. - If appending/modifying an existing graph, you MUST execute
python .agents/scripts/hypergraph_updater.py spec/compiled/architecture.yml [modified_node_ids]via your terminal tool to properly propagate the Blast Radius.
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.
- yesterday First seen · 39 lines · 18 tokens per session scan A 0be2d3ad2af2
discover is a skill published in the GitHub repository tjmustard/Hypergraph-Coding-Agent-Framework (2 stars, last pushed 1mo ago), licensed MIT. It adds 18 tokens to every session and 415 once invoked, about $0.0001 per session on Opus 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.
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