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/codybrom/clairvoyance/deep-modulesnpx skills add codybrom/clairvoyance --skill deep-modulesgit clone --depth 1 https://github.com/codybrom/clairvoyanceWhat 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 | $0.00071 | $0.01750 |
| Opus 5 | $0.00036 | $0.00875 |
| Sonnet 5 | $0.00014 | $0.00350 |
| Haiku 4.5 | $0.00007 | $0.00175 |
Grade A, and why
deep-modules 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 2d 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Modules Review Lens
When invoked with $ARGUMENTS, focus the analysis on the specified file or module. Read the target code first, then apply the checks below.
Evaluate whether modules provide powerful functionality through simple interfaces.
When to Apply
- Reviewing a new class, module, or API design
- When a module feels like it "doesn't do enough" or has too many parameters
- When you see many small classes collaborating to do one thing
- When a method signature closely mirrors what it calls
- During refactoring to decide what to merge or deepen
Core Principles
The Depth Principle
Every module gives functionality and costs knowledge (in the form of an interface). Deep modules give a lot and ask for very little, delivering the highest return on interface cost. This is the same interface-vs-implementation-cost asymmetry pull-complexity-down's "Core Asymmetry" applies to caller burden — here it's applied to depth.
The deepest possible module can even have no interface at all. Garbage collection is the classic example: it does enormously complex work that callers never think about.
Importantly, interfaces are also bigger than they look. The visible part is the formal interface: signatures, types, and exceptions. The invisible part is the informal interface: ordering rules, side effects, performance characteristics, and thread safety assumptions. The informal interface is usually larger and more dangerous. When the invisible part goes undocumented, callers end up with dependencies they don't know exist, creating new unknown unknowns.
The Depth Test
For each module under review:
- How many things must a caller know to use this correctly?
- How much work does the module do behind that interface?
- Could a caller skip this module and do the work directly with similar effort?
If #3 is "yes," the module is shallow.
A concrete signal: if the documentation for a method would be longer than its implementation, it is shallow.
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.
- 2d ago First seen · 142 lines · 71 tokens per session scan A d6f6a369bf53
deep-modules is a skill published in the GitHub repository codybrom/clairvoyance (12 stars, last pushed 1mo ago), licensed MIT. It adds 71 tokens to every session and 1,750 once invoked, about $0.0004 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 2026-08-30.
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