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 jimmc414/claude-code-plugin-marketplace --skill use-table-driven-rulesgit clone --depth 1 https://github.com/jimmc414/claude-code-plugin-marketplaceWrote 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/jimmc414/claude-code-plugin-marketplace/use-table-driven-rules)<a href="https://agentmods.dev/skills/jimmc414/claude-code-plugin-marketplace/use-table-driven-rules"><img src="https://agentmods.dev/badge/skills/jimmc414/claude-code-plugin-marketplace/use-table-driven-rules/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/jimmc414/claude-code-plugin-marketplace/use-table-driven-rules"><img src="https://agentmods.dev/badge/skills/jimmc414/claude-code-plugin-marketplace/use-table-driven-rules.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 | $0.00033 | $0.00710 |
| Opus 5.5 | $0.00013 | $0.00284 |
| Sonnet 5.5 | $0.00007 | $0.00142 |
| Haiku 4.5 | $0.00003 | $0.00071 |
Grade A, and why
use-table-driven-rules 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
use-table-driven-rules
When to Use
- Many special cases (simplification rules)
- Configuration-driven behavior
- Lookup tables
- State machines
- Rules that change frequently
- Making rules visible/maintainable
When NOT to Use
- Few simple cases (use if-elif)
- Rules require complex logic
- Order of evaluation matters in complex ways
The Pattern
Store rules in data structures instead of code.
# Instead of:
def simplify(expr):
if expr == ('+', 0, x): return x
if expr == ('+', x, 0): return x
if expr == ('*', 1, x): return x
...
# Use table:
simp_rules = {
('+', 0, 'x'): 'x',
('+', 'x', 0): 'x',
('*', 1, 'x'): 'x',
('*', 'x', 1): 'x',
('*', 0, 'x'): 0,
}
def simplify(expr):
pattern = generalize(expr) # Convert to pattern form
if pattern in simp_rules:
return instantiate(simp_rules[pattern], expr)
return expr
Example (from pytudes)
# Simplification table (Differentiation.ipynb)
simp_table = {
sin(0): 0,
sin(pi): 0,
sin(pi/2): 1,
cos(0): 1,
cos(pi): -1,
cos(pi/2): 0,
tan(0): 0,
ln(1): 0,
ln(e): 1,
}
def simp(y):
"""Simplify expression using table and rules."""
if y in simp_table:
return simp_table[y]
# ... additional simplification logic
# Direction vectors (AdventUtils.ipynb)
arrow_direction = {
'^': (0, -1), # North
'v': (0, 1), # South
'>': (1, 0), # East
'<': (-1, 0), # West
'.': (0, 0), # Stay
}
def move(pos, arrow):
dx, dy = arrow_direction[arrow]
return (pos[0] + dx, pos[1] + dy)
# Factor multipliers (pal2.py)
factor = {'left': +1, 'right': -1}
def add_word(self, direction, word):
self.diff += factor[direction] * len(word)
# Lisp operations (lis.py)
env = {
'+': op.add,
'-': op.sub,
'*': op.mul,
'/': op.truediv,
}
Key Principles
- Data over code: Rules as dict entries, not if statements
- Easy to extend: Adding rule = adding dict entry
- Visible rules: Can print/inspect the table
- Consistent lookup: All rules checked same way
- Fallback logic: Handle missing keys gracefully
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 · 105 lines · 33 tokens per session scan A 60b219bf5248
use-table-driven-rules is a skill published in the GitHub repository jimmc414/claude-code-plugin-marketplace (4 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 710 once invoked, about $0.0001 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 2026-09-04.
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