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 precompute-relationshipsgit 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/precompute-relationships)<a href="https://agentmods.dev/skills/jimmc414/claude-code-plugin-marketplace/precompute-relationships"><img src="https://agentmods.dev/badge/skills/jimmc414/claude-code-plugin-marketplace/precompute-relationships/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/precompute-relationships"><img src="https://agentmods.dev/badge/skills/jimmc414/claude-code-plugin-marketplace/precompute-relationships.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 | $0.00028 | $0.00706 |
| Opus 5 | $0.00014 | $0.00353 |
| Sonnet 5 | $0.00006 | $0.00141 |
| Haiku 4.5 | $0.00003 | $0.00071 |
Grade A, and why
precompute-relationships 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.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
precompute-relationships
When to Use
- Problem has fixed structure (grid, graph, hierarchy)
- Same relationships queried repeatedly
- Relationships can be computed from problem definition
- Constraint problems with peers/neighbors
- Any "what affects what" mapping
When NOT to Use
- Relationships change during execution
- Structure is too large to precompute
- Only needed once
The Pattern
Compute all static relationships at module load time. Store in dicts for O(1) lookup.
# Define structure
rows = 'ABCDEFGHI'
cols = '123456789'
# Precompute all squares
squares = [r + c for r in rows for c in cols]
# Precompute all units (rows, cols, boxes)
unit_list = (
[[r + c for c in cols] for r in rows] + # Rows
[[r + c for r in rows] for c in cols] + # Columns
[[r + c for r in rs for c in cs] # Boxes
for rs in ['ABC', 'DEF', 'GHI']
for cs in ['123', '456', '789']]
)
# Precompute which units each square belongs to
units = {s: [u for u in unit_list if s in u] for s in squares}
# Precompute peers (squares that constrain this one)
peers = {s: set(sum(units[s], [])) - {s} for s in squares}
Example (from pytudes Sudoku.ipynb)
def cross(A, B):
"""Cross product of elements in A and B."""
return [a + b for a in A for b in B]
digits = '123456789'
rows = 'ABCDEFGHI'
cols = digits
# All 81 squares
squares = cross(rows, cols)
# All 27 units
unitlist = ([cross(rows, c) for c in cols] +
[cross(r, cols) for r in rows] +
[cross(rs, cs)
for rs in ('ABC', 'DEF', 'GHI')
for cs in ('123', '456', '789')])
# units[s] = list of 3 units containing square s
units = {s: [u for u in unitlist if s in u]
for s in squares}
# peers[s] = set of 20 squares that see square s
peers = {s: set(sum(units[s], [])) - {s}
for s in squares}
# Now constraint propagation is fast:
def eliminate(values, s, d):
for peer in peers[s]: # O(1) lookup!
eliminate(values, peer, d)
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 · 89 lines · 28 tokens per session scan A 91133c6ec69a
precompute-relationships is a skill published in the GitHub repository jimmc414/claude-code-plugin-marketplace (4 stars, last pushed yesterday), licensed MIT. It adds 28 tokens to every session and 706 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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