Borrowing it
Nothing to install: this file belongs to s-morgan-jeffries/omnifocus-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/s-morgan-jeffries/omnifocus-mcp/main/.claude/skills/performance-patterns/SKILL.mdgit clone --depth 1 https://github.com/s-morgan-jeffries/omnifocus-mcpWrote 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/s-morgan-jeffries/omnifocus-mcp/performance-patterns)<a href="https://agentmods.dev/skills/s-morgan-jeffries/omnifocus-mcp/performance-patterns"><img src="https://agentmods.dev/badge/skills/s-morgan-jeffries/omnifocus-mcp/performance-patterns.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 | $0.00064 | $0.01395 |
| Opus 5 | $0.00032 | $0.00698 |
| Sonnet 5 | $0.00013 | $0.00279 |
| Haiku 4.5 | $0.00006 | $0.00139 |
Grade A, and why
performance-patterns 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 8d 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OmniFocus MCP Performance Patterns
AppleScript round-trips via osascript are the dominant performance cost. Every optimization in this project reduces the number of subprocess calls.
Known Operation Timings
| Operation | Time | Notes |
|---|---|---|
Single osascript call overhead |
100-300ms | Minimum cost per subprocess |
get_tasks() — 188 tasks, no filters |
~2.3s | Baseline with full property extraction |
get_projects() — 33 projects |
~0.9s | After N+1 fix |
get_projects() — 33 projects (BEFORE fix) |
~7.6s | N+1 pattern: 1 call per project |
| Individual task update | 200-400ms | Single property change |
get_tasks() with overdue filter |
~0.3s | Filter-first eliminates non-matching tasks early |
| Database safety check | ~100ms | Runs before every destructive operation |
Timeout defaults: 60s standard, 300s maximum. Configurable per operation.
Pattern 1: Eliminate N+1 Queries
The most impactful optimization in this project. Before the fix, filtering projects by their tasks required one AppleScript call per project:
# BAD: N+1 pattern (33 projects = 33 calls = 7.6s)
for project in projects:
tasks = get_tasks(project_id=project.id) # 230ms each!
if meets_filter(tasks):
results.append(project)
# GOOD: Batch pattern (33 projects = 1 call = 0.9s)
all_tasks_by_project = _get_tasks_batch_for_filtering() # Single call
for project in projects:
tasks = all_tasks_by_project.get(project.id, [])
if meets_filter(tasks):
results.append(project)
Implementation: _get_tasks_batch_for_filtering() fetches minimal data (id, projectId, dueDate) for ALL tasks in one AppleScript call, returning a {projectId: [tasks]} dictionary.
Result: 8.5x faster (7.6s -> 0.9s for 33 projects). Scales linearly — 500 projects would go from 115s to 13.5s.
Apply this pattern whenever you need to cross project boundaries for filtering or aggregation.
Pattern 2: Conditional Filter-First Architecture
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.
- 8d ago First seen · 121 lines · 64 tokens per session scan A 917d7b92a80c
performance-patterns is a skill published in the GitHub repository s-morgan-jeffries/omnifocus-mcp (7 stars, last pushed 4mo ago), licensed MIT. It adds 64 tokens to every session and 1,395 once invoked, about $0.0003 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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