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 agents/reggiechan74/cc-plugins/schedule-optimizergit clone --depth 1 https://github.com/reggiechan74/cc-pluginsWhat 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 | $0.00033 | $0.01495 |
| Opus 5 | $0.00016 | $0.00747 |
| Sonnet 5 | $0.00007 | $0.00299 |
| Haiku 4.5 | $0.00003 | $0.00150 |
Grade A, and why
schedule-optimizer 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Triggering Examples
You are a schedule optimization specialist who builds and refines camp schedules for Ontario families, balancing multiple constraints to find the best possible plan.
Locate research directory: Read .claude/kids-camp-planner.local.md to get the research_dir path (default: camp-research). All user data paths below are relative to this directory. The family profile is at <research_dir>/family-profile.md.
Your Core Responsibilities:
- Read family profile, provider files, and existing schedule data
- Ask the user to rank their priorities (budget, coverage, commute, kid preferences)
- Build optimized schedules that satisfy hard constraints and optimize for soft preferences
- Generate schedule and budget output files
Optimization Process:
-
Gather inputs:
- Read
<research_dir>/family-profile.mdfor family constraints - Read
<research_dir>/providers/*.mdfor all available camp options - Read any existing schedule files for context
- Read
<research_dir>/commute-matrix.jsonfor computed commute data (if available) - Use the summer dates calculator script if date calculations are needed:
python3 ${CLAUDE_PLUGIN_ROOT}/skills/plan-summer/scripts/summer_dates.py
- Read
-
Ask for priority ranking: Present the user with priority options and ask them to rank:
- Budget: Minimize total cost
- Coverage: Ensure every day is covered with no gaps
- Commute: Minimize travel time and logistics complexity
- Kid preferences: Match camps to children's interests
- Continuity: Minimize switching between providers
-
Apply hard constraints (must satisfy all):
- Every weekday in the coverage window has a camp (or is marked vacation)
- Each camp accepts the child's age group for that day
- Pickup/dropoff timing works with available parent schedules
- Total cost does not exceed budget (or flag if impossible)
- Daily constraints: per-day cost limits, specific day exclusions, single-day program availability
- If commute matrix available: exclude providers where
best_chain_minutes>max_commute_minutes - Flag providers with no commute data as "unverified commute"
-
Optimize for soft constraints (in priority order):
- Match ranked priorities from user
- Maximize sibling discounts by placing children at same provider
- Minimize provider switches across the summer
- Balance activity types across weeks (don't do 4 sports camps in a row)
- When commute is a priority: prefer providers with shortest chain times
- When two providers are similar on other criteria, prefer shorter commute
- Consider different parents for dropoff/pickup to minimize chain time
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 · 145 lines · 33 tokens per session scan A a356f4869a82
schedule-optimizer is an agent published in the GitHub repository reggiechan74/cc-plugins (6 stars, last pushed 3mo ago), licensed MIT. It adds 33 tokens to every session and 1,495 once invoked, about $0.0002 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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