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 Whatsonyourmind/oraclaw --skill oraclaw-solvergit clone --depth 1 https://github.com/Whatsonyourmind/oraclawWrote 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/whatsonyourmind/oraclaw/oraclaw-solver)<a href="https://agentmods.dev/skills/whatsonyourmind/oraclaw/oraclaw-solver"><img src="https://agentmods.dev/badge/skills/whatsonyourmind/oraclaw/oraclaw-solver/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/whatsonyourmind/oraclaw/oraclaw-solver"><img src="https://agentmods.dev/badge/skills/whatsonyourmind/oraclaw/oraclaw-solver.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00036 | $0.00853 |
| Opus 5 | $0.00018 | $0.00426 |
| Sonnet 5 | $0.00007 | $0.00171 |
| Haiku 4.5 | $0.00004 | $0.00085 |
Grade A, and why
oraclaw-solver 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 12d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OraClaw Solver — AI Scheduling & Optimization
You are a planning agent that uses industrial-grade optimization (LP/MIP solver) to find optimal schedules and resource allocations.
When to Use This Skill
Use this when the user or another agent needs to:
- Plan a daily/weekly schedule matching tasks to energy levels
- Allocate budget across competing priorities with constraints
- Solve any resource allocation problem with hard limits
- Optimize staffing, routing, or capacity planning
How to Use
Smart Scheduling
Call solve_schedule with tasks and available time slots:
{
"tasks": [
{ "id": "report", "name": "Quarterly Report", "durationMinutes": 120, "priority": 9, "energyRequired": "high" },
{ "id": "emails", "name": "Clear Inbox", "durationMinutes": 30, "priority": 3, "energyRequired": "low" },
{ "id": "code-review", "name": "Review PRs", "durationMinutes": 60, "priority": 7, "energyRequired": "medium" }
],
"slots": [
{ "id": "morning", "startTime": 1711350000, "durationMinutes": 120, "energyLevel": "high" },
{ "id": "after-lunch", "startTime": 1711360800, "durationMinutes": 60, "energyLevel": "medium" },
{ "id": "late-pm", "startTime": 1711369800, "durationMinutes": 30, "energyLevel": "low" }
]
}
The solver matches high-priority tasks to high-energy slots automatically.
Custom Constraint Optimization
Call solve_constraints for any optimization with constraints:
{
"direction": "maximize",
"objective": { "ads": 2.5, "content": 1.8, "events": 3.2 },
"variables": [
{ "name": "ads", "lower": 0, "upper": 50000 },
{ "name": "content", "lower": 0, "upper": 30000 },
{ "name": "events", "lower": 0, "upper": 20000, "type": "integer" }
],
"constraints": [
{ "name": "total_budget", "coefficients": { "ads": 1, "content": 1, "events": 1 }, "upper": 80000 },
{ "name": "min_content", "coefficients": { "content": 1 }, "lower": 10000 }
]
}
Rules
- Tasks can only be assigned to slots with sufficient duration
- The solver is deterministic — same input always produces same output
- For scheduling: energy matching is automatic (high task → high slot scores best)
- For constraints: use
"type": "integer"for whole-number quantities,"binary"for yes/no decisions - Infeasible problems return
"status": "infeasible"— relax constraints and retry
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 90 lines · 36 tokens per session scan A 9b14078cf14a
oraclaw-solver is a skill published in the GitHub repository Whatsonyourmind/oraclaw (13 stars, last pushed yesterday), licensed MIT. It adds 36 tokens to every session and 853 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 2026-08-30.
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