Schedule
asgeirtj/system_prompts_leaks
Create, update, list, or run scheduled cloud agents (routines) that execute on a cron schedule.
Design deterministic online scheduling policies from current observations.
Apache-2.0Auto-check passed
Install Online Resource Scheduling
$ npx skills add benchflow-ai/skillsbench --skill online-resource-scheduling -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install benchflow-ai/skillsbench online-resource-scheduling --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .claude/skills && cp -r skills-src/tasks-extra/gpu-cluster-online-scheduling/environment/skills/online-resource-scheduling .claude/skills/online-resource-scheduling && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "online-resource-scheduling" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks-extra/gpu-cluster-online-scheduling/environment/skills/online-resource-scheduling into .claude/skills/online-resource-scheduling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "online-resource-scheduling", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/benchflow-ai/skillsbench/tree/main/tasks-extra/gpu-cluster-online-scheduling/environment/skills/online-resource-schedulingType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add benchflow-ai/skillsbench --skill online-resource-scheduling -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install benchflow-ai/skillsbench online-resource-scheduling --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .agents/skills && cp -r skills-src/tasks-extra/gpu-cluster-online-scheduling/environment/skills/online-resource-scheduling .agents/skills/online-resource-scheduling && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "online-resource-scheduling" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks-extra/gpu-cluster-online-scheduling/environment/skills/online-resource-scheduling into .agents/skills/online-resource-scheduling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "online-resource-scheduling", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add benchflow-ai/skillsbench --skill online-resource-scheduling -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install benchflow-ai/skillsbench online-resource-scheduling --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/tasks-extra/gpu-cluster-online-scheduling/environment/skills/online-resource-scheduling .cursor/skills/online-resource-scheduling && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "online-resource-scheduling" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks-extra/gpu-cluster-online-scheduling/environment/skills/online-resource-scheduling into .cursor/skills/online-resource-scheduling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "online-resource-scheduling", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/benchflow-ai/skillsbench.git --path tasks-extra/gpu-cluster-online-scheduling/environment/skills/online-resource-scheduling--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add benchflow-ai/skillsbench --skill online-resource-scheduling -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install benchflow-ai/skillsbench online-resource-scheduling --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/tasks-extra/gpu-cluster-online-scheduling/environment/skills/online-resource-scheduling .gemini/skills/online-resource-scheduling && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "online-resource-scheduling" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks-extra/gpu-cluster-online-scheduling/environment/skills/online-resource-scheduling into .gemini/skills/online-resource-scheduling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "online-resource-scheduling", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install benchflow-ai/skillsbench online-resource-schedulingInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add benchflow-ai/skillsbench --skill online-resource-scheduling -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .github/skills && cp -r skills-src/tasks-extra/gpu-cluster-online-scheduling/environment/skills/online-resource-scheduling .github/skills/online-resource-scheduling && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "online-resource-scheduling" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks-extra/gpu-cluster-online-scheduling/environment/skills/online-resource-scheduling into .github/skills/online-resource-scheduling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "online-resource-scheduling", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add benchflow-ai/skillsbench --skill online-resource-scheduling -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install benchflow-ai/skillsbench online-resource-scheduling --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/tasks-extra/gpu-cluster-online-scheduling/environment/skills/online-resource-scheduling .opencode/skills/online-resource-scheduling && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "online-resource-scheduling" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks-extra/gpu-cluster-online-scheduling/environment/skills/online-resource-scheduling into .opencode/skills/online-resource-scheduling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "online-resource-scheduling", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
online-resource-scheduling- GitHub stars
- 1.8k
- Token cost
- ~761 tokens
- SKILL.md length
- 278 words
- Files
- 1
- Skills in repo
- 189
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Design deterministic online scheduling policies from current observations.
- Works in 6 steps: Read visible objective weights. → For each pending item, enumerate… → For each feasible action, estimate the… → …
- Assigning arriving work to limited resources without seeing future requests
- SKILL.md covers Core Workflow, Weighted Marginal Scoring, Unrelated Example and Practical Guidance
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Online Resource Scheduling is an agent skill from benchflow-ai/skillsbench. Design deterministic online scheduling policies from current observations. Use when assigning arriving work to limited resources without seeing future requests.
Its SKILL.md is about 760 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
The repository describes itself as: SkillsBench evaluates how well skills work and how effective agents are at using them. The licence is Apache-2.0.
When your agent uses it
- Assigning arriving work to limited resources without seeing future requests
Example prompts
- “/online-resource-scheduling”
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Read visible objective weights.
- For each pending item, enumerate feasible actions.
- For each feasible action, estimate the change in each objective component.
- Compute weighted_marginal_score.
- Choose the feasible action with the lowest score.
- Apply the action to temporary state before scoring later actions.
What it can do on your machine
Read from SKILL.md and the folder at commit 9a1f4dd. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Online Resource Scheduling loads about 761 tokens when it runs. Until then it costs about 47 tokens; SKILL.md has 278 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from benchflow-ai/skillsbench at commit 9a1f4dd, republished under its Apache-2.0 licence (© benchflow-ai). 278 words, ~761 tokens.
Download SKILL.mdSave it as
.claude/skills/online-resource-scheduling/SKILL.md (or your agent's skills folder).- name
- online-resource-scheduling
- description
- Design deterministic online scheduling policies from current observations. Use when assigning arriving work to limited resources without seeing future requests.
Online Resource Scheduling
Use this skill to build online schedulers that make deterministic decisions from the current observation only.
Core Workflow
Convert each observation into a temporary state, rank pending work, score feasible actions by weighted marginal cost, update the temporary state immediately, then replay the final action list before returning it.
actions = []
temporary_state = copy_resources(observation)
for item in ranked_pending_items(observation):
candidates = enumerate_feasible_actions(item, temporary_state)
if not candidates:
actions.append(defer_or_reject(item))
continue
scored = []
for action in candidates:
deltas = estimate_objective_deltas(action, temporary_state)
score = sum(weights[k] * deltas[k] for k in deltas)
scored.append((score, stable_tie_break(action), action))
chosen = min(scored)[-1]
actions.append(chosen)
apply(chosen, temporary_state)
validate(actions, observation)
return actionsWeighted Marginal Scoring
When a task provides objective weights, use them to compare feasible actions. Avoid fixed rules such as "always first-fit", "always minimize fragmentation", or "always use the tightest slot". Those can be wrong when another objective component has a larger weighted effect.
Suggested generic workflow:
- Read visible objective weights.
- For each pending item, enumerate feasible actions.
- For each feasible action, estimate the change in each objective component.
- Compute
weighted_marginal_score. - Choose the feasible action with the lowest score.
- Apply the action to temporary state before scoring later actions.
weighted_marginal_score =
weight_1 * delta_component_1
+ weight_2 * delta_component_2
+ weight_3 * delta_component_3
+ ...
+ deterministic_tie_breakFeasibility remains a hard filter. Only score feasible actions. Useful components might include resource activation cost, residual-capacity cost, waiting or lateness cost, rejection or unserved-work cost, and fragmentation or stranded-capacity cost.
Unrelated Example
In delivery planning, the shortest route is not always best. Suppose route distance has weight 1, but opening a new vehicle has weight 100. Sending a package on an already-open vehicle with 5 extra miles may be better than opening a new vehicle with only 1 extra mile:
weighted score =
distance_weight * extra_distance
+ vehicle_weight * new_vehicle_usedThe correct decision compares the weighted score, not distance alone.
Practical Guidance
- Use only information present in the current observation.
- Prefer deterministic tie-breaking so repeated runs are reproducible.
- If no feasible action exists, defer or reject rather than guessing.
- Validate the complete action list, not just each action in isolation.
© benchflow-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in tasks-extra/gpu-cluster-online-scheduling/environment/skills/online-resource-scheduling of benchflow-ai/skillsbench.
Open the folder on GitHubat commit 9a1f4dd
Compare with similar skills
Online Resource Scheduling next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Online Resource Scheduling this skillbenchflow-ai/skillsbench | 1.8k | — | ~761 | Automated safety check: Pass | Apache-2.0 | |
| Scheduleasgeirtj/system_prompts_leaks | 69k | — | ~2.9k | Automated safety check: Pass | CC0-1.0 | |
| Scheduleasgeirtj/system_prompts_leaks | 69k | — | ~597 | Automated safety check: Pass | CC0-1.0 | |
| Implementing Policy As Code With Open Policy Agentmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~2.6k | Automated safety check: Notes | Apache-2.0 | |
| Langsmith ObservabilityOrchestra-Research/AI-Research-SKILLs | 13k | 2 repos | ~2.4k | Automated safety check: Pass | MIT | |
| ObservabilityBuilderIO/agent-native | 7.1k | — | ~7.3k | Automated safety check: Pass | None |
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Questions about Online Resource Scheduling
What does Online Resource Scheduling do?
Design deterministic online scheduling policies from current observations. Online Resource Scheduling is an agent skill from benchflow-ai/skillsbench. Design deterministic online scheduling policies from current observations.
When should I use Online Resource Scheduling?
Online Resource Scheduling fits situations like: assigning arriving work to limited resources without seeing future requests.
How do I install Online Resource Scheduling in Claude Code?
Run `npx skills add benchflow-ai/skillsbench --skill online-resource-scheduling -a claude-code`. Or copy the skill folder (tasks-extra/gpu-cluster-online-scheduling/environment/skills/online-resource-scheduling in benchflow-ai/skillsbench) into .claude/skills/online-resource-scheduling in your project. Claude Code loads it when a task matches its description.
How do I install Online Resource Scheduling in Codex?
Run `npx skills add benchflow-ai/skillsbench --skill online-resource-scheduling -a codex`. Or copy the skill folder (tasks-extra/gpu-cluster-online-scheduling/environment/skills/online-resource-scheduling in benchflow-ai/skillsbench) into .agents/skills/online-resource-scheduling in your project. Codex loads it when a task matches its description.
Can I use Online Resource Scheduling in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add benchflow-ai/skillsbench --skill online-resource-scheduling -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/online-resource-scheduling, .gemini/skills/online-resource-scheduling, .github/skills/online-resource-scheduling and .opencode/skills/online-resource-scheduling in your project.
What does Online Resource Scheduling need to run?
SKILL.md names no scripts, command-line tools or credentials: Online Resource Scheduling is instructions for the agent only.
Does Online Resource Scheduling access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Online Resource Scheduling safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Online Resource Scheduling use?
Online Resource Scheduling is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Online Resource Scheduling use?
About 761 tokens (SKILL.md is roughly 3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Online Resource Scheduling?
Skills that share tags, products or a category with Online Resource Scheduling: Schedule (asgeirtj/system_prompts_leaks, 69k stars), Schedule (asgeirtj/system_prompts_leaks, 69k stars), Implementing Policy As Code With Open Policy Agent (mukul975/Anthropic-Cybersecurity-Skills, 34k stars) and Langsmith Observability (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Online Resource Scheduling?
benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,834 GitHub stars. The repository holds 189 skills in this directory. The repository was last updated on July 23, 2026.
Source: benchflow-ai/skillsbench on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.