Clade Cost Tuning
jeremylongshore/tons-of-skills-marketplace
Optimize Anthropic API costs — model selection, prompt caching, batches, Use when working with cost-tuning patterns.
Selecting MPC prediction horizon and cost matrices for web handling.
Apache-2.0Auto-check passed
Install Mpc Horizon Tuning
$ npx skills add benchflow-ai/skillsbench --skill mpc-horizon-tuning -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install benchflow-ai/skillsbench mpc-horizon-tuning --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/r2r-mpc-control/environment/skills/mpc-horizon-tuning .claude/skills/mpc-horizon-tuning && 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 "mpc-horizon-tuning" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/r2r-mpc-control/environment/skills/mpc-horizon-tuning into .claude/skills/mpc-horizon-tuning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mpc-horizon-tuning", 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/r2r-mpc-control/environment/skills/mpc-horizon-tuningType 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 mpc-horizon-tuning -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install benchflow-ai/skillsbench mpc-horizon-tuning --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/r2r-mpc-control/environment/skills/mpc-horizon-tuning .agents/skills/mpc-horizon-tuning && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "mpc-horizon-tuning" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/r2r-mpc-control/environment/skills/mpc-horizon-tuning into .agents/skills/mpc-horizon-tuning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mpc-horizon-tuning", 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 mpc-horizon-tuning -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install benchflow-ai/skillsbench mpc-horizon-tuning --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/r2r-mpc-control/environment/skills/mpc-horizon-tuning .cursor/skills/mpc-horizon-tuning && 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 "mpc-horizon-tuning" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/r2r-mpc-control/environment/skills/mpc-horizon-tuning into .cursor/skills/mpc-horizon-tuning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mpc-horizon-tuning", 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/r2r-mpc-control/environment/skills/mpc-horizon-tuning--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 mpc-horizon-tuning -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install benchflow-ai/skillsbench mpc-horizon-tuning --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/r2r-mpc-control/environment/skills/mpc-horizon-tuning .gemini/skills/mpc-horizon-tuning && 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 "mpc-horizon-tuning" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/r2r-mpc-control/environment/skills/mpc-horizon-tuning into .gemini/skills/mpc-horizon-tuning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mpc-horizon-tuning", 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 mpc-horizon-tuningInstalls 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 mpc-horizon-tuning -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/r2r-mpc-control/environment/skills/mpc-horizon-tuning .github/skills/mpc-horizon-tuning && 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 "mpc-horizon-tuning" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/r2r-mpc-control/environment/skills/mpc-horizon-tuning into .github/skills/mpc-horizon-tuning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mpc-horizon-tuning", 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 mpc-horizon-tuning -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 mpc-horizon-tuning --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/r2r-mpc-control/environment/skills/mpc-horizon-tuning .opencode/skills/mpc-horizon-tuning && 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 "mpc-horizon-tuning" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/r2r-mpc-control/environment/skills/mpc-horizon-tuning into .opencode/skills/mpc-horizon-tuning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mpc-horizon-tuning", 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
mpc-horizon-tuning- GitHub stars
- 1.8k
- Token cost
- ~298 tokens
- SKILL.md length
- 97 words
- Files
- 1
- Skills in repo
- 189
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Selecting MPC prediction horizon and cost matrices for web handling.
- SKILL.md covers Prediction Horizon Selection, Cost Matrix Design, Trade-offs and Terminal Cost
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Mpc Horizon Tuning is an agent skill from benchflow-ai/skillsbench. Selecting MPC prediction horizon and cost matrices for web handling.
Its SKILL.md is about 300 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.
Example prompts
- “/mpc-horizon-tuning”
Requirements
- Python 3
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 (its code samples are python).
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
Mpc Horizon Tuning loads about 298 tokens when it runs. Until then it costs about 22 tokens; SKILL.md has 97 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). 97 words, ~298 tokens.
Download SKILL.mdSave it as
.claude/skills/mpc-horizon-tuning/SKILL.md (or your agent's skills folder).- name
- mpc-horizon-tuning
- description
- Selecting MPC prediction horizon and cost matrices for web handling.
MPC Tuning for Tension Control
Prediction Horizon Selection
Horizon N affects performance and computation:
- Too short (N < 5): Poor disturbance rejection
- Too long (N > 20): Excessive computation
- Rule of thumb: N ≈ 2-3× settling time / dt
For R2R systems with dt=0.01s: N = 5-15 typical
Cost Matrix Design
State cost Q: Emphasize tension tracking
Q_tension = 100 / T_ref² # High weight on tensions
Q_velocity = 0.1 / v_ref² # Lower weight on velocities
Q = diag([Q_tension × 6, Q_velocity × 6])Control cost R: Penalize actuator effort
R = 0.01-0.1 × eye(n_u) # Smaller = more aggressiveTrade-offs
| Higher Q | Effect |
|---|---|
| Faster tracking | More control effort |
| Lower steady-state error | More aggressive transients |
| Higher R | Effect |
|---|---|
| Smoother control | Slower response |
| Less actuator wear | Higher tracking error |
Terminal Cost
Use LQR solution for terminal cost to guarantee stability:
P = solve_continuous_are(A, B, Q, R)© 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/r2r-mpc-control/environment/skills/mpc-horizon-tuning of benchflow-ai/skillsbench.
Open the folder on GitHubat commit 9a1f4dd
Compare with similar skills
Mpc Horizon Tuning 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 |
|---|---|---|---|---|---|---|
| Mpc Horizon Tuning this skillbenchflow-ai/skillsbench | 1.8k | — | ~298 | Automated safety check: Pass | Apache-2.0 | |
| Clade Cost Tuningjeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Supabase Cost Tuningjeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~2k | Automated safety check: Pass | MIT | |
| Langfuse Cost Tuningjeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~2.4k | Automated safety check: Pass | MIT | |
| Cost Trackingaffaan-m/ECC | 276k | 1 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Anth Cost Tuningjeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~2.1k | Automated safety check: Pass | MIT |
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Cost Tracking
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Track and report Claude Code token usage, spending, and budgets from the local ECC cost-tracker metrics log.
Anth Cost Tuning
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Questions about Mpc Horizon Tuning
What does Mpc Horizon Tuning do?
Selecting MPC prediction horizon and cost matrices for web handling. Mpc Horizon Tuning is an agent skill from benchflow-ai/skillsbench. Selecting MPC prediction horizon and cost matrices for web handling.
How do I install Mpc Horizon Tuning in Claude Code?
Run `npx skills add benchflow-ai/skillsbench --skill mpc-horizon-tuning -a claude-code`. Or copy the skill folder (tasks/r2r-mpc-control/environment/skills/mpc-horizon-tuning in benchflow-ai/skillsbench) into .claude/skills/mpc-horizon-tuning in your project. Claude Code loads it when a task matches its description.
How do I install Mpc Horizon Tuning in Codex?
Run `npx skills add benchflow-ai/skillsbench --skill mpc-horizon-tuning -a codex`. Or copy the skill folder (tasks/r2r-mpc-control/environment/skills/mpc-horizon-tuning in benchflow-ai/skillsbench) into .agents/skills/mpc-horizon-tuning in your project. Codex loads it when a task matches its description.
Can I use Mpc Horizon Tuning 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 mpc-horizon-tuning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mpc-horizon-tuning, .gemini/skills/mpc-horizon-tuning, .github/skills/mpc-horizon-tuning and .opencode/skills/mpc-horizon-tuning in your project.
What does Mpc Horizon Tuning need to run?
SKILL.md names no scripts, command-line tools or credentials: Mpc Horizon Tuning is instructions for the agent only. Our summary lists: Python 3.
Does Mpc Horizon Tuning 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 Mpc Horizon Tuning 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 Mpc Horizon Tuning use?
Mpc Horizon Tuning 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 Mpc Horizon Tuning use?
About 298 tokens (SKILL.md is roughly 1.2k 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 Mpc Horizon Tuning?
Skills that share tags, products or a category with Mpc Horizon Tuning: Clade Cost Tuning (jeremylongshore/tons-of-skills-marketplace, 2.8k stars), Supabase Cost Tuning (jeremylongshore/tons-of-skills-marketplace, 2.8k stars), Langfuse Cost Tuning (jeremylongshore/tons-of-skills-marketplace, 2.8k stars) and Cost Tracking (affaan-m/ECC, 276k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Mpc Horizon Tuning?
benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,835 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.