cgf-optimize
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- Author updated Live
- Author repo skills-registry
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- Other
- Compatible agents
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- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @tomevault-io · no license declared
- Token usage
- Lean
- Setup complexity
- Guided setup
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- Python
- Permissions
-
- Read-only
- Write / modify
- Shell exec
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: cgf-optimize
description: > Use when this capability is needed. This skill launches the CGF (Claude Gradient Feedback) opt…
category: other
runtime: Python
---
# cgf-optimize output preview
## PART A: Task fit
- Use case: > Use when this capability is needed. This skill launches the CGF (Claude Gradient Feedback) optimization pipeline for a specified resource or creates a new resource from description. runs entirely locally; runs on Python. Works with Claude Code, Cursor, Cline and 23 more..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Usage / Optimization Mode (Existing Resource) / Creation Mode (New Resource)” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “> Use when this capability is needed. This skill launches the CGF (Claude Gradient Feedback) optimization pipeline for a specified resource or creates a new resource from description. runs entirely locally; runs on Python. Works with Claude Code, Cursor, Cline and 23 more.”.
- **02** When the source has headings, the agent prioritizes “Usage / Optimization Mode (Existing Resource) / Creation Mode (New Resource)” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. The source mentions slash commands such as `/cgf-optimize`, `/cgf-create`; use them first when your agent supports command triggers.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files, run shell commands.
Start with a small task and check whether the result follows “Usage / Optimization Mode (Existing Resource) / Creation Mode (New Resource)”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: cgf-optimize
description: > Use when this capability is needed. This skill launches the CGF (Claude Gradient Feedback) opt…
category: other
source: tomevault-io/skills-registry
---
# cgf-optimize
## When to use
- > Use when this capability is needed. This skill launches the CGF (Claude Gradient Feedback) optimization pipeline for…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Usage / Optimization Mode (Existing Resource) / Creation Mode (New Resource)” and keep inference separate from source facts.
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "cgf-optimize" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Usage / Optimization Mode (Existing Resource) / Creation Mode (New Resource)
rules -> SKILL.md triggers / order / output contract
runtime -> Python | read files, write/modify files, run shell commands | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} CGF Optimize Skill
This skill launches the CGF (Claude Gradient Feedback) optimization pipeline for a specified resource or creates a new resource from description.
Usage
Optimization Mode (Existing Resource)
/cgf-optimize <resource> <optimization_goal> [--review]
Creation Mode (New Resource)
/cgf-create <description> [--review]
Arguments
For optimization mode:
resource: Resource identifier - can be:
- Agent name:
python-expert,refactor-agent - Namespaced agent:
research-team:research-specialist - Full path:
.claude/agents/dev-python-expert.md
- Agent name:
optimization_goal: What to optimize for:
async programmingbetter error handlingcode quality improvementsContext7 usage patterns
For creation mode:
description: Natural language description of the desired resource:
Python async expert that helps with asyncio patternsKubernetes deployment agent for managing k8s resourcesCode review skill for security-focused reviews
--review (optional): Enable checkpoint mode for human review at each phase
Examples
Basic Optimization
/cgf-optimize python-expert async programming
Runs full optimization pipeline automatically.
With Review Checkpoints
/cgf-optimize typescript-expert --review
Pauses after research, test generation, and evaluation for your review.
Plugin Agent
/cgf-optimize research-team:research-specialist Context7 integration
Optimizes a plugin agent.
Create New Agent
/cgf-create Python async expert that helps with asyncio patterns
Creates initial agent draft using context-engineer, then optimizes.
Create With Review
/cgf-create Kubernetes deployment agent --review
Creates and optimizes with human review at each phase.
Workflow
Optimization Mode
- INIT: Creates workspace, detects resource type
- RESEARCH: Investigates domain best practices (via research-team)
- RESEARCH_ITERATE: Agentic optimization using research findings and LLM self-critique
- EVALUATE: Assesses results, recommends accept/refine/reject
- FINALIZE: Applies recommendation
Creation Mode
- INIT: Creates workspace, detects creation mode
- CREATE: Spawns context-engineer to create initial resource draft
- RESEARCH: Investigates domain best practices
- RESEARCH_ITERATE: Agentic optimization using research findings and LLM self-critique
- EVALUATE: Assesses results, recommends accept/refine/reject
- FINALIZE: Applies recommendation
Output
Results saved to workspace/{resource_id}/:
run_state.json- Current state (supports resume){resource_id}-v{N}.md- Optimized versionreviews/v{N}_review.md- Evaluation report
Resume
If optimization was interrupted, simply re-run the same command - it will resume from the last checkpoint.
Source: andisab/casdk-harness — distributed by TomeVault.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review