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 skills/anilcancakir/claude-code-plugin/command-creatornpx skills add anilcancakir/claude-code-plugin --skill command-creatorgit clone --depth 1 https://github.com/anilcancakir/claude-code-pluginWhat 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 | $0.00030 | $0.01428 |
| Opus 5 | $0.00015 | $0.00714 |
| Sonnet 5 | $0.00006 | $0.00286 |
| Haiku 4.5 | $0.00003 | $0.00143 |
Grade A, and why
command-creator 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Claude Code commands — user-invocable workflows that orchestrate agents, interview users, and delegate complex work through structured phases.
Core Process
1. Classify Command Scope
Determine structure before drafting:
- How many phases? Simple commands (1-2 phases) vs orchestration commands (5+ phases).
- Does the command need subagents, or can it run entirely in the main context with Read/Glob/Grep/Bash? Default to main context; only add Agent delegation when the work must run in a fresh context or with a different model.
- Interactive (AskUserQuestion gates) or autonomous (runs end-to-end without prompts)?
- What approval gates are needed before destructive or irreversible actions?
2. Research Existing Commands
Use Glob + Read directly to find similar commands in the target project:
- Glob
**/commands/*.md, read 2-3 representative files - Note phase structure, frontmatter fields, and whether they use subagents or direct tools
3. Dedup Audit
Read ${CLAUDE_SKILL_DIR}/../prompt-writer/references/cc-dedup-guide.md before drafting.
Key dedup rules for commands:
- Commands receive CLAUDE.md automatically — do not re-state its global directives.
- Do not repeat Intent Gate, Delegation Check, or barrier semantics — these load every message.
- Do not re-explain agent model routing rules already in global CLAUDE.md.
- Commands specify WHAT to do (which agents, which phases) not HOW CC should behave.
4. Draft Command
Follow phase-based structure from references/command-patterns.md. Each phase uses:
## Phase N: Name
**Goal**: One sentence — what this phase achieves.
**Actions**:
1. First action
2. Second action
3. Third action (with sub-steps if needed)
Rules:
- Phases must be sequential — later phases depend on earlier ones.
- Insert AskUserQuestion approval gates before destructive actions.
- Delegate heavy work to agents — commands orchestrate, agents execute.
- Reference
$ARGUMENTSfor user-supplied input. - Use
${CLAUDE_PLUGIN_ROOT}for paths to bundled templates.
5. Frontmatter
---
description: "Short, action-oriented. What does this command do? ≤250 chars."
argument-hint: "[expected-input]"
effort: low | medium | high
---
Notes:
- Do not declare
allowed-tools,disallowedTools, ormodel. CC ignores them at runtime for user-level plugin components; tool access flows from session permissions. - Always include
AskUserQuestionandAgentif the command interviews users or delegates to agents. effortsignals expected token budget:low(single task, <5 phases),medium(multi-agent, 5-7 phases),high(full orchestration, 7+ phases).- Do not add
modelto command frontmatter — commands inherit the session model. Only agents have model routing.
6. Review
Present draft. Verify before finalizing:
- Phases sequential? No phase assumes work from a later phase.
- Approval gates present before destructive or irreversible actions?
- Agent delegation clear? Each Agent call has TASK + EXPECTED OUTCOME + MUST DO + MUST NOT DO + CONTEXT.
- Error handling covers: missing input, agent failure, no changes found, user cancellation.
$ARGUMENTSreferenced at the right phase (usually Phase 1)?- No ineffective frontmatter declared (
model,allowed-tools,disallowedTools)?
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.
- 2d ago First seen · 170 lines · 30 tokens per session scan A 0f7c52e015bb
command-creator is a skill published in the GitHub repository anilcancakir/claude-code-plugin (2 stars, last pushed 4mo ago), licensed MIT. It adds 30 tokens to every session and 1,428 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.
Other skills, from other repositories
agent-framework-py-release
Use when cutting a Python release for the microsoft/agent-framework monorepo. Triggers on "bump py versions", "cut a python release", "prepare release PR for python", "release py packages", "bump python to X.Y.Z", or similar requests to bump Python package versions and prepare a release PR. Handles all four lifecycle…
python-package-management
Guide for managing packages in the Agent Framework Python monorepo, including creating new connector packages, versioning, and the lazy-loading pattern. Use this when adding, modifying, or releasing packages.
foundry-hosted-agent-validation
Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.
python-feature-lifecycle
Guidance for package and feature lifecycle in the Agent Framework Python codebase, including stage meanings, feature-stage decorators, feature enums, and how to move APIs from one stage to the next.
build-and-test
How to build and test .NET projects in the Agent Framework repository. Use this when verifying or testing changes.
python-code-quality
Code quality checks, linting, formatting, and type checking commands for the Agent Framework Python codebase. Use this when running checks, fixing lint errors, or troubleshooting CI failures.