TÂCHES Claude Code Resources is a collection of custom commands, skills, and agents that structure Claude Code workflows such as planning, debugging, automation, and subagent creation. It is intended for developers who use Claude Code for real software projects. The catalogue entries are examples of the resources included in the collection.
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.
git clone --depth 1 https://github.com/glittercowboy/taches-cc-resourcesWrote 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/commands/glittercowboy/taches-cc-resources/create-prompt)<a href="https://agentmods.dev/commands/glittercowboy/taches-cc-resources/create-prompt"><img src="https://agentmods.dev/badge/commands/glittercowboy/taches-cc-resources/create-prompt/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/commands/glittercowboy/taches-cc-resources/create-prompt"><img src="https://agentmods.dev/badge/commands/glittercowboy/taches-cc-resources/create-prompt.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00009 | $0.03730 |
| Opus 5 | $0.00005 | $0.01865 |
| Sonnet 5 | $0.00002 | $0.00746 |
| Haiku 4.5 | $0.00001 | $0.00373 |
Grade B, and why
create-prompt scanned grade B with 1 finding 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 9d 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.
Asks the agent to reveal its instructionsmediumSystem prompt leakage
Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.
- File output instructions using relative paths: `./filename` or `./subfolder/filename` How it starts
The opening of the file, as written. The whole thing — 469 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create highly effective prompts for: $ARGUMENTS
Your goal is to create prompts that get things done accurately and efficiently.
<step_0_intake_gate>
<critical_first_action> BEFORE analyzing anything, check if $ARGUMENTS contains a task description.
IF $ARGUMENTS is empty or vague (user just ran /create-prompt without details):
→ IMMEDIATELY use AskUserQuestion with:
- header: "Task type"
- question: "What kind of prompt do you need?"
- options:
- "Coding task" - Build, fix, or refactor code
- "Analysis task" - Analyze code, data, or patterns
- "Research task" - Gather information or explore options
After selection, ask: "Describe what you want to accomplish" (they select "Other" to provide free text).
IF $ARGUMENTS contains a task description: → Skip this handler. Proceed directly to adaptive_analysis. </critical_first_action>
<adaptive_analysis> Analyze the user's description to extract and infer:
- Task type: Coding, analysis, or research (from context or explicit mention)
- Complexity: Simple (single file, clear goal) vs complex (multi-file, research needed)
- Prompt structure: Single prompt vs multiple prompts (are there independent sub-tasks?)
- Execution strategy: Parallel (independent) vs sequential (dependencies)
- Depth needed: Standard vs extended thinking triggers
Inference rules:
- Dashboard/feature with multiple components → likely multiple prompts
- Bug fix with clear location → single prompt, simple
- "Optimize" or "refactor" → needs specificity about what/where
- Authentication, payments, complex features → complex, needs context </adaptive_analysis>
<contextual_questioning> Generate 2-4 questions using AskUserQuestion based ONLY on genuine gaps.
<question_templates>
For ambiguous scope (e.g., "build a dashboard"):
- header: "Dashboard type"
- question: "What kind of dashboard is this?"
- options:
- "Admin dashboard" - Internal tools, user management, system metrics
- "Analytics dashboard" - Data visualization, reports, business metrics
- "User-facing dashboard" - End-user features, personal data, settings
For unclear target (e.g., "fix the bug"):
- header: "Bug location"
- question: "Where does this bug occur?"
- options:
- "Frontend/UI" - Visual issues, user interactions, rendering
- "Backend/API" - Server errors, data processing, endpoints
- "Database" - Queries, migrations, data integrity
For auth/security tasks:
- header: "Auth method"
- question: "What authentication approach?"
- options:
- "JWT tokens" - Stateless, API-friendly
- "Session-based" - Server-side sessions, traditional web
- "OAuth/SSO" - Third-party providers, enterprise
For performance tasks:
- header: "Performance focus"
- question: "What's the main performance concern?"
- options:
- "Load time" - Initial render, bundle size, assets
- "Runtime" - Memory usage, CPU, rendering performance
- "Database" - Query optimization, indexing, caching
For output/deliverable clarity:
- header: "Output purpose"
- question: "What will this be used for?"
- options:
- "Production code" - Ship to users, needs polish
- "Prototype/POC" - Quick validation, can be rough
- "Internal tooling" - Team use, moderate polish
</question_templates>
<question_rules>
- Only ask about genuine gaps - don't ask what's already stated
- Each option needs a description explaining implications
- Prefer options over free-text when choices are knowable
- User can always select "Other" for custom input
- 2-4 questions max per round </question_rules> </contextual_questioning>
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.
- 9d ago First seen · 469 lines · 9 tokens per session scan B 2110e278322e
create-prompt is a command published in the GitHub repository glittercowboy/taches-cc-resources (1,975 stars, last pushed 5mo ago), licensed MIT. It adds 9 tokens to every session and 3,730 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it B with 1 finding (asks the agent to reveal its instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
report
Generate a project analytics report covering code quality, velocity, and health metrics.
explain
Explain a code file, function, or concept in clear, structured language.
env-setup
Set up environment configuration files from templates with validation.
debug-pod
Debug a failing or unhealthy Kubernetes pod by analyzing events, logs, and configuration.
document-all
Auto-document the entire codebase by generating module-level docs, function signatures, and API references.
fix-comments
Address PR review comments by implementing requested changes automatically.