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/holymonkey/youtube-example-ai-studio/estimatenpx skills add HolyMonkey/youtube-example-ai-studio --skill estimategit clone --depth 1 https://github.com/HolyMonkey/youtube-example-ai-studioWrote 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/skills/holymonkey/youtube-example-ai-studio/estimate)<a href="https://agentmods.dev/skills/holymonkey/youtube-example-ai-studio/estimate"><img src="https://agentmods.dev/badge/skills/holymonkey/youtube-example-ai-studio/estimate.svg" alt="Measured on agentmods" 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 | $0.00028 | $0.01497 |
| Opus 5 | $0.00014 | $0.00749 |
| Sonnet 5 | $0.00006 | $0.00299 |
| Haiku 4.5 | $0.00003 | $0.00150 |
Grade A, and why
estimate 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 5d 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.
This is a copy
100% identical to estimate — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When this skill is invoked:
-
Read the task description from the argument. If the description is too vague to estimate meaningfully, ask for clarification before proceeding.
-
Read CLAUDE.md for project context: tech stack, coding standards, architectural patterns, and any estimation guidelines.
-
Read relevant design documents from
design/gdd/if the task relates to a documented feature or system. -
Scan the codebase to understand the systems affected by this task:
- Identify files and modules that would need to change
- Assess the complexity of those files (size, dependency count, cyclomatic complexity)
- Identify integration points with other systems
- Check for existing test coverage in the affected areas
-
Read past sprint data from
production/sprints/if available:- Look for similar completed tasks and their actual effort
- Calculate historical velocity (planned vs actual)
- Identify any estimation bias patterns (consistently over or under)
-
Analyze the following factors:
Code Complexity:
- Lines of code in affected files
- Number of dependencies and coupling level
- Whether this touches core/engine code vs leaf/feature code
- Whether existing patterns can be followed or new patterns are needed
Scope:
- Number of systems touched
- New code vs modification of existing code
- Amount of new test coverage required
- Data migration or configuration changes needed
Risk:
- New technology or unfamiliar libraries
- Unclear or ambiguous requirements
- Dependencies on unfinished work
- Cross-system integration complexity
- Performance sensitivity
-
Generate the estimate:
## Task Estimate: [Task Name]
Generated: [Date]
### Task Description
[Restate the task clearly in 1-2 sentences]
### Complexity Assessment
| Factor | Assessment | Notes |
|--------|-----------|-------|
| Systems affected | [List] | [Core, gameplay, UI, etc.] |
| Files likely modified | [Count] | [Key files listed below] |
| New code vs modification | [Ratio, e.g., 70% new / 30% modification] | |
| Integration points | [Count] | [Which systems interact] |
| Test coverage needed | [Low / Medium / High] | [Unit, integration, manual] |
| Existing patterns available | [Yes / Partial / No] | [Can follow existing code or new ground] |
**Key files likely affected:**
- `[path/to/file1]` -- [what changes here]
- `[path/to/file2]` -- [what changes here]
- `[path/to/file3]` -- [what changes here]
### Effort Estimate
| Scenario | Days | Assumption |
|----------|------|------------|
| Optimistic | [X] | Everything goes right, no surprises, requirements are clear |
| Expected | [Y] | Normal pace, minor issues, one round of review feedback |
| Pessimistic | [Z] | Significant unknowns surface, blocked for a day, requirements change |
**Recommended budget: [Y days]**
[If historical data is available: "Based on [N] similar tasks that averaged
[X] days actual vs [Y] days estimated, a [correction factor] adjustment has
been applied."]
### Confidence: [High / Medium / Low]
**High** -- Clear requirements, familiar systems, follows existing patterns,
similar tasks completed before.
**Medium** -- Some unknowns, touches moderately complex systems, partial
precedent from previous work.
**Low** -- Significant unknowns, new technology, unclear requirements, or
cross-cutting concerns across many systems.
[Explain which factors drive the confidence level for this specific task.]
### Risk Factors
| Risk | Likelihood | Impact | Mitigation |
|------|-----------|--------|------------|
| [Specific risk] | [High/Med/Low] | [Days added if realized] | [How to reduce] |
| [Another risk] | [Likelihood] | [Impact] | [Mitigation] |
### Dependencies
| Dependency | Status | Impact if Delayed |
|-----------|--------|-------------------|
| [What must be done first] | [Done / In Progress / Not Started] | [How it affects this task] |
### Suggested Breakdown
| # | Sub-task | Estimate | Notes |
|---|----------|----------|-------|
| 1 | [Research / spike] | [X days] | [If unknowns need investigation first] |
| 2 | [Core implementation] | [X days] | [The main work] |
| 3 | [Integration with system X] | [X days] | [Connecting to existing code] |
| 4 | [Testing and validation] | [X days] | [Writing tests, manual verification] |
| 5 | [Code review and iteration] | [X days] | [Review feedback, fixes] |
| | **Total** | **[Y days]** | |
### Historical Comparison
[If similar tasks exist in sprint history:]
| Similar Task | Estimated | Actual | Relevant Difference |
|-------------|-----------|--------|-------------------|
| [Past task 1] | [X days] | [Y days] | [What makes it similar/different] |
| [Past task 2] | [X days] | [Y days] | [What makes it similar/different] |
### Notes and Assumptions
- [Key assumption that affects the estimate]
- [Another assumption]
- [Any caveats about scope boundaries -- what is included vs excluded]
- [Recommendations: e.g., "Consider a spike first if requirement X is unclear"]
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.
- 5d ago First seen · 164 lines · 28 tokens per session scan A 4b40cd30a3e8
estimate is a skill published in the GitHub repository HolyMonkey/youtube-example-ai-studio (11 stars, last pushed 5mo ago), licensed MIT. It adds 28 tokens to every session and 1,497 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to estimate, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…