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 skills add cenconq25/claude-code-app-studio --skill estimategit clone --depth 1 https://github.com/cenconq25/claude-code-app-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/cenconq25/claude-code-app-studio/estimate)<a href="https://agentmods.dev/skills/cenconq25/claude-code-app-studio/estimate"><img src="https://agentmods.dev/badge/skills/cenconq25/claude-code-app-studio/estimate/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/skills/cenconq25/claude-code-app-studio/estimate"><img src="https://agentmods.dev/badge/skills/cenconq25/claude-code-app-studio/estimate.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 | $0.00048 | $0.01899 |
| Opus 5.5 | $0.00019 | $0.00760 |
| Sonnet 5.5 | $0.00010 | $0.00380 |
| Haiku 4.5 | $0.00005 | $0.00190 |
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 1mo 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 — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Estimate
Estimation in mobile work is risky because framework knowledge gaps, platform divergence, and store review cycles inflate timelines. This skill produces estimates with explicit reasoning and confidence levels, not a single number.
Read-only.
Purpose / When to Run
Run when:
- Sizing a story before adding to a sprint
- Sizing an epic before committing to a milestone
- A new feature request needs a rough budget before deciding go / no-go
Inputs
- A story path, epic slug, or glob of stories
- Recent sprint plans (for velocity baseline)
- Story files in scope (for complexity analysis)
Outputs
- Printed estimate with low / mid / high bounds, confidence, and assumptions
Phase 1: Resolve Scope
Argument forms:
story <path>— one storyepic <slug>— all stories under that epicbatch <glob>— explicit list
If no argument, ask:
- Prompt: "Estimate what?"
- Options:
Single story,Whole epic,A list of stories,A new feature with no stories yet
For "feature with no stories yet", switch to T-shirt mode (Phase 4 alternate).
Phase 2: Establish Velocity Baseline
Read the last 2-3 sprint plans. From each:
- Stories planned vs. completed
- Capacity in dev-days
- Average story-days
Compute the rolling average story-day cost. If sprints don't exist yet, use the default heuristic:
- Logic story: 0.5-1 day
- Integration story: 1-2 days
- UI story: 1-2 days
- Visual / Feel story: 1-3 days
- Platform story (push, biometric, IAP, deep links): 2-4 days
- Config / Data story: 0.25-0.5 day
Document the baseline source ("Computed from sprints 001-003" or "Heuristic — no sprint history").
Phase 3: Per-Story Complexity Analysis
For each story in scope, read it and score:
Complexity factors (each adds time)
- Type:
- Logic: base
- Integration: +50%
- UI: base or +25% if novel
- Visual: +25-50%
- Platform: +50-100%
- Config: -50%
- Framework risk (from story header):
- LOW: 0
- MEDIUM: +25%
- HIGH: +50% (verification work)
- ADR status:
- Accepted: 0
- Proposed: +100% (risk that decision changes mid-implementation, or block)
- UX spec maturity (UI stories):
- Reviewed: 0
- Authored not reviewed: +25%
- Missing: BLOCKED — can't estimate
- Acceptance criteria count:
- 1-3: 0
- 4-6: +25%
- 7+: +50% (story may be too big — recommend split)
- Cross-platform: if both iOS and Android target → +25-50%
- Permission flow: any story that asks for permissions → +0.25 day for denied-state UX
- Backend integration: if real backend not yet ready → +50% for stub/mock work
- Store-review-affecting: any IAP, deletion, or privacy change → +0.5 day for review-prep tasks
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.
- 1mo ago First seen · 224 lines · 48 tokens per session scan A d65c838bc8cf
estimate is a skill published in the GitHub repository cenconq25/claude-code-app-studio (42 stars, last pushed 5mo ago), licensed MIT. It adds 48 tokens to every session and 1,899 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-03.
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