SuperClaude Framework is a configuration framework that organizes Claude Code into a structured development environment with specialized commands, AI agents, behavioral modes, and integrations. It is for developers who want guided workflows covering activities from brainstorming through deployment. The catalogue entries are its commands, agents, skills, instructions, integrations, settings, hooks, and plugin components.
Borrowing it
Nothing to install: this file belongs to SuperClaude-Org/SuperClaude_Framework. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/SuperClaude-Org/SuperClaude_Framework/master/.claude/skills/confidence-check/SKILL.mdgit clone --depth 1 https://github.com/SuperClaude-Org/SuperClaude_FrameworkWrote 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/superclaude-org/superclaude_framework/confidence-check)<a href="https://agentmods.dev/skills/superclaude-org/superclaude_framework/confidence-check"><img src="https://agentmods.dev/badge/skills/superclaude-org/superclaude_framework/confidence-check.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 5.1 | $0.00041 | $0.00759 |
| Opus 5 | $0.00020 | $0.00380 |
| Sonnet 5 | $0.00008 | $0.00152 |
| Haiku 4.5 | $0.00004 | $0.00076 |
Grade A, and why
Confidence Check 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 7d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Confidence Check Skill
Purpose
Prevents wrong-direction execution by assessing confidence BEFORE starting implementation.
Requirement: ≥90% confidence to proceed with implementation.
Test Results (2025-10-21):
- Precision: 1.000 (no false positives)
- Recall: 1.000 (no false negatives)
- 8/8 test cases passed
When to Use
Use this skill BEFORE implementing any task to ensure:
- No duplicate implementations exist
- Architecture compliance verified
- Official documentation reviewed
- Working OSS implementations found
- Root cause properly identified
Confidence Assessment Criteria
Calculate confidence score (0.0 - 1.0) based on 5 checks:
1. No Duplicate Implementations? (25%)
Check: Search codebase for existing functionality
# Use Grep to search for similar functions
# Use Glob to find related modules
✅ Pass if no duplicates found ❌ Fail if similar implementation exists
2. Architecture Compliance? (25%)
Check: Verify tech stack alignment
- Read
CLAUDE.md,PLANNING.md - Confirm existing patterns used
- Avoid reinventing existing solutions
✅ Pass if uses existing tech stack (e.g., Supabase, UV, pytest) ❌ Fail if introduces new dependencies unnecessarily
3. Official Documentation Verified? (20%)
Check: Review official docs before implementation
- Use Context7 MCP for official docs
- Use WebFetch for documentation URLs
- Verify API compatibility
✅ Pass if official docs reviewed ❌ Fail if relying on assumptions
4. Working OSS Implementations Referenced? (15%)
Check: Find proven implementations
- Use Tavily MCP or WebSearch
- Search GitHub for examples
- Verify working code samples
✅ Pass if OSS reference found ❌ Fail if no working examples
5. Root Cause Identified? (15%)
Check: Understand the actual problem
- Analyze error messages
- Check logs and stack traces
- Identify underlying issue
✅ Pass if root cause clear ❌ Fail if symptoms unclear
Confidence Score Calculation
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.
- 7d ago First seen · 126 lines · 41 tokens per session scan A 98486c22d13d
Confidence Check is a skill published in the GitHub repository SuperClaude-Org/SuperClaude_Framework (23,871 stars, last pushed 16d ago), licensed MIT. It adds 41 tokens to every session and 759 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 2026-08-30.
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