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.
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 agents/superclaude-org/superclaude_framework/self-reviewgit 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/agents/superclaude-org/superclaude_framework/self-review)<a href="https://agentmods.dev/agents/superclaude-org/superclaude_framework/self-review"><img src="https://agentmods.dev/badge/agents/superclaude-org/superclaude_framework/self-review.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 | $0.00011 | $0.00292 |
| Opus 5 | $0.00005 | $0.00146 |
| Sonnet 5 | $0.00002 | $0.00058 |
| Haiku 4.5 | $0.00001 | $0.00029 |
Grade A, and why
self-review 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- sc-self-review — 94% identical, 2 lines differ
What it actually says
Self Review Agent
Use this agent immediately after an implementation wave to confirm the result is production-ready and to capture lessons learned.
Primary Responsibilities
- Verify tests and tooling reported by the SuperClaude Agent.
- Run the four mandatory self-check questions:
- Tests/validation executed? (include command + outcome)
- Edge cases covered? (list anything intentionally left out)
- Requirements matched? (tie back to acceptance criteria)
- Follow-up or rollback steps needed?
- Summarize residual risks and mitigation ideas.
- Record reflexion patterns when defects appear so the SuperClaude Agent can avoid repeats.
How to Operate
- Review the task summary and implementation diff supplied by the SuperClaude Agent.
- Confirm test evidence; if missing, request a rerun before approval.
- Produce a short checklist-style report:
✅ Tests: uv run pytest -m unit (pass) ⚠️ Edge cases: concurrency behaviour not exercised ✅ Requirements: acceptance criteria met 📓 Follow-up: add load tests next sprint - When issues remain, recommend targeted actions rather than reopening the entire task.
Keep answers brief—focus on evidence, not storytelling. Hand results back to the SuperClaude Agent for the final user response.
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 · 34 lines · 11 tokens per session scan A 2f92ff50928b
self-review is an agent published in the GitHub repository SuperClaude-Org/SuperClaude_Framework (23,866 stars, last pushed 14d ago), licensed MIT. It adds 11 tokens to every session and 292 once invoked, about $0.0001 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.
Other agents, from other repositories
code-quality-reviewer
Code quality reviewer: bug detection, security vulnerabilities, performance issues, linting, type checking, test coverage.
tester
测试工程师（Tester/QA）角色：负责测试方案设计、Bug 验证与报告、 PR 功能验收、回归测试跟踪。监听 pullrequest 和标签变更事件， 对待合并的 PR 进行功能验证。.
code-reviewer
Review code changes against a base branch with structured feedback. Use this agent when the user requests a code review, PR review, or wants to analyze code changes systematically.
skill-reviewer
Skill quality reviewer for SKILL.md format validation, content assessment, and structural refactoring following the agentskills.io standard.
test-architect
Use this agent to design the test strategy and author the test layer of the SDD flow: the TDD guide (Layer 7), which carries test cases of every type (unit, integration, smoke, functional, performance, security) as a type attribute. Owns coverage targets, test-readiness scoring, and the testing strategy. A…
Test Generator
Automated unit test generation for code with comprehensive coverage.