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/smrafiz/claude-superchargerWrote 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/smrafiz/claude-supercharger/think)<a href="https://agentmods.dev/commands/smrafiz/claude-supercharger/think"><img src="https://agentmods.dev/badge/commands/smrafiz/claude-supercharger/think.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 | $0.00000 | $0.00282 |
| Opus 5 | $0.00000 | $0.00141 |
| Sonnet 5 | $0.00000 | $0.00056 |
| Haiku 4.5 | $0.00000 | $0.00028 |
Grade A, and why
think 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.
What it actually says
Structured reasoning for an ambiguous problem. Apply this process to: $ARGUMENTS
Work through each step before moving to the next.
Step 1 — Clarify Restate the core question in one sentence, stripped of assumptions. What is actually being asked?
Step 2 — Inventory What do we know for certain? What is assumed but unverified? What is unknown?
Step 3 — Hypotheses List 2–3 candidate explanations or approaches. Each must be distinct — not variations of the same idea.
Step 4 — Stress-test For each hypothesis: what evidence supports it? What single piece of evidence would disprove it?
Step 5 — Decide Pick one hypothesis or path. State why it beats the others. Name the biggest remaining risk.
Output format:
QUESTION: [restated in one sentence]
KNOWN: [bullet list]
UNKNOWN/ASSUMED: [bullet list]
HYPOTHESIS 1: [description]
Supports: [evidence]
Would disprove: [what would rule this out]
HYPOTHESIS 2: [description]
Supports: [evidence]
Would disprove: [what would rule this out]
VERDICT: [chosen path]
REASONING: [why this over the others]
TOP RISK: [what could still go wrong]
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 · 38 lines · 0 tokens per session scan A 6d14cae6f1e3
think is a command published in the GitHub repository smrafiz/claude-supercharger (11 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 282 tokens. 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.