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/rlaope/claude-code-savantWrote 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/rlaope/claude-code-savant/savant-consensus)<a href="https://agentmods.dev/commands/rlaope/claude-code-savant/savant-consensus"><img src="https://agentmods.dev/badge/commands/rlaope/claude-code-savant/savant-consensus.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 | $0.00014 | $0.00321 |
| Opus 5 | $0.00007 | $0.00161 |
| Sonnet 5 | $0.00003 | $0.00064 |
| Haiku 4.5 | $0.00001 | $0.00032 |
Grade A, and why
savant-consensus 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
Savant Consensus - Team Discussion Mode
$ARGUMENTS
Your Task
Gather opinions from all 4 genius personas (Shakespeare, Einstein, Socrates, Steve Jobs) and synthesize their insights into a unified consensus.
Execution
Use the MCP tool savant_consensus:
mcp__claude-code-savant__savant_consensus:
- question: [User's question or decision to analyze]
- code: [Code to analyze, if provided]
If no code is provided, ask the user to provide the code they want analyzed.
What This Does
- Shakespeare (The Bard) - Analyzes code structure and flow narratively
- Einstein (The Professor) - Provides first-principles analysis with complexity metrics
- Socrates (The Questioner) - Examines edge cases and potential issues
- Steve Jobs (The Visionary) - Offers simplification and direction insights
Then synthesizes all perspectives into:
- Points of agreement
- Key insights from each perspective
- Recommended actions
- Notes on differing views (if any)
When to Use
- Important architectural decisions
- Code review discussions
- "Should we refactor this?"
- "Is this design correct?"
- Any decision where multiple perspectives help
Example
User: "Should we refactor this authentication module?"
The consensus tool will gather all 4 perspectives and provide a unified recommendation.
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 · 51 lines · 14 tokens per session scan A a7e5235752ca
savant-consensus is a command published in the GitHub repository rlaope/claude-code-savant (2 stars, last pushed 6mo ago), licensed MIT. It adds 14 tokens to every session and 321 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 2026-08-31.
Other commands, from other repositories
checklist
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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.