Borrowing it
Nothing to install: this file belongs to Vvkmnn/claude-augur-mcp. 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/Vvkmnn/claude-augur-mcp/main/.claude/skills/claude-augur/SKILL.mdgit clone --depth 1 https://github.com/Vvkmnn/claude-augur-mcpWrote 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/vvkmnn/claude-augur-mcp/claude-augur)<a href="https://agentmods.dev/skills/vvkmnn/claude-augur-mcp/claude-augur"><img src="https://agentmods.dev/badge/skills/vvkmnn/claude-augur-mcp/claude-augur/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/vvkmnn/claude-augur-mcp/claude-augur"><img src="https://agentmods.dev/badge/skills/vvkmnn/claude-augur-mcp/claude-augur.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 | $0.00045 | $0.00451 |
| Opus 5 | $0.00023 | $0.00226 |
| Sonnet 5 | $0.00009 | $0.00090 |
| Haiku 4.5 | $0.00005 | $0.00045 |
Grade A, and why
claude-augur 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 11d 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
Claude Augur
Surface plan reasoning as scannable inline abstracts. Decisions, tradeoffs, assumptions — visible at a glance.
When to Use
- After writing or editing a plan file
- When the user asks to explain or summarize a plan
- When the user asks "why did you choose this approach?"
- After plan mode exits with a new or updated plan
Quick Reference
| Tool | Purpose |
|---|---|
augur_explain |
Extract plan structure and return a template for inline rendering |
Workflow
- Write or edit a plan file
augur_explain(plan_path: "/path/to/plan.md")- MCP returns a one-line summary + template with
[FILL]markers - Render the filled template inline in your response (not in a code block)
Template Format
MCP pre-renders the header, purpose, and progress. You fill:
- Decisions —
✓ choice — reasonwith└ child — reasonfor sub-decisions - Assumptions —
? statement - Tradeoffs —
+for pro,−for con - Reasoning — 2-3 lines explaining WHY
Rules
- Every content line starts with
│ - ~60 chars max per line after
│ - Terse: verb phrases, no articles, no filler
- Render inline, never in a code block
- Never truncate the purpose or header
Common Mistakes
| Mistake | Fix |
|---|---|
| Rendering in a code block | Include the template directly in your response text |
| Skipping after plan edits | Always call after writing/editing plan files |
| Verbose fill content | Keep each line under ~60 chars, terse verb phrases |
| Truncating purpose | Purpose wraps fully, never truncated |
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.
- 11d ago First seen · 55 lines · 45 tokens per session scan A fdbfcad3f4c5
claude-augur is a skill published in the GitHub repository Vvkmnn/claude-augur-mcp (0 stars, last pushed 6mo ago), licensed MIT. It adds 45 tokens to every session and 451 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 2026-08-31.
Other skills, from other repositories
thinking-theory-of-constraints
When throughput or latency is pipeline-limited, identify the single binding constraint and exploit, subordinate, elevate, then recheck—ignore non-constraints.
Vizra ADK Memory System
Implement persistent memory, session context, and vector memory (RAG) for AI agents.
foundation-models
On-device LLM integration using Apple's Foundation Models framework. Use when implementing AI text generation, structured output, or tool calling.
analytics-interpretation
Interpret app metrics and make data-driven decisions. Covers DAU/MAU, retention, LTV, ARPU, App Store Connect analytics, AARRR funnel analysis, cohort analysis, and diagnostic decision trees. Use when user wants to understand their metrics, diagnose problems, or build a data-driven growth plan.
app-namer
Turn an app idea into validated, App-Store-ready name candidates. Use when the user says "name my app", "what should I call it", "app name ideas", "help me name this app", "is this name available", or needs to pick a brandable, ownable name before reserving it in App Store Connect.
in-app-events
Generates In-App Event metadata templates for App Store Connect — event names, descriptions, badge types, image specs, and deep link configuration. Use when creating events for App Store visibility, engagement campaigns, or seasonal promotions.