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 commands/basher83/agent-auditor/intelligent-lintgit clone --depth 1 https://github.com/basher83/agent-auditorWrote 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/basher83/agent-auditor/intelligent-lint)<a href="https://agentmods.dev/commands/basher83/agent-auditor/intelligent-lint"><img src="https://agentmods.dev/badge/commands/basher83/agent-auditor/intelligent-lint.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 | $0.00009 | $0.00646 |
| Opus 5 | $0.00005 | $0.00323 |
| Sonnet 5 | $0.00002 | $0.00129 |
| Haiku 4.5 | $0.00001 | $0.00065 |
Grade A, and why
intelligent-lint 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 4d 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Intelligent Markdown Linting
Execute the intelligent markdown linting workflow using autonomous agents.
Workflow
This command orchestrates a three-layer agent system:
-
Orchestrator (scripts/intelligent-markdown-lint.py):
- Discovers and triages markdown linting errors
- Spawns Investigator for ambiguous errors
- Spawns Fixer for confirmed errors
- Verifies results
-
Investigator (.claude/agents/markdown-investigator.md):
- Autonomous analysis with Read, Grep, Glob, Bash tools
- Determines if errors are fixable or false positives
- Provides reasoning for each verdict
-
Fixer (.claude/agents/markdown-fixer.md):
- Executes fixes using investigation context
- Verifies changes with rumdl
- Reports results
Usage
/intelligent-lint
What This Does
- Runs
rumdl check .to find all markdown errors - Categorizes as simple (MD013, MD036, MD025) or ambiguous (MD033, MD053, MD052, MD041)
- Spawns Investigator subagent for ambiguous errors
- Aggregates results and calculates total workload
- Spawns Fixer subagent with error context
- Verifies fixes with final rumdl check
- Reports before/after statistics and fix rate
Expected Output
🚀 Intelligent Markdown Linting Orchestrator
============================================================
📋 Phase 1: Discovery & Triage
------------------------------------------------------------
Total errors found: [N]
├─ Simple (directly fixable): [N]
└─ Ambiguous (needs investigation): [N]
🔍 Phase 2: Investigation
------------------------------------------------------------
📊 Spawning Investigator subagent...
✅ Investigation complete: analyzed [N] files
📊 Phase 3: Calculate Workload
------------------------------------------------------------
Simple errors: [N]
Investigated fixable: [N]
False positives preserved: [N]
Total fixable: [N]
🔧 Phase 4: Fixing
------------------------------------------------------------
🔧 Spawning Fixer subagent...
✅ Fixes complete: processed [N] files
✅ Fixed [N] errors across [N] files
✅ Phase 5: Verification
------------------------------------------------------------
Errors before: [N]
Errors after: [N]
Fix rate: [N]%
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.
- 4d ago First seen · 97 lines · 9 tokens per session scan A b1392956ae6d
intelligent-lint is a command published in the GitHub repository basher83/agent-auditor (5 stars, last pushed 6mo ago), licensed MIT. It adds 9 tokens to every session and 646 once invoked, about $0.0000 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
git
Git operations with intelligent commit messages and workflow optimization.
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.