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/leanandmean/mach10/issue-assessmentgit clone --depth 1 https://github.com/LeanAndMean/mach10Wrote 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/leanandmean/mach10/issue-assessment)<a href="https://agentmods.dev/commands/leanandmean/mach10/issue-assessment"><img src="https://agentmods.dev/badge/commands/leanandmean/mach10/issue-assessment.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 | $0.00014 | $0.02167 |
| Opus 5 | $0.00007 | $0.01084 |
| Sonnet 5 | $0.00003 | $0.00433 |
| Haiku 4.5 | $0.00001 | $0.00217 |
Grade A, and why
issue-assessment 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Issue Assessment
You are performing an independent assessment of a GitHub issue. Your goal is to deeply understand the issue, explore the relevant codebase, and present your findings with a recommended next step.
User input: $ARGUMENTS
Note: This command performs best with an Opus-class model. On Sonnet or Haiku, results may be shallower.
Step 1: Parse Input
The user's input contains:
- An issue number (required)
- Additional context or constraints (optional)
Extract the issue number from the input. If the input is ambiguous, ask the user to clarify. If context was provided, note it for use in Steps 3-4.
Step 2: Read the Issue
Read the issue title and body:
gh issue view <issue-number>
Then read all comments (--comments returns only comments and silently drops the title and body, so both calls are required):
gh issue view <issue-number> --comments
Parse and understand:
- The problem statement
- Any constraints or requirements mentioned
- Prior discussion or decisions in the comments
- Acceptance criteria (if specified)
- Current state of the issue (open, closed, linked PRs, etc.)
Step 3: Explore the Codebase
Launch 5 exploration agents of type feature-dev:code-explorer in parallel by delegating to subagents. Each agent should trace through the code comprehensively and target a different aspect. All lenses are required -- Step 4 always evaluates risks and critical premises, so their corresponding evidence-gathering lenses must always run:
- Relevant code: Find existing code related to the issue. Trace through their implementation comprehensively, identifying patterns, conventions, and the design decisions that shaped them.
- Architecture: Map the relevant architecture layers, abstractions, and data flow, tracing through the code comprehensively to understand how components interact and where boundaries lie.
- Prior work: Check for related branches, PRs, or commits that may already address part of the issue. Trace through any partial implementations to assess their completeness and approach.
- Counter-evidence: Look for codebase evidence that challenges the issue's premise or proposed approach. Identify existing patterns, design decisions, or prior solutions that suggest a different approach, reveal the issue may be addressing symptoms rather than root causes, or indicate the problem is a special case of something more general.
- Constraints and edge cases: Investigate what could go wrong with the proposed approach. Look for failure modes, boundary conditions, implicit assumptions, and pitfalls in the affected code areas.
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 · 157 lines · 14 tokens per session scan A bf0380ff334b
issue-assessment is a command published in the GitHub repository LeanAndMean/mach10 (20 stars, last pushed 3mo ago), licensed MIT. It adds 14 tokens to every session and 2,167 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.
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