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/alpyenigun/eliniscan/scangit clone --depth 1 https://github.com/AlpYenigun/eliniscanWhat 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 | $0.00027 | $0.01168 |
| Opus 5 | $0.00014 | $0.00584 |
| Sonnet 5 | $0.00005 | $0.00234 |
| Haiku 4.5 | $0.00003 | $0.00117 |
Grade A, and why
eliniscan:scan scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CRITICAL: Before doing ANYTHING, you MUST ask the setup questions below. Do NOT start scanning until all questions are answered. Do NOT assume defaults — ASK the user.
<execution_context> @$HOME/.claude/eliniscan/workflows/scan.md </execution_context>
You MUST use AskUserQuestion or direct questions to get answers for ALL of these. Do NOT skip any. Do NOT assume defaults. Wait for user answers.
Display this header first:
═══════════════════════════════════════════════════
eliniscan — Full Codebase Scanner
═══════════════════════════════════════════════════
Then ask these questions ONE BY ONE:
Question 1 — Scan Depth:
Scan depth?
1. Full — Read every line of every file (thorough, slower)
2. Quick — Focus on critical patterns only (faster)
Question 2 — AI Model:
Which model should scan your files?
1. Opus — Most thorough, catches subtle issues (slower)
2. Sonnet — Balanced speed and quality (recommended)
3. Haiku — Fastest, may miss subtle issues
Question 3 — File Types:
Which file extensions to scan? (comma-separated)
Default: ts,tsx,js,jsx,css
Enter custom or press enter for default:
Question 4 — Exclude Directories:
Directories to exclude? (comma-separated)
Default: node_modules,.next,dist,build,.git
Enter additional or press enter for default:
Question 5 — Severity Filter:
Minimum severity to report?
1. All — Report everything (CRITICAL → INFO)
2. High — Only CRITICAL and HIGH
3. Critical — Only CRITICAL
After ALL questions are answered, display a summary:
Scan Config:
─────────────────────────────
Depth: {depth}
Model: {model}
File types: {types}
Exclude: {dirs}
Severity: {severity}
Starting scan...
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.
- 2d ago First seen · 150 lines · 27 tokens per session scan A b6b4cb5403f3
eliniscan:scan is a command published in the GitHub repository AlpYenigun/eliniscan (3 stars, last pushed 6d ago), licensed MIT. It adds 27 tokens to every session and 1,168 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
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
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.