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/raja21068/AutoResearchWrote 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/raja21068/autoresearch/quality-gate)<a href="https://agentmods.dev/commands/raja21068/autoresearch/quality-gate"><img src="https://agentmods.dev/badge/commands/raja21068/autoresearch/quality-gate/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/commands/raja21068/autoresearch/quality-gate"><img src="https://agentmods.dev/badge/commands/raja21068/autoresearch/quality-gate.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 | $0.00000 | $0.00155 |
| Opus 5.5 | $0.00000 | $0.00062 |
| Sonnet 5.5 | $0.00000 | $0.00031 |
| Haiku 4.5 | $0.00000 | $0.00015 |
Grade A, and why
quality-gate 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 1mo 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.
This is a copy
100% identical to quality-gate — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Quality Gate Command
Run the ECC quality pipeline on demand for a file or project scope.
Usage
/quality-gate [path|.] [--fix] [--strict]
- default target: current directory (
.) --fix: allow auto-format/fix where configured--strict: fail on warnings where supported
Pipeline
- Detect language/tooling for target.
- Run formatter checks.
- Run lint/type checks when available.
- Produce a concise remediation list.
Notes
This command mirrors hook behavior but is operator-invoked.
Arguments
$ARGUMENTS:
[path|.]optional target path--fixoptional--strictoptional
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.
- 1mo ago First seen · 30 lines · 0 tokens per session scan A 826bbb000043
quality-gate is a command published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 155 tokens. A static security scan graded it A with 0 findings. It is 100% identical to quality-gate, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
config-validate
Validate application configuration with schemas, per-environment rules, runtime checks, and secure handling of sensitive values.
smart-debug
AI-assisted smart debugging — parse error messages, stack traces, and failure patterns to identify root causes and produce a fix with automated observability steps.
solve-perplexity
Iteratively solve complex bugs, edge cases, and nearly-unsolvable issues using progressive Perplexity queries with contradiction tracking and convergence detection. Tier 2.5 in the escalation ladder — between single-pass /research-perplexity (Tier 2) and user handoff (Tier 3).
diagnose
Trace a failed or stalled pipeline run — identify which FORGE annotation is missing or malformed, diagnose the failure point, and suggest specific remediation steps.
incident-response
Coordinate P0 incident response — hotfix validation, timeline reconstruction, and post-incident analysis.
validate
Independently verify if a reported issue is actually a problem before making code changes.