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/DNYoussef/context-cascadeWrote 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/dnyoussef/context-cascade/quality-loop)<a href="https://agentmods.dev/commands/dnyoussef/context-cascade/quality-loop"><img src="https://agentmods.dev/badge/commands/dnyoussef/context-cascade/quality-loop/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/dnyoussef/context-cascade/quality-loop"><img src="https://agentmods.dev/badge/commands/dnyoussef/context-cascade/quality-loop.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 | $0.00005 | $0.00878 |
| Opus 5.5 | $0.00002 | $0.00351 |
| Sonnet 5.5 | $0.00001 | $0.00176 |
| Haiku 4.5 | $0.00001 | $0.00088 |
Grade A, and why
quality-loop 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.
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Library-First Directive
This agent operates under library-first constraints:
-
Pre-Check Required: Before writing code, search:
.claude/library/catalog.json(components).claude/docs/inventories/LIBRARY-PATTERNS-GUIDE.md(patterns)D:\Projects\*(existing implementations)
-
Decision Matrix:
Result Action Library >90% REUSE directly Library 70-90% ADAPT minimally Pattern documented FOLLOW pattern In existing project EXTRACT and adapt No match BUILD new
Library-First Directive
This agent operates under library-first constraints:
-
Pre-Check Required: Before writing code, search:
.claude/library/catalog.json(components).claude/docs/inventories/LIBRARY-PATTERNS-GUIDE.md(patterns)D:\Projects\*(existing implementations)
-
Decision Matrix:
Result Action Library >90% REUSE directly Library 70-90% ADAPT minimally Pattern documented FOLLOW pattern In existing project EXTRACT and adapt No match BUILD new
STANDARD OPERATING PROCEDURE
Purpose
- Primary action: Primary input
Trigger Conditions
- Command syntax: /quality-loop [args]
- Ensure prerequisites are met before execution.
Inputs and Options
- Inputs: No structured parameters defined; capture user intent explicitly.
Execution Phases
- Review the request and confirm scope.
- Execute the command flow.
- Summarize outcomes and next actions.
Success Criteria and Outputs
- Document artifacts, decisions, and follow-up actions clearly.
Error Handling and Recovery
- If execution fails, capture the failure mode, retry with verbose context, and surface actionable remediation steps.
Chaining and Coordination
Memory and Tagging
- Tag session outputs with who/when/why for traceability.
Example Invocation
- /quality-loop example
Output Format
- Provide a concise summary, actions taken, artifacts generated, and recommended next steps.
- Always include an explicit confidence line: "Confidence: X.XX (ceiling: TYPE Y.YY)".
- Use ceilings — inference/report: 0.70, research: 0.85, observation: 0.95, definition: 0.95.
- Keep user-facing output in plain English; reserve VCL markers for the appendix only.
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 · 115 lines · 5 tokens per session scan A 902859c05fa3
quality-loop is a command published in the GitHub repository DNYoussef/context-cascade (32 stars, last pushed 3mo ago), licensed MIT. It adds 5 tokens to every session and 878 once invoked, about $0.0000 per session on Opus 5.5. 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.
Other commands, from other repositories
checklist
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clarify
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specify
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converge
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implement
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speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.