Borrowing it
Nothing to install: this file belongs to MDGrey33/claude_mind. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/MDGrey33/claude_mind/main/.claude/commands/feedback.mdgit clone --depth 1 https://github.com/MDGrey33/claude_mindWrote 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/mdgrey33/claude_mind/feedback)<a href="https://agentmods.dev/commands/mdgrey33/claude_mind/feedback"><img src="https://agentmods.dev/badge/commands/mdgrey33/claude_mind/feedback/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/mdgrey33/claude_mind/feedback"><img src="https://agentmods.dev/badge/commands/mdgrey33/claude_mind/feedback.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 | $0.00011 | $0.00236 |
| Opus 5.5 | $0.00004 | $0.00094 |
| Sonnet 5.5 | $0.00002 | $0.00047 |
| Haiku 4.5 | $0.00001 | $0.00024 |
Grade A, and why
feedback 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 2mo 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.
What it actually says
Feedback Command
User feedback: $ARGUMENTS
Process
- Validate - Check active task exists
- Acknowledge - Confirm receipt
- Categorize - Determine type (requirements, quality, ux, security)
- Route - Send to appropriate phase/agent
- Log - Record in learning system
- Iterate - Re-invoke agent with feedback
Categories & Routing
| Category | Phase | Agent |
|---|---|---|
| requirements | Planning | Technical PM |
| quality/bugs | Implementation | Backend Engineer |
| ux | Implementation | Frontend Engineer |
| security | Security | Security Engineer |
Confirmation
## Iteration Started
**Feedback**: $ARGUMENTS
**Category**: [detected]
**Returning to**: [phase]
The team is addressing your feedback.
Check progress: /status
Learning
Record feedback in .claude-workspace/patterns.json to improve future deliveries.
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.
- 2mo ago First seen · 44 lines · 11 tokens per session scan A 5c420117a424
feedback is a command published in the GitHub repository MDGrey33/claude_mind (2 stars, last pushed 10mo ago), licensed MIT. It adds 11 tokens to every session and 236 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 2026-08-31.
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.
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.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.