Borrowing it
Nothing to install: this file belongs to plipowczan/claude-piv-skeleton. 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/plipowczan/claude-piv-skeleton/main/.claude/commands/validation/suggest-improvement.mdgit clone --depth 1 https://github.com/plipowczan/claude-piv-skeletonWrote 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/plipowczan/claude-piv-skeleton/suggest-improvement)<a href="https://agentmods.dev/commands/plipowczan/claude-piv-skeleton/suggest-improvement"><img src="https://agentmods.dev/badge/commands/plipowczan/claude-piv-skeleton/suggest-improvement/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/plipowczan/claude-piv-skeleton/suggest-improvement"><img src="https://agentmods.dev/badge/commands/plipowczan/claude-piv-skeleton/suggest-improvement.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 | $0.00971 |
| Opus 5 | $0.00005 | $0.00485 |
| Sonnet 5 | $0.00002 | $0.00194 |
| Haiku 4.5 | $0.00001 | $0.00097 |
Grade A, and why
suggest-improvement 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 12d 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Suggest Improvement: Generate Rule/Validation/Skill Enhancement
Purpose
Create a structured improvement suggestion artifact that proposes changes to:
- Rules (
.claude/rules/*.md) - Add patterns, anti-patterns, examples - Validation (
.claude/commands/validation/*.md) - Add checks - Skills (
.claude/skills/*.md) - Add enforcement behaviors
Prerequisites
- Learning insights exist (run
/piv-speckit:learnfirst) - Improvement is based on real data from code reviews
Arguments
| Argument | Description | Example |
|---|---|---|
type |
Type of improvement (rule, validation, skill) | rule |
title |
Brief title of the improvement | "Add N+1 query detection" |
Process
Step 1: Gather Context
Read learning insights from .claude/agents/learning/insights/ to understand:
- What issue/pattern prompted this suggestion
- How many times it occurred
- What the impact was
Step 2: Identify Target File
Based on type:
- rule: Determine which rule file (security, testing, general, etc.)
- validation: Determine which validation command to enhance
- skill: Determine which skill to enhance or create
Step 3: Propose Change
Determine:
- What content to add
- Where to add it (section, line)
- How it will prevent the issue
Step 4: Create Improvement Artifact
Create at: .claude/agents/learning/suggestions/{suggestion-id}.md
Generate unique suggestion-id: {type}-{short-title}-{timestamp}
Example: rule-n1-query-detection-20250115
Structure:
# Improvement Suggestion: {Title}
**ID:** {suggestion-id}
**Date:** {timestamp}
**Type:** {rule-update | validation-addition | skill-enhancement}
**Priority:** {critical | high | medium | low}
**Status:** pending
## Problem
{Description of the recurring issue or gap}
## Evidence
Based on learning analysis:
- **Found in:** {N} code reviews
- **Last occurrence:** {date}
- **Source reviews:**
- `.claude/agents/reviews/{review1}.md`
- `.claude/agents/reviews/{review2}.md`
- **Impact:** {description of impact}
## Proposed Change
### Target File
`{path to file to update}`
### Section
{Which section to add to}
### Proposed Content
```{language}
{The actual content to add}
Rationale
{Why this change will prevent the issue}
Validation
After applying this change:
- {How to verify it works}
- {Expected behavior}
Risks
- {Potential downside 1}
- {Potential downside 2}
Approval
- Reviewed by human
- Approved for implementation
- Applied to codebase
Application Instructions
To apply this improvement:
- Review the proposed change above
- If approved, update
{target file}with the proposed content - Run validation to ensure no regressions
- Mark as applied and move to
.claude/agents/learning/applied/
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.
- 12d ago First seen · 167 lines · 11 tokens per session scan A b73124334f6c
suggest-improvement is a command published in the GitHub repository plipowczan/claude-piv-skeleton (4 stars, last pushed 7mo ago), licensed MIT. It adds 11 tokens to every session and 971 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.
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.