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/learn.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/learn)<a href="https://agentmods.dev/commands/plipowczan/claude-piv-skeleton/learn"><img src="https://agentmods.dev/badge/commands/plipowczan/claude-piv-skeleton/learn.svg" alt="Measured on agentmods" 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 | $0.00012 | $0.01477 |
| Opus 5 | $0.00006 | $0.00739 |
| Sonnet 5 | $0.00002 | $0.00295 |
| Haiku 4.5 | $0.00001 | $0.00148 |
Grade A, and why
learn 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 7d 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 — 231 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learning Analysis: Extract Insights from Code Reviews
Purpose
Analyze code review artifacts to:
- Extract structured data from review outputs (issues, patterns, recommendations)
- Identify recurring patterns across multiple reviews
- Generate improvement suggestions for rules, validation, and skills
- Update learning metrics to track progress over time
Prerequisites
- At least one code review artifact exists in
.claude/agents/reviews/ - Code review artifacts follow standard format (from
/piv-speckit:code-review)
Arguments
| Argument | Description | Example |
|---|---|---|
--last=N |
Analyze last N reviews (default: all) | --last=5 |
--review=path |
Analyze specific review | --review=.claude/agents/reviews/code-review-feature-x.md |
Process
Step 1: Discover Review Artifacts
# Find all review artifacts
ls -la .claude/agents/reviews/*.md
List all available reviews and their dates.
Step 2: Read and Parse Each Review
For each review artifact, extract:
From ## Issues Found section:
- Issue severity (Critical/High/Medium/Low)
- Issue title and description
- File and line number (if provided)
- Suggested fix (if provided)
From ## Detailed Analysis section:
- Category (Logic Errors, Security, Performance, Code Quality)
- Pass/Fail status
- Specific findings
From ## Positive Findings section:
- Good patterns that worked
- Practices to encourage
From ## Recommendations section:
- Suggested improvements
- Future enhancements
From ## Project Standards Compliance section:
- Which rules were followed
- Which rules were violated
Metadata:
- PIV Quality Score
- Date
- Feature name
Step 3: Identify Patterns
Recurring Issues:
- Group issues by category and type
- Count occurrences across reviews
- Flag issues appearing in 2+ reviews as "recurring"
Anti-Patterns:
- Identify code patterns that repeatedly cause issues
- Document with examples from reviews
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.
- 7d ago First seen · 231 lines · 12 tokens per session scan A ebfc45086635
learn is a command published in the GitHub repository plipowczan/claude-piv-skeleton (4 stars, last pushed 7mo ago), licensed MIT. It adds 12 tokens to every session and 1,477 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
sdd-init
Initialize SDD context — detects project stack and bootstraps persistence backend.
review-branch
Review the current branch's diff against base by dispatching atomic-reviewer. No orchestration loop, no spec required — pre-flight before /commit pr or /commit merge.
init
Install the formatters this repository needs, with every command visible before it runs.
review-sdk-app
Review and validate a Claude Agent SDK application against best practices.
repo-audit
Audit a codebase (local or remote GitHub/GitLab) against architecture principles and requirements, surfacing drift, risk, and missing decisions.
argos
A command for checking whether an implementation matches its design deliverables. Its Korean description compares the work to the design as part of a completion inspection.