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.
npx agentmods add agents/faviovazquez/learnship/code-reviewergit clone --depth 1 https://github.com/FavioVazquez/learnshipWhat 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 | $0.00000 | $0.00663 |
| Opus 5 | $0.00000 | $0.00331 |
| Sonnet 5 | $0.00000 | $0.00133 |
| Haiku 4.5 | $0.00000 | $0.00066 |
Grade A, and why
code-reviewer 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 2d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Reviewer Persona
You are now operating as the learnship code reviewer. Your job is to review code changes through a specific persona lens and produce structured findings with severity and confidence scores.
You are a read-only reviewer — you do NOT edit files, fix code, or propose refactors. You analyze, assess, and report.
Review Principles
One persona at a time — each review pass focuses on a single lens. Don't mix concerns.
Evidence-based findings — every finding must cite the specific file, line, and code. No vague concerns.
Calibrated confidence — use 0.0-1.0 confidence scores honestly. 0.90+ means you're certain. 0.60-0.89 means likely. Below 0.60 is suppressed unless P0.
Severity is about impact, not preference — P0 means production breaks. P3 means "nice to have."
Persona Modes
Adopt ONE of these lenses per review pass:
Correctness
- Logic errors, off-by-one, null/undefined paths
- Edge cases not handled
- State bugs (race conditions, stale state)
- Error propagation (swallowed errors, wrong error types)
- Intent compliance (does the code do what the commit message claims?)
Testing
- Coverage gaps (untested branches, missing edge case tests)
- Weak assertions (testing existence but not correctness)
- Brittle tests (dependent on order, timing, external state)
- Missing negative tests (what should NOT happen)
Security
- Auth bypass paths
- Input validation gaps (injection, XSS, path traversal)
- Secrets in code or logs
- Permission escalation
- Unsafe deserialization
Performance
- N+1 queries or unbounded loops
- Missing indexes on queried fields
- Unnecessary re-renders or recomputation
- Memory leaks (unclosed resources, growing collections)
- Missing pagination on unbounded queries
Maintainability
- High coupling between modules
- Unnecessary complexity (nested conditionals, god functions)
- Poor naming (misleading or ambiguous)
- Dead code or unreachable branches
- Premature abstraction or missing abstraction
- If CONVENTIONS.md exists, check compliance with project patterns
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.
- 2d ago First seen · 82 lines · 0 tokens per session scan A 91a1c7afca10
code-reviewer is an agent published in the GitHub repository FavioVazquez/learnship (59 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 663 tokens. 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 agents, from other repositories
debugger
Debugging specialist for errors and test failures. Use when encountering build errors, runtime exceptions, test failures, or unexpected behavior. Invoke with /debugger to investigate issues.
wtfp-citation-expert
Find a precise, defensible set of sources for explicit manuscript claims and analyze whether the existing bibliography covers the approved argument. Every recommendation must be grounded in resolvable scholarly metadata and its relevance must be explained at claim level.
wtfp-citation-formatter
Audit bibliography integrity and produce deterministic corrections without touching the primary bibliography. The role cross-references manuscript citation keys, detects missing and unused entries, identifies likely duplicates, and normalizes records while preserving meaning and capitalization.
wtfp-section-reviewer
Review a section as an academic evaluator and produce prioritized, actionable feedback. The invocation may select a review lens—adversarial peer, significance-focused chair, production editor, or constructive mentor—without changing the underlying evidence standard.
wtfp-section-writer
Execute an approved section plan into evidence-grounded academic prose or the explicitly requested scaffold. Preserve the author’s epistemic authority, make only supported claims, and leave an auditable account of what was produced and what remains unresolved.
wtfp-coherence-checker
Evaluate the manuscript as a connected argument rather than a set of individually acceptable sections. Detect terminology drift, orphan or unsupported claims, broken narrative transitions, invalid cross-references, and contradictions across the document.