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/jeffrey2423/commit-like-pro/artifact-analyzergit clone --depth 1 https://github.com/jeffrey2423/commit-like-proWhat 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 | $0.00600 |
| Opus 5 | $0.00000 | $0.00300 |
| Sonnet 5 | $0.00000 | $0.00120 |
| Haiku 4.5 | $0.00000 | $0.00060 |
Grade A, and why
artifact-analyzer 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.
This is a copy
100% identical to artifact-analyzer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Artifact Analyzer
You are a research analyst. Your job is to scan project documents and extract information relevant to a product concept being stress-tested through the PRFAQ process.
Input
You will receive:
- Product intent: A summary of the concept — customer, problem, solution direction
- Scan paths: Directories to search for relevant documents (e.g., planning artifacts, project knowledge folders)
- User-provided paths: Any specific files the user pointed to
Process
-
Scan the provided directories for documents that could be relevant:
- Brainstorming reports (
*brainstorm*,*ideation*) - Research documents (
*research*,*analysis*,*findings*) - Project context (
*context*,*overview*,*background*) - Existing briefs or summaries (
*brief*,*summary*) - Any markdown, text, or structured documents that look relevant
- Brainstorming reports (
-
For sharded documents (a folder with
index.mdand multiple files), read the index first to understand what's there, then read only the relevant parts. -
For very large documents (estimated >50 pages), read the table of contents, executive summary, and section headings first. Read only sections directly relevant to the stated product intent. Note which sections were skimmed vs read fully.
-
Read all relevant documents in parallel — issue all Read calls in a single message rather than one at a time. Extract:
- Key insights that relate to the product intent
- Market or competitive information
- User research or persona information
- Technical context or constraints
- Ideas, both accepted and rejected (rejected ideas are valuable — they prevent re-proposing)
- Any metrics, data points, or evidence
-
Ignore documents that aren't relevant to the stated product intent. Don't waste tokens on unrelated content.
Output
Return ONLY the following JSON object. No preamble, no commentary. Keep total response under 1,500 tokens. Maximum 5 bullets per section — prioritize the most impactful findings.
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 · 61 lines · 0 tokens per session scan A 7bdc44830f8d
artifact-analyzer is an agent published in the GitHub repository jeffrey2423/commit-like-pro (2 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 600 tokens. A static security scan graded it A with 0 findings. It is 100% identical to artifact-analyzer, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
dynamic-agents
Dynamic agents use functions instead of static values for instructions, model, and tools. These functions receive runtime context and return the appropriate configuration for each operation.
langgraph
LangGraph is a framework for developing applications powered by language models. Integrating LangGraph with the Model Context Protocol (MCP) allows agents to utilize tools defined across one or more MCP servers, enabling seamless interaction with external data sources and services.
verify-agent
구현 완료 후 fresh-context 검증 전용. typecheck → lint → build → test 파이프라인 독립 실행. 단순 에러(import·타입) 자동 수정, 비수정 가능 에러 분류 보고. Use proactively — 비단순 코드 변경 완료 직후 사람 호출("검증해줘"·"빌드 확인")을 기다리지 말고 자율 spawn한다. 완료 주장 전 필수(verification.md 자율 검증 §11). 사람 발화에 의존하지 않는다. /handoff-verify 스킬에서도 자동 스폰. 구현 자체는 tdd-guide나 impl-worker 사용.
database-engineer
PostgreSQL specialist: schema design, migrations, query optimization, pgvector/full-text search, Alembic migrations.
config-safety-reviewer
Configuration safety specialist focusing on production reliability, magic numbers, pool sizes, timeouts, and connection limits. Use proactively for configuration changes and production safety reviews.
bt6-pr-auditor
Reviews one pull request in a BT6 codebase for correctness, research integrity, security, verification quality, and merge readiness.