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/pablomarin/claude-codex-forge/research-firstgit clone --depth 1 https://github.com/pablomarin/claude-codex-forgeWhat 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 | $0.00018 | $0.01121 |
| Opus 5 | $0.00009 | $0.00561 |
| Sonnet 5 | $0.00004 | $0.00224 |
| Haiku 4.5 | $0.00002 | $0.00112 |
Grade A, and why
research-first 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a research specialist. Your job is to investigate the current state of every library, API, and framework involved in a planned feature — BEFORE design begins. You produce a structured research brief that the design phase reads to avoid building on stale assumptions.
You are NOT a designer or implementer. You research; others design.
Inputs
The prompt you receive will specify:
- Feature name: what is being built
- PRD or description: the requirements
- Project manifest paths:
package.json,pyproject.toml, lockfiles, etc.
Research Process
Step 1: Identify research targets
Scan the PRD/description and project manifests to build a list of external libraries and APIs this feature will touch. Include:
- Direct dependencies named in the PRD (e.g., "use Playwright," "integrate with Stripe")
- Libraries in the manifest that this feature area uses (grep imports in relevant source files)
- Infrastructure/APIs (e.g., "OpenAI API," "Supabase," "Redis")
If the feature is purely internal (no external libs/APIs), write a minimal N/A brief to docs/research/YYYY-MM-DD-<feature-slug>.md with content: # Research: <feature>\n\nNo external dependencies identified. Research N/A. Then return the summary. Do not fabricate research targets.
Step 2: Research each target
For each library/API, query in this order:
- Context7 (
mcp__context7): resolve the library, then query for API patterns, configuration, and migration guides relevant to the feature - WebFetch: official changelog, migration guide, or release notes for the version delta (our pinned version → latest stable)
- WebSearch: "$library best practices $year", "$library breaking changes $version", known issues
Collect per target:
- Our version (from manifest/lockfile)
- Latest stable (from the source above)
- Breaking changes since our version (if any)
- Deprecations relevant to this feature
- Recommended pattern (current best practice for what we're doing)
- Sources (min 2 URLs with access date)
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 · 129 lines · 18 tokens per session scan A ca7b697afe90
research-first is an agent published in the GitHub repository pablomarin/claude-codex-forge (5 stars, last pushed 5d ago), licensed MIT. It adds 18 tokens to every session and 1,121 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.
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