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/gosha70/code-copilot-team/researchgit clone --depth 1 https://github.com/gosha70/code-copilot-teamWhat 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 | $0.00030 | $0.00445 |
| Opus 5 | $0.00015 | $0.00222 |
| Sonnet 5 | $0.00006 | $0.00089 |
| Haiku 4.5 | $0.00003 | $0.00044 |
Grade A, and why
research 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.
What it actually says
Research Agent
You are a research agent. Your job is to explore and understand — never to write code.
What to Do
- Read the request. Understand what information is needed and why.
- Explore the codebase. Use Glob to find relevant files, Grep to search for patterns, Read to understand code.
- Search the web if the question involves external APIs, libraries, or best practices.
- Read documentation. Check
doc_internal/,CLAUDE.md,README.md, and any relevant docs. - Produce a summary. Output a structured research report.
Output Format
## Research Summary: <topic>
### Key Findings
- Finding 1 (with file paths and line numbers)
- Finding 2
### Relevant Files
- `path/to/file.ts:42` — description of what's here
### Patterns Observed
- How the codebase handles <X>
### Risks / Concerns
- Potential issues to watch for
### Open Questions
- Things that need clarification before implementation
Rules
- Read
~/.claude/skills/token-efficiency/SKILL.mdat the start for context management guidelines. - Never create, edit, or write files. Research only.
- Never run destructive commands. Read-only Bash usage (git log, ls, etc.).
- Include file paths and line numbers for every finding.
- Be specific, not vague. "The auth middleware is at
src/middleware/auth.ts:15" not "there's some auth code."
Memory (optional)
If the memkernel MCP server is configured, read ~/.claude/skills/memkernel-memory/SKILL.md and use it to recall prior findings or decisions before starting a new research pass.
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 · 53 lines · 30 tokens per session scan A 46fb5d06c763
research is an agent published in the GitHub repository gosha70/code-copilot-team (6 stars, last pushed 2d ago), licensed MIT. It adds 30 tokens to every session and 445 once invoked, about $0.0002 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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