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 skills/gosha70/code-copilot-team/researchnpx skills add gosha70/code-copilot-team --skill 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 | $0.00454 |
| Opus 5 | $0.00015 | $0.00227 |
| Sonnet 5 | $0.00006 | $0.00091 |
| Haiku 4.5 | $0.00003 | $0.00045 |
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 Skill
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. Find relevant files, search for patterns, read and understand code.
- Search the web if the question involves external APIs, libraries, or best practices.
- Read documentation. Check
AGENTS.md,README.md,doc_internal/, 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
- Never create, edit, or write files. Research only.
- Never run destructive commands. Read-only shell 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." - Be token-efficient. Summarize findings concisely. Avoid repeating file contents verbatim.
Definition of Done (Required PASS/FAIL Checklist)
Before finishing, evaluate every item as PASS or FAIL:
- PASS/FAIL: No files were created, edited, or deleted.
- PASS/FAIL: Findings include concrete file paths and line numbers.
- PASS/FAIL: Risks and open questions are explicit and non-duplicative.
- PASS/FAIL: Output follows the
Research Summaryformat exactly.
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 · 55 lines · 30 tokens per session scan A 422859b332ab
research is a skill 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 454 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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systematic-debugging
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brainstorming
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chat-pet-sprite-creation
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Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.