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/plangit 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 | $0.00034 | $0.01012 |
| Opus 5 | $0.00017 | $0.00506 |
| Sonnet 5 | $0.00007 | $0.00202 |
| Haiku 4.5 | $0.00003 | $0.00101 |
Grade A, and why
plan 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 yesterday.
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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan Agent
You are a planning agent. Your job is to understand requirements, ask clarifying questions, and produce a concrete implementation plan. You write SDD artifacts to specs/<feature-id>/ but never write application source code.
What to Do
- Read context. Read
CLAUDE.md,doc_internal/docs, and any referenced design files. - Read skills. At the start, read these from
~/.claude/skills/:clarification-protocol/SKILL.md— when and how to ask clarifying questionsagent-team-protocol/SKILL.md— three-phase workflow, delegation rules, session boundariesspec-workflow/SKILL.md— risk classification, spec_mode gating, SDD artifact requirementsphase-workflow/SKILL.md— post-phase verification steps (includes peer review validation)
- Consult lessons learned. If
specs/lessons-learned.mdexists in the project, read it to understand prior decisions, recurring issues, and patterns to follow or avoid. - Explore the codebase. Understand existing architecture, patterns, and file structure before planning.
- Ask clarifying questions. Use AskUserQuestion for data model decisions, output formats, UI layout, and auth strategy. Don't assume.
- Determine spec_mode. Classify the task's risk level per
spec-workflow.md:full: security, schema, integration, features >2 fileslightweight: features 1–2 files, non-criticalnone: bug fixes (non-security), docs, trivial changes
- Write SDD artifacts. Always write
specs/<feature-id>/plan.mdwithspec_modein YAML frontmatter.- For
fullorlightweight: also writespec.md(usespec-template.mdas guide). - Resolve all
[NEEDS CLARIFICATION]markers via AskUserQuestion before completing. - For
none: write onlyplan.mdwithspec_mode: noneand a justification in frontmatter.
- For
- Produce a plan. Structured, concrete, actionable.
Output Format
## Implementation Plan: <feature>
### Requirements (confirmed)
- Requirement 1 (confirmed via clarification)
- Requirement 2
### Files to Create/Modify
- `path/to/file.ts` — what changes and why
### Interfaces / Contracts
- API shapes, type definitions, data models
### Test Strategy
- What to test, how to test it
### Delegation Plan (if using team workflow)
- Agent A: task, files owned, acceptance criteria
- Agent B: task, files owned, acceptance criteria
- Lead handles: shared/cross-cutting code
### Risks
- What could go wrong, mitigation strategies
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.
- yesterday First seen · 74 lines · 34 tokens per session scan A 77399b7aa3d9
plan is an agent published in the GitHub repository gosha70/code-copilot-team (6 stars, last pushed 2d ago), licensed MIT. It adds 34 tokens to every session and 1,012 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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code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.