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 skills add mhylle/claude-skills-collection --skill team-implement-plangit clone --depth 1 https://github.com/mhylle/claude-skills-collectionWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mhylle/claude-skills-collection/team-implement-plan)<a href="https://agentmods.dev/skills/mhylle/claude-skills-collection/team-implement-plan"><img src="https://agentmods.dev/badge/skills/mhylle/claude-skills-collection/team-implement-plan/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mhylle/claude-skills-collection/team-implement-plan"><img src="https://agentmods.dev/badge/skills/mhylle/claude-skills-collection/team-implement-plan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00082 | $0.02577 |
| Opus 5 | $0.00041 | $0.01288 |
| Sonnet 5 | $0.00016 | $0.00515 |
| Haiku 4.5 | $0.00008 | $0.00258 |
Grade A, and why
team-implement-plan scanned grade A with 1 finding 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 12d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
b. Run integration tests (curl endpoints, check UI, verify behavior) How it starts
The opening of the file, as written. The whole thing — 322 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Team Implement Plan (Small Review Team)
Overview
This skill executes implementation plans using a small team of 2-3 members. Unlike the solo implement-plan which uses an orchestrator/subagent pattern, this skill has teammates that implement code directly and review each other's work adversarially.
Team composition:
- Implementer: Writes code directly using Write/Edit/Bash. Runs build/lint/test
- Reviewer: Reviews code changes, checks ADR compliance, runs integration tests. Sends fix requests
- Integrator (optional, for plans with 4+ phases): Monitors cross-phase consistency, handles plan sync
When to use this vs implement-plan:
- Use
implement-planfor simple plans (1-3 phases, clear requirements, ~30-40K tokens/phase) - Use
team-implement-planfor quality-sensitive plans where adversarial review matters (~60-80K tokens/phase) - Use
team-implement-plan-fullfor large plans with parallel phases (~100-150K tokens/wave)
Reference: See references/team-lifecycle.md for team lifecycle and references/quality-pipeline-distribution.md for pipeline distribution.
Initial Response
When invoked with a plan path:
"I'll set up a small implementation team. An Implementer will write code while a Reviewer independently checks quality. Let me read the plan and set up the team."
Workflow
Phase 1: Plan Reading & Team Setup
Step 1a: Read and validate the plan
Read($0) # Plan path from argument
Validate the plan has:
- Implementation phases with objectives
- Tasks per phase
- Exit conditions per phase
- Dependencies between phases
If the plan is missing required sections, inform the user and stop.
Step 1b: Check existing progress
TaskList # Check for existing tasks from this plan
If tasks exist with some completed, resume from the first incomplete phase. If no tasks exist, the plan was created without task bootstrapping — create tasks now.
Step 1c: Determine team size
| Plan Size | Team |
|---|---|
| 1-3 phases | Implementer + Reviewer (2 members) |
| 4+ phases | Implementer + Reviewer + Integrator (3 members) |
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.
- 12d ago First seen · 322 lines · 82 tokens per session scan A afad87dffd5f
team-implement-plan is a skill published in the GitHub repository mhylle/claude-skills-collection (18 stars, last pushed 9d ago), licensed MIT. It adds 82 tokens to every session and 2,577 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
autoreview
Pre-commit/ship code review: Codex default; optional Claude or Pi.
omh-code-review
This is a Hermes-native code-review workflow skill.
revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
code-reviewer
Code review specialist focused on patterns, bugs, security, and performance.
full-repo-review
Comprehensive four-wave review of all repo source files, producing a prioritized issue backlog.
agent-teams-simplify-and-harden
Implementation + audit loop using parallel agent teams with structured simplify, harden, and document passes. Spawns implementation agents to do the work, then audit agents to find complexity, security gaps, and spec deviations, then loops until code compiles cleanly, all tests pass, and auditors find zero issues or…