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 tobihagemann/turbo --skill discuss-changegit clone --depth 1 https://github.com/tobihagemann/turboWrote 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/tobihagemann/turbo/discuss-change)<a href="https://agentmods.dev/skills/tobihagemann/turbo/discuss-change"><img src="https://agentmods.dev/badge/skills/tobihagemann/turbo/discuss-change/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/tobihagemann/turbo/discuss-change"><img src="https://agentmods.dev/badge/skills/tobihagemann/turbo/discuss-change.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 12 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00088 | $0.01541 |
| Opus 5 | $0.00044 | $0.00771 |
| Sonnet 5 | $0.00018 | $0.00308 |
| Haiku 4.5 | $0.00009 | $0.00154 |
Grade A, and why
discuss-change 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 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.
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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Discuss Change
Escalate open decisions, agree on the implementation shape, then implement.
Task Tracking
Use TaskCreate to create a task for each step:
- Capture the task
- Escalate product decisions
- Deep-dive discussion
- Confirm the shape
- Run
/implementskill
Step 1: Capture the Task
Absorb the request without interrupting. Take the task from the user's request, or from conversation context when the task was already established. Restate the goal in one or two sentences and confirm.
Step 2: Escalate Product Decisions
Identify product or design decisions the request did not resolve. Escalate these via AskUserQuestion before any code is written. Read the code the change would touch before judging whether a bullet matches. Skip when no bullet below matches the change.
Escalate when:
- The change requires choosing between user-facing behaviors the request did not specify (opt-in vs opt-out, strict vs lenient, sync vs async)
- The change assumes product requirements that were not stated
- Design trade-offs affect UX or product direction rather than technical implementation
- Multiple valid approaches exist and the choice is a matter of product preference, not technical merit
- The change would introduce a pattern not yet established in this codebase, or follow one sourced from outside it
- The change adds consistency or durability machinery (a lease, lock, queue, versioning scheme, or new persistent entity) that no stated requirement demands; carrying that machinery is itself a product decision
Do not escalate technical decisions the agent can make autonomously: which data structure, which existing pattern to follow, internal implementation approach. The boundary is product intent.
Confirm external constraints before escalating. When an option depends on a third-party API, service, or platform behaving a particular way, query documentation MCP tools (or WebSearch as a fallback) and drop the option unless current documentation confirms that behavior.
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 · 89 lines · 88 tokens per session scan A ba783c8588a1
discuss-change is a skill published in the GitHub repository tobihagemann/turbo (402 stars, last pushed 2d ago), licensed MIT. It adds 88 tokens to every session and 1,541 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
plan-ceo-review
CEO/founder-mode plan review. Rethink the problem, find the 10-star product, challenge premises, expand scope when it creates a better product. Three modes: SCOPE EXPANSION (dream big), HOLD SCOPE (maximum rigor), SCOPE REDUCTION (strip to essentials). Originally from Garry Tan's gstack…
master
Turn reference docs into active mastery through retrieval practice, case binding, and scenario simulation. Combines Skycak (learning science) and Chin (expertise acceleration) frameworks. Use when you want to deeply learn material, not just read it.
upskilling-coach
Expert coach for learning, mastering, and upskilling in any domain. Use when the user wants to learn, master, improve, upskill, get better at, or get coached on any topic. Helps build consistent practice habits, identify prerequisites, design efficient learning loops, avoid common pitfalls, maintain discipline, and…
agentic-review
Deep multi-agent code review for local changes. Inspired by AmpCode's agentic review. Use when you want comprehensive analysis of staged changes, unstaged changes, specific commits, or branch differences. Spawns parallel specialized agents (security, performance, patterns, architecture) and synthesizes actionable…
qa
Full QA on all session changes using Codex as a second pair of eyes. Use when user says "QA", "full QA", "QA my changes", "QA all your changes", or "use codex to review". Runs git diff, sends changes to Codex for thorough review, and synthesizes findings.
design-swarm
Orchestrate a team of 10 design agents for holistic UI audit, ideation, and implementation. Use when: "design swarm", "holistic audit", "design review with all lenses", "full design analysis". Modes: audit, ideate, implement (any combination). Targets: screenshots, URLs, file paths, or text briefs.