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 implement-plangit 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/implement-plan)<a href="https://agentmods.dev/skills/tobihagemann/turbo/implement-plan"><img src="https://agentmods.dev/badge/skills/tobihagemann/turbo/implement-plan.svg" alt="Measured on agentmods" 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 | $0.00092 | $0.00846 |
| Opus 5 | $0.00046 | $0.00423 |
| Sonnet 5 | $0.00018 | $0.00169 |
| Haiku 4.5 | $0.00009 | $0.00085 |
Grade A, and why
implement-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 8d 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implement Plan
Execute an implementation plan file.
Task Tracking
At the start, use TaskCreate to create a task for each step:
- Resolve and read the plan file
- Read context files
- Run
/implementskill - Update plan status
Step 1: Resolve and Read the Plan File
Determine which plan file to implement using these rules in order:
- Explicit path — If an absolute or relative path was passed and that file exists, use it
- Explicit slug — If a slug was passed (e.g.,
add-image-cache), resolve to.turbo/plans/<slug>.mdif that file exists - Single file — Glob
.turbo/plans/*.md. If exactly one plan exists, use it - Most recent — If multiple plans exist, use the most recently modified
- Legacy fallback — If
.turbo/plans/does not exist but.turbo/plan.mdexists, use it - Nothing found — If no rule above resolved to an existing file, tell the user to run
/turboplanand stop. When a slug or path was passed but no file matched it, say which one was tried
If multiple plans exist and the most-recent choice is non-obvious (e.g., several plans were modified within the same minute), use AskUserQuestion to let the user pick from the candidates.
State the resolved plan path before continuing, then read the file.
Unless an explicit path or slug was passed, confirm the resolved plan still describes work that remains to be done:
- Already implemented — the frontmatter
status:isdone
When the signal fires, output it as text. Then use AskUserQuestion to offer:
- Implement anyway — the marker is stale
- Pick another plan — resolve to a different file under
.turbo/plans/, then confirm that plan against this same signal - Leave it unimplemented — the plan needs revising first
On Leave it unimplemented, tell the user to bring the plan current with /refine-plan and run this skill again. Mark the remaining implement steps deleted, then use the TaskList tool and proceed to any remaining task.
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.
- 8d ago First seen · 68 lines · 92 tokens per session scan A 62c3b767cb03
implement-plan is a skill published in the GitHub repository tobihagemann/turbo (402 stars, last pushed yesterday), licensed MIT. It adds 92 tokens to every session and 846 once invoked, about $0.0005 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
my-adhd-caveman
Self-contained output mode: ADHD actable structure (action first, numbered steps, one next action, one-line progress) plus caveman diction (drop fluff/articles, fragments, fewer tokens). Use when the user says "adhd-caveman", "my-adhd-caveman", or invokes /my-adhd-caveman. Needs no other skills.
diffler
Generate and deliver a graded comprehension quiz from the current Git branch diff, either in the local terminal or through Google Forms. Use when a user asks to prove, test, or verify understanding of branch changes, a feature branch, or a pull request.
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…