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 r13v/pi-feature-dev --skill elevategit clone --depth 1 https://github.com/r13v/pi-feature-devWrote 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/r13v/pi-feature-dev/elevate)<a href="https://agentmods.dev/skills/r13v/pi-feature-dev/elevate"><img src="https://agentmods.dev/badge/skills/r13v/pi-feature-dev/elevate/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/r13v/pi-feature-dev/elevate"><img src="https://agentmods.dev/badge/skills/r13v/pi-feature-dev/elevate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00091 | $0.00758 |
| Opus 5.5 | $0.00036 | $0.00303 |
| Sonnet 5.5 | $0.00018 | $0.00152 |
| Haiku 4.5 | $0.00009 | $0.00076 |
Grade A, and why
elevate 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 10d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Elevate
Aim the task at the outcome, not the artifact. Ask what the user wants to happen after the task is done, and rewrite the request to serve that. Elevating changes scope on purpose, so propose the change and do not take it: the rewritten request is a proposal, not permission to do more work.
Inspect before answering
Ground every answer in the current environment. Check which data, artifacts, tools, and access actually exist before you name them. Treat a discoverable fact as your work, not a question for the user. Mark anything you could not confirm as an assumption.
Keep the user's constraints: deadline, budget, audience, systems they may not touch, and anything they said is out of scope. Elevating changes the target, not these limits.
Answer four questions
- Outcome. What should happen after this task is done? Name the outcome in measurable terms, such as "releases ship without new crashes" instead of "fix this crash", or "the team decides what to cut" instead of "prepare the quarterly review".
- Lever. What is the strongest lever for that outcome? Climb only as far
as the task justifies:
- a better answer: what changed, why, and what to do about it;
- the decisions it forces: the most urgent ones, with the case for and against each;
- the process that produces the task: a redesign or a workflow that keeps the outcome true with less human work. A one-off task may stop at the first step. Do not propose automation only because it is possible.
- Workflow change. What must change in how the user works for the model to reach that lever? Name the access it needs, such as crash reports, a map from versions to commits, raw business data, or machines to reproduce and verify fixes.
- Validation. How will you know the new target is met, other than by your judgment or the user's? For a process, test it against past history first, then run it beside the current one and compare. For a decision, name the signal that would show it was right or wrong.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 70 lines · 91 tokens per session scan A de0ae070e49a
elevate is a skill published in the GitHub repository r13v/pi-feature-dev (3 stars, last pushed 2d ago), licensed MIT. It adds 91 tokens to every session and 758 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-10-22.
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