Borrowing it
Nothing to install: this file belongs to haakonbull/autosprint. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/haakonbull/autosprint/master/.claude/skills/grill-destination/SKILL.mdgit clone --depth 1 https://github.com/haakonbull/autosprintWrote 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/haakonbull/autosprint/grill-destination)<a href="https://agentmods.dev/skills/haakonbull/autosprint/grill-destination"><img src="https://agentmods.dev/badge/skills/haakonbull/autosprint/grill-destination/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/haakonbull/autosprint/grill-destination"><img src="https://agentmods.dev/badge/skills/haakonbull/autosprint/grill-destination.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 | $0.00121 | $0.06465 |
| Opus 5 | $0.00060 | $0.03232 |
| Sonnet 5 | $0.00024 | $0.01293 |
| Haiku 4.5 | $0.00012 | $0.00647 |
Grade A, and why
grill-destination 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 11d 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 — 257 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Interview the user to produce a concrete destination.md at <target_repo>/autosprint/destination.md. This file is the sole input to autosprint's planning phase — the single fixed point the PIT loop iterates toward.
The interview walks three passes through the canonical concerns. At each section the user picks one of these outcomes:
- Answer it → user dictates the answer; you write it into
destination.md. - Accept the recommended default (only for sections that ship one) → you write the default content as-is.
- Modify the recommended default (only for sections that ship one) → user dictates changes; you write the modified version. If the modification carries non-trivial reasoning, flag that the change deserves an ADR entry recording the rationale.
- Let autosprint decide → you write a destination-shaped sentence with an
*(Open — autosprint to decide.)*marker. Autosprint will resolve in a future sprint, recording rationale inadr.md. - Skip → you don't add the section to
destination.mdat all. (Allowed only in passes 2 and 3.)
The resulting file contains only the sections the user picked into. Everything else stays out — no empty placeholders, no aspirational sections nobody intends to fill in.
Where things live
destination.md is the destination — the GPS coordinate the PIT loop descends toward. It lives at <target_repo>/autosprint/destination.md and is the load-bearing input the planner reads on every sprint.
Sibling locations under <target_repo>/autosprint/:
inputs/— supporting human-authored material the destination may reference. Half-finished data models, domain glossary, project description, design notes. Read on demand, not on every sprint.destination.mdis authoritative; if a file ininputs/contradicts it, the file ininputs/is wrong and gets updated. Each file ininputs/is anchored by humans; agents may append to designated AI-append sections (e.g.## AI-observed inconsistencies) but never modify human content above them. Pattern:destination.md's "Referenced inputs" section names whichinputs/files matter for which kinds of tasks.adr.md— append-only history of technical decisions. Read by Plan and Implement on every sprint as context for what's already locked in. Both humans and the implementor agent author entries here. To change a decision, append a new entry that references the old one under**Supersedes:**— old entries stay in the file as history.plan.md— loop state authored by the planner. Not an input to the grill skill.logs/,cache/— agent-generated bookkeeping. Never touch by hand.
Plus the standard agent file:
<target_repo>/CLAUDE.md— agent navigation context (folder layout, conventions, "we use uv not pip"). Loaded on every agent invocation. Not part of this interview, but updated by autosprint as project shape changes.
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.
- 11d ago First seen · 257 lines · 121 tokens per session scan A a9f0d0fdcc24
grill-destination is a skill published in the GitHub repository haakonbull/autosprint (5 stars, last pushed 2mo ago), licensed MIT. It adds 121 tokens to every session and 6,465 once invoked, about $0.0006 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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