Borrowing it
Nothing to install: this file belongs to mtarcure/claude-vibe-squad. 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/mtarcure/claude-vibe-squad/main/.agents/skills/head-tail/SKILL.mdgit clone --depth 1 https://github.com/mtarcure/claude-vibe-squadWrote 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/mtarcure/claude-vibe-squad/head-tail)<a href="https://agentmods.dev/skills/mtarcure/claude-vibe-squad/head-tail"><img src="https://agentmods.dev/badge/skills/mtarcure/claude-vibe-squad/head-tail/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/mtarcure/claude-vibe-squad/head-tail"><img src="https://agentmods.dev/badge/skills/mtarcure/claude-vibe-squad/head-tail.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 | $0.00068 | $0.01125 |
| Opus 5 | $0.00034 | $0.00562 |
| Sonnet 5 | $0.00014 | $0.00225 |
| Haiku 4.5 | $0.00007 | $0.00112 |
Grade A, and why
head-tail 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 9d 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Head-Tail
Sample a file that is too large to read whole by taking a bounded slice from each end. Logs and build output are usually structured this way on purpose: the beginning carries the invocation, configuration, and version banner, and the end carries the outcome, the error, and the exit status.
Canonical efficiency contract
The task packet's injected Execution efficiency — the cost unit is ROUND-TRIPS section is the sole source for whole-file reads, batching independent operations, avoiding re-reads, and excluding generated content with tool-level globs. This skill does not redefine those rules. It adds only the log-specific decision test, bounded-sample evidence contract, long-line defense, middle-search rule, and producer-status trap that the packet section does not specify.
When this is the right tool — and when it is the wrong one
This technique is narrow, and applying it to the wrong file is a measured, expensive mistake.
- Right: log files, CI/build output, test-runner transcripts, generated artifacts, data dumps, and anything whose middle is repetitive by construction.
- Wrong: source files, briefs, packets, and configuration. Follow the injected whole-file contract; do not use this skill as an exception to it. A large dense file is not a sampling candidate merely because its full read is inconvenient.
The deciding question is not "is this file long?" but "is this file's middle repetitive?" A 3,000-line source file is long and dense; a 3,000-line test log is long and repetitive. Only the second is a head-tail candidate.
Steps
- Size it first. Check the byte size and line count before reading anything. This is one cheap call and it decides the entire approach — a file you assumed was huge is often small enough to read whole.
- Choose the slice deliberately. Twenty lines from each end is a reasonable default for a log. Take more from the tail when you are chasing a failure, more from the head when you are checking how a run was invoked or configured.
- Label both slices. Follow the packet's batching rule, and make the output itself distinguish the head from the tail so lines from opposite ends cannot be mistaken for contiguous evidence.
- Bound the line length. A single line in a log can be megabytes — a serialized payload, a base64 blob, a minified bundle. Truncate over-long lines and mark them truncated, so one pathological line cannot displace everything else you read.
- State the gap explicitly. Report the sampled range and what was skipped: "lines 1–20 and 14,981–15,000 of 15,000; the middle 14,960 lines were not read." A reader who does not know a gap exists will treat your sample as the whole file.
- Search the middle rather than paging it. When the ends do not answer the question, do not walk the file in windows. Search it — grep for the error, the failing test name, the exception class — and read the matches with a little surrounding context. One targeted search beats twenty sequential pages.
- Capture producer status separately. Save the build/test process's direct exit status before any
sampling pipeline. A successful
head,tail, formatter, or filter says only that the sampler ran; it cannot establish that the producer succeeded.
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.
- 9d ago First seen · 73 lines · 68 tokens per session scan A 385b463de1d1
head-tail is a skill published in the GitHub repository mtarcure/claude-vibe-squad (122 stars, last pushed yesterday), licensed MIT. It adds 68 tokens to every session and 1,125 once invoked, about $0.0003 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.
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