Reduce AI coding agent token costs by up to 22%
Token-efficiency skill for coding agents, measured at up to 18% lower cost with unchanged task quality.
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Why it matters
Inject token-efficiency habits into coding agents to reduce conversation bloat and API costs by 10-22% per task without sacrificing code quality, measured across real-world software engineering benchmarks.
Outcomes
What it gets done
Teach agents to gather repository facts in one combined reconnaissance pass instead of multiple separate lookups
Enforce keyhole reads that preview 50 lines when browsing code, reserving full reads only for files being transformed
Batch environment checks and dependency installations into single commands instead of discovering issues one crash at a time
Replace wasteful per-second polling with 30-second intervals during long-running builds and verification steps
Install
Add it to your toolbox
Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/jetbrains-benjamin-plus-skill | bash After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.
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Overview
Benjamin Plus Skill
benjamin-plus is a token-efficiency skill injected into a coding agent's system prompt that changes lookup and polling behavior, measured at 10-18% lower cost and fewer tokens per task with unchanged quality. Use it with any coding agent where session cost or step count matters, injecting it at session start rather than leaving it as a discoverable skill folder.
What it does
benjamin-plus is a token-efficiency skill for coding agents that changes how an agent looks things up and waits, never what it builds. It teaches five habits: recon in one combined pass instead of five separate repo pokes (and checking two real examples before copying a convention, not one); keyhole reads of only 50 lines when the agent just needs to see something, never truncating data it will actually transform; probing the environment for every dependency in one command instead of discovering them one crash at a time; treating the task's own stated check as the definition of done, so a failing check twice means the approach is wrong, not the symptom; and treating polling as a step, checking a running build every 30 seconds rather than every second, since polling alone was measured as nearly half of all steps on some agent platforms. The full ruleset is about 745 tokens.
When to use - and when NOT to
Use it with any coding agent where session cost or step count matters and sessions tend to run bloated with repeated lookups and tight polling loops - measured results ranged from roughly 10% to 18% lower median cost depending on how bloated the baseline sessions were, with quality unchanged (7 better / 5 worse / 68 ties across paired trials, mean verifier reward 0.362 to 0.392) and a cross-platform Java SWE-bench run showing -4.4% cost with an unchanged solve rate and 20% fewer tool calls. It must be injected into the system prompt or session start rather than left as a discoverable skill folder: tested head-to-head, injection saved -17.9% median cost while the same skill as a folder agents had to find first saved nothing (-0.5%, not significant), because agents burned steps just locating SKILL.md.
Inputs and outputs
git clone https://github.com/JetBrains/benjamin-plus-skill ~/.benjamin-plus
For Claude Code, add a SessionStart hook to ~/.claude/settings.json that runs cat ~/.benjamin-plus/injected-instruction.md, or append the same file to a project's CLAUDE.md for zero-config per-project injection. For Codex CLI, no hook is needed since AGENTS.md loads into every session: cat ~/.benjamin-plus/injected-instruction.md >> ~/.codex/AGENTS.md (or into a repo's own AGENTS.md). For any other agent, appending injected-instruction.md (about 3 KB) to the system prompt is the whole integration.
Integrations
The skill was produced by an automated research loop: an agent mined about 1,200 old agent traces for where cost actually goes, drafted rules, ran a paired A/B, read failing trajectories, and revised - six versions, keeping only rules that survived the evidence and deleting ideas that traded quality for savings. The measurement method is a paired A/B (same agent, model, tasks, and container images, differing only in the injected skill text) across 80 paired SkillsBench tasks on Claude Code 2.1.201 in Docker sandboxes with Sonnet 5, using a Wilcoxon test on paired deltas and a sign test on rewards, with a per-trial adoption check confirming the payload reached the model in 80 of 80 treated runs and 0 of 80 controls. Licensed under MIT.
Who it's for
Teams and individuals running coding agents at scale who want to cut token and dollar cost per task through more efficient lookup and polling behavior, without changing what the agent actually builds or its output quality.
Source README
“Beware of little expenses; a small leak will sink a great ship.”
- Benjamin Franklin
benjamin-plus
A token-efficiency skill for coding agents.
It changes how the agent looks things up and waits - never what it builds.
Measured: up to −18 % cost and −22 % tokens per task, quality unchanged.
What the skill teaches
An agent pays twice for every clumsy lookup: once for the step itself, and again every time the growing conversation gets re-read. So the skill teaches five habits:
- Recon in one pass. Gather the facts up front in one combined step instead of poking at the repo five separate times. And before copying a format or convention, look at two real examples, not one.
- Keyhole reads. When the agent only needs to see something, it reads 50 lines, not the whole file. Data it will actually transform is never truncated.
- Probe the environment once. Check every dependency in one command and install whatever is missing in one go, instead of discovering them one crash at a time.
- Green means the task's own check. If the task says how to verify, that command is the definition of done. A missing compiler is still the agent's problem to fix, and a check that fails twice means the approach is wrong, not the symptom. When it passes: stop.
- Polling is a step. A build that hasn't finished has nothing new to say. Check on it every 30 seconds, not every second. On some agent platforms, polling alone turned out to be nearly half of all steps.
The skill's full text: RULESET.md (~745 tokens injected).
What to expect
- Quality unchanged. 7 better / 5 worse / 68 ties (sign p = 0.77); mean verifier reward 0.362 → 0.392. Not powered as an equivalence test - large effects ruled out, small ones not.
- Savings scale with baseline bloat. An identical run a day earlier measured −10.0 % median cost against a leaner-running baseline; the treated arm stayed flat across both days while the control drifted +10.5 %. Expect roughly −10 % to −18 % cost depending on how bloated your sessions run.
- Cross-platform: on Java SWE-bench (Codex CLI, gpt-5.6-luna, 675 paired replicas) the hook-injected skill measured −4.4 % cost [−7.5, −1.5], p = 0.003, solve rate unchanged (p = 0.22), tool calls −20 %.
- Medians are the honest unit: a few hard-task tails can give part of the aggregate back.
Install - inject it, don't "install" it
Same skill, two delivery methods, tested head-to-head: injected, it saves (−17.9 % cost median on the charts above; −4.4 % even on the harder Java/Codex setup) - as a discoverable skill folder, it saves nothing (−0.5 %, n.s.; agents burned steps just finding SKILL.md). So: inject.
git clone https://github.com/JetBrains/benjamin-plus-skill ~/.benjamin-plus
Claude Code - add to ~/.claude/settings.json (verify with /hooks, or just ask Claude Code to add it):
{ "hooks": { "SessionStart": [ { "matcher": "startup|resume|clear|compact",
"hooks": [ { "type": "command", "command": "cat ~/.benjamin-plus/injected-instruction.md" } ] } ] } }
…or per-project, zero config: cat ~/.benjamin-plus/injected-instruction.md >> CLAUDE.md
Codex CLI - AGENTS.md is loaded into every session; no hook needed:
cat ~/.benjamin-plus/injected-instruction.md >> ~/.codex/AGENTS.md # or >> AGENTS.md in a repo
Any other agent - append injected-instruction.md to the system prompt. That's the whole integration (~3 KB).
How it was measured
This skill wasn't written once and shipped. It came out of auto research: an agent working in a loop against the benchmark. Mine ~1,200 old agent traces for where the money actually goes, draft rules, run a paired A/B, read the failing trajectories, revise, run again. Six versions later, only the rules that survived the evidence were left. Everything that traded quality for savings got deleted along the way, which turned out to be most of the clever ideas.
The measurement itself is a paired A/B. Same agent, same model, same tasks, same container images; the only difference between the arms is the injected skill text. 80 paired SkillsBench tasks (Claude Code 2.1.201 in Docker sandboxes, Sonnet 5, low effort), Wilcoxon on the paired deltas, a sign test on rewards, and a per-trial adoption check: the payload reached the model in 80 of 80 treated runs and 0 of 80 controls. Trials that failed on one side only were retried before anything was counted. Every number and caveat lives in EXPECTED-RESULTS.md.
FAQ
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