Token efficient execution
Skill markoblogo/abvx-agent-skills/skills/token-efficient-execution
Reduce token waste in long coding-agent sessions by avoiding repeated reads, noisy output, oversized context, broad rewrites, and unnecessary narration. Use on long tasks, heavy repos, multi-file audits, shell-heavy debugging loops, or whenever token budget is a practical delivery constraint.From its SKILL.md
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SKILL.md
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Token Efficient Execution
Save tokens by tightening the work loop, not only the answer style.
Core Principle
The biggest waste usually comes from repeated exploration, repeated explanation, and oversized context reloads. Fix the loop first.
Execution Rules
- Read targeted files, not whole trees.
- Search exact symbols, error text, route names, or config keys before broad terms.
- Do not reread the same file unless it changed or a new hypothesis makes it relevant.
- Prefer narrow patches over file rewrites.
- Avoid long “plan recap” messages after every small step.
- Keep intermediary updates to one or two sentences unless the user asked for a plan.
- Reuse gathered evidence instead of re-deriving it.
- Stop low-value parallel exploration once a clear winning path appears.
Task Patterns
Debugging
- Build one reproducible signal.
- Maintain a checked-location ledger.
- Rank hypotheses before opening more files.
Implementation
- Read surrounding code once, then patch.
- Keep edits scoped to the behavior under change.
- Run the narrowest verification first, then broaden only as needed.
Research
- Keep an evidence ledger with claims, sources, and open questions.
- Quote or cite only when needed.
- Do not re-open sources already summarized unless precision requires it.
- If the dominant waste is repeatedly loading the same long document, use
doc-to-lora-evaluatorbefore assuming a Doc-to-LoRA adapter is better than RAG, summaries, or direct context.
Anti-Patterns
- broad
greploops over the same code; - rereading README, config, and the same module repeatedly;
- restating the task after every command;
- proposing before reading enough code;
- validating everything when only one seam changed;
- carrying stale notes forward after the code changed.
Final Report
Include what changed, what was verified, and any remaining uncertainty. Do not spend tokens listing every trivial command unless it matters for continuation.
What ships with it: 2 files
2.1 KB alongside SKILL.md
agents/
- openai.yaml218 B
- SKILL_CARD.md1.9 KB