wheakerd

optimize-agent-usage

Reduce or diagnose agent usage of tokens, credits, and context without weakening task quality or safety. Use only when the user explicitly asks to save credits, tokens, or usage limits in the current agent host; optimize Skills, instruction files such as AGENTS.md or CLAUDE.md, MCP, or context loading; explain unusually high usage; or design a lower-consumption workflow. Do not use for ordinary coding, software performance work, or generic cost questions.

wheakerd 1 Actualitzat fa 1 d

Recursos

2
GitHub

Instal·la

npx skillscat add wheakerd/axiom/optimize-agent-usage

Instal·la des del registre de SkillsCat.

SKILL.md

Optimize Agent Usage

Reduce total agent consumption while preserving the requested outcome,
authorization boundaries, safety checks, and completion evidence.

Load Policy

  • For a conceptual explanation or a narrow current-product fact, use this file
    and the current host's official documentation only. Do not inspect a
    repository or load the audit reference unless the request needs it.
  • For an audit, design change, or implementation involving Skills, instruction
    files, MCP, context, history, tools, validation, or reporting, read
    references/context-audit.md before changing files.
  • Add another Axiom skill only when the requested implementation crosses into
    that skill's distinct owner: an AGENTS instruction system, traceable Git
    state, or a persistent system change. Usage analysis alone needs only this
    skill.

Measurement Boundary

  • Use exact token, credit, cache, reasoning, or tool metrics only when the host
    exposes them for the scoped run. Axiom cannot recover hidden usage data.
  • Otherwise label UTF-8 bytes, words, lines, reference counts, tool calls, and
    route-chain sizes as proxies or estimates.
  • Do not claim a percentage saving without a repeatable before/after workload
    and equivalent quality acceptance.
  • Fetch the current host's official documentation only when a volatile host
    fact affects the decision. Never place current prices, plan limits, or model
    quotas in always-loaded instructions.

Non-Negotiable Boundaries

  • Define the required quality, safety, authorization, rollback, and evidence
    bar before optimizing. Lower resource use counts only when that bar still
    passes.
  • Do not remove required tests, rollback checks, secret handling, destructive
    safeguards, or outcome-owning evidence to reduce context or calls.
  • Do not automatically change the model, reasoning effort, execution mode, or
    host configuration. Offer a bounded comparison only when the user requests
    usage optimization and the host supports the choice.
  • Preserve unrelated work. Do not reset, stash, clean, stage, commit, push,
    deploy, install tools, change user Skills, or mutate remote state without
    separate authorization.
  • Do not add telemetry, background work, automatic updates, or network checks.

Optimization Order

  1. Bound the target workflows and representative requests.
  2. Measure always-loaded and common-path context before rare worst cases.
  3. Inventory metadata and route edges before reading bodies.
  4. Read only candidate owners, then remove duplication or route detail behind
    one directly discoverable on-demand reference.
  5. Reduce repeated tool calls, checks, updates, and reports only where the
    next decision does not depend on each intermediate result.
  6. Re-run the same routing, authorization, safety, and outcome scenarios.
  7. Stop when further reduction would trade away required quality or evidence.

Prefer precise trigger descriptions, one canonical owner per rule,
phase-specific references, bounded retries, conclusive stopping conditions,
and concise evidence-led reporting. Keep ordinary no-match work outside Axiom.

Report

Lead with the implemented or recommended outcome. Include affected files or
surfaces, comparable before/after measurements, validation results, and real
unverified gaps. Omit repeated rationale, routine successful details, and exact
usage claims the host did not expose.