agentsclimarketplace

Token usage audit

Skill markoblogo/abvx-agent-skills/skills/token-usage-audit

Reviewable capability layer for coding agents. Portable skills, delivery gates, workflow patterns, and verification-first engineering.

Install
npx -y skills add markoblogo/abvx-agent-skills --skill token-usage-audit

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 4 stars4 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Audit where tokens are being wasted across startup context, shell output, repeated file reads, bloated agent docs, oversized summaries, and compaction loss. Use when token budget is tight, sessions are degrading, or an agent setup needs measurement-driven optimization.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

2.1 KB, as published. Nobody here has run it

Token Usage Audit

Save tokens by measuring the waste categories before optimizing them.

Waste Categories

Audit these in order:

  1. startup-context weight;
  2. shell and tool-output noise;
  3. repeated file reads or re-analysis;
  4. oversized handoffs and summaries;
  5. stale skills, prompts, or always-loaded docs;
  6. compaction loss and re-derivation cost.

Audit Workflow

  1. Identify the current failure mode:
    • output overflow;
    • startup bloat;
    • long-session decay;
    • repeated repo rediscovery.
  2. Estimate which category dominates.
  3. Apply the narrowest corrective skill:
    • rtk-assisted-shell or shell-output-compaction;
    • graph-guided-code-reading;
    • token-efficient-execution;
    • lean-context-layout;
    • compaction-survival;
    • token-frugal-mode.
  4. Re-check after the intervention:
    • fewer broad reads;
    • shorter command output;
    • smaller startup docs;
    • cleaner resume state.
  5. Record what changed so the same waste is not rediscovered next session.

Practical Signals

  • top-level agent file is large and generic;
  • same README or module keeps getting reopened;
  • shell output dominates the conversation;
  • compaction causes “what were we doing?” recovery turns;
  • too many installed skills are irrelevant to current work;
  • long status updates cost more than the actual edit.

Deliverable

Report:

  • biggest waste category;
  • corrective skills used;
  • what was changed;
  • expected future savings.

Guardrails

  • Do not chase precision that costs more tokens than it saves.
  • Do not instrument everything if a clear dominant waste category is already visible.
  • Optimize the highest recurring cost first, not the most novel one.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.