agent-skill-description-optimizer AGENTS.md

Repository instructions for improving an agent skill's description—the text that determines when the skill is selected.

In plain words
What is it for?
They help evaluate and update skill descriptions with the repository's optimizer, including previewing results and applying a backed-up change.
Why use it?
They define the required commands, inputs, and output handling so description optimization can be run consistently and safely.

Instructions file for CodexOpenCode

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add instructions/netopsengineer/agent-skill-description-optimizer/agents-md
Clone the repo
git clone --depth 1 https://github.com/netopsengineer/agent-skill-description-optimizer

Made for: Codex, OpenCode.

Per session 4,889 This file is loaded in full into every session.
When invoked 4,889 The same file — it is already loaded in full.
Security scan C 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.04889 $0.04889
Opus 5 $0.02445 $0.02445
Sonnet 5 $0.00978 $0.00978
Haiku 4.5 $0.00489 $0.00489

Measured 2d ago against content hash 4aefe38b595a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade C, and why

agent-skill-description-optimizer AGENTS.md scanned grade C with 1 finding 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 2d 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.

Recursive force deletehighDestructive command

rm -rf with a variable or a broad path is one typo away from removing the wrong tree.

Clean up: `rm -rf "$SCRATCH"`.
AGENTS.md · 385 lines

How it starts

The opening of the file, as written. The whole thing — 385 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md

Agent execution contract for this repo. Human explanation is canonical in README.md; reference it rather than duplicating it, and keep operator narration out of this file.

Purpose

Optimize a skill's description: frontmatter (the text that gates skill invocation). Evaluation and improvement both run through claude -p, so no ANTHROPIC_API_KEY is required. Entry point: optimize_description_v2.py (shim over the src/skill_optimizer/ package). Runtime: Python standard library only; interpreter floor Python 3.14+.

Invoke

Canonical cross-repo call (project-targeted uv pins the 3.14 interpreter):

uv run --project /ABSOLUTE/PATH/TO/agent-skill-description-optimizer \
  optimize-skill-description \
  --eval-set /ABSOLUTE/PATH/TO/eval.json \
  --skill-path /ABSOLUTE/PATH/TO/skill \
  --model MODEL_ID --report none --max-iterations 5 --verbose
  1. Pass --report none for every headless run: stdout stays a single JSON object and no browser opens. Mandatory for a stdout-parsing caller.
  2. Parse stdout as JSON; read best_description.
  3. Apply it to the skill's SKILL.md frontmatter, or pass --write to apply it automatically (backs up to SKILL.md.bak; refuses an over---max-desc-chars winner).
  4. Treat stderr as progress only — tail it, never parse it.

Equivalent in-repo forms: uv run optimize-skill-description ..., uv run python -m skill_optimizer ..., uv run python optimize_description_v2.py .... Direct execution requires a named 3.14+ interpreter (python3.14 optimize_description_v2.py ...). Full flag list: README.md.

Preconditions

Checked at startup, before any token spend or artifact write. Each failure exits 1 with a one-line stderr message and empty stdout (a stdout-parsing caller fails legibly, never on a JSONDecodeError or mid-run traceback):

Condition Failure message prefix
claude on PATH and executable claude CLI not found or not executable:
Interpreter is Python >=3.14 Requires Python >=3.14;
Eval set well-formed Invalid eval set:
Holdout split satisfiable Invalid holdout split:
--out and --results-dir not both set --out and --results-dir are mutually exclusive
--iterations in [0, 50] --iterations must be between 0 and 50

Read the full file on GitHub · 385 lines

Changes

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.

  1. 2d ago First seen · 385 lines · 4,889 tokens per session scan C 4aefe38b595a

Subscribe to this mod's changes

agent-skill-description-optimizer AGENTS.md is an instructions file published in the GitHub repository netopsengineer/agent-skill-description-optimizer (2 stars, last pushed 4d ago), licensed MIT. It adds 4,889 tokens to every session, about $0.0244 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.