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Agent skill

Prompt Optimizer

by cbrock84 in cbrock84/headcount

Turns rough intent or a weak prompt into a reliable one — diagnosing why output is inconsistent, restructuring the instruction, and adapting it across models.

MITAuto-check passedAI & LLM Engineering

Install Prompt Optimizer

skills CLI
$ npx skills add cbrock84/headcount --skill prompt-optimizer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install cbrock84/headcount prompt-optimizer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/cbrock84/headcount.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/technology/skills/prompt-optimizer .claude/skills/prompt-optimizer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
prompt-optimizer
GitHub stars
2k
Token cost
~1.2k tokens
SKILL.md length
670 words
Files
1
Skills in repo
178
Repo updated
First seen
Licence
MIT

At a glance

Turns rough intent or a weak prompt into a reliable one — diagnosing why output is inconsistent, restructuring the instruction, and adapting it across models.

  • Tasks that involve Prompt engineering
  • SKILL.md covers Diagnose before rewriting, What reliably helps, What does not help and Structure, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Prompt Optimizer is an agent skill from cbrock84/headcount. Turns rough intent or a weak prompt into a reliable one — diagnosing why output is inconsistent, restructuring the instruction, and adapting it across models. Use this when a prompt is not producing what was wanted, when output varies run to run, when writing a prompt for a repeated task, when moving a prompt between models, or when someone describes what they want an AI to do and needs it written properly.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Prompt engineering. The repository describes itself as: An agent organization structured as a company — 15+ departments, 125+ skills, each independently installable, citing the standards and regulators that settle the question. Runs… The licence is MIT.

When your agent uses it

  • Tasks that involve Prompt engineering

Example prompts

  • “Use the prompt-optimizer skill to turn rough intent or a weak prompt into a reliable one — diagnosing why output is inconsistent, restructuring the…”
  • “/prompt-optimizer”

What it can do on your machine

Read from SKILL.md and the folder at commit 98d1c17. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Prompt Optimizer loads about 1.2k tokens when it runs. Until then it costs about 107 tokens; SKILL.md has 670 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~107
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from cbrock84/headcount at commit 98d1c17, republished under its MIT licence (© cbrock84). 670 words, ~1,190 tokens.

Download SKILL.mdSave it as .claude/skills/prompt-optimizer/SKILL.md (or your agent's skills folder).
name
prompt-optimizer
description
Turns rough intent or a weak prompt into a reliable one — diagnosing why output is inconsistent, restructuring the instruction, and adapting it across models. Use this when a prompt is not producing what was wanted, when output varies run to run, when writing a prompt for a repeated task, when moving a prompt between models, or when someone describes what they want an AI to do and needs it written properly.

Prompt optimizer

Diagnose before rewriting

Bad output has a small number of causes, and the fix differs entirely by cause:

  • Underspecified — the model is guessing at something you know. Most common by far.
  • Overspecified — so many constraints that they conflict, and the model satisfies some arbitrarily.
  • Wrong shape — asking for a paragraph when you want a table, or a decision when you want options.
  • No success criterion — nothing in the prompt says what good looks like, so quality varies with nothing.
  • Buried instruction — the actual task is in the middle of context and gets weighted like context.
  • Genuinely hard — the task needs information the model does not have, and no prompt fixes that. Say so rather than iterating.

Read the actual bad output before rewriting. The failure mode names the cause.

What reliably helps

  • Say what to do, not what to avoid. Negative instructions are weaker than positive ones and often summon the thing named.
  • Give the shape of the output — the sections, the length, the format. If format matters, show an example rather than describing it.
  • Provide one worked example where the task is judgment-heavy. Examples carry more instruction per token than description does, and one good one beats three mediocre.
  • State the audience and purpose. "For a technical reader deciding whether to adopt this" changes the output more than most adjectives.
  • Ask for reasoning before the answer on analytical tasks — order matters, since a conclusion stated first is defended rather than derived.
  • Give an out. Tell it what to do when the input is insufficient, or it will invent something.

What does not help

Politeness, threats, incentives, insisting on importance, and stacked superlatives. These consume tokens and change little. So does repeating an instruction in three phrasings — it usually signals the instruction is unclear rather than reinforcing it.

Structure

Put the task first, context second, and any output format last where it will be closest to generation. Long context between instruction and output is where instructions get lost.

For repeated prompts, separate the fixed instruction from the variable input explicitly, so the model can tell which is which.

Testing

A prompt is not done because one run looked good. Run it three to five times on the same input and look at the variance — that is the actual quality. Then run it on the awkward inputs: empty, far too long, ambiguous, adversarial.

Fix the worst case, not the average. The average is what you see in testing; the worst case is what your users see.

Show full SKILL.md (253 more words)Show less

Producing variations

When a prompt matters enough to optimize, produce genuinely different versions rather than variants of one phrasing:

  • Terse — instruction only, minimal framing. Often outperforms, and it is cheapest.
  • Structured — explicit sections, numbered constraints, defined output shape.
  • Exemplar-led — one worked example carrying most of the instruction.
  • Role-framed — audience and stance set before the task.

Test all four on the same inputs. Which wins is genuinely hard to predict, and the intuition that a longer prompt is better is wrong about as often as it is right.

Scoring output

Judge against criteria written before seeing results, or you will rationalize whatever came back. For most tasks: did it do the task, is it correct, is it the right shape and length, is it usable without editing. Score each run rather than forming an overall impression — impressions are dominated by the best run, and the worst run is what matters.

Moving between models

Do not assume a prompt transfers. Models differ in how they weight system versus user instruction, how they handle long context, and how they respond to formatting. Re-test on the target model, and be especially suspicious of prompts tuned through many small iterations — those are often fitted to one model's quirks.

Never

  • Rewrite a prompt before reading the failing outputs. You will fix the wrong thing.
  • Change more than one thing between test runs.
  • Judge a change on a single output. Sampling variance will fool you.
  • Keep an instruction because it was already there. Every line has to earn its tokens.

© cbrock84, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugins/technology/skills/prompt-optimizer of cbrock84/headcount.

Open the folder on GitHubat commit 98d1c17

Compare with similar skills

Prompt Optimizer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Prompt Optimizer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Prompt Optimizer this skillcbrock84/headcount2k—~1.2kAutomated safety check: PassMIT
Prompt Improverseverity1/claude-code-prompt-improver1.9k1 repos~1.7kAutomated safety check: PassMIT
Prompt Engineering Patternsynulihao/AgentSkillOS61714 repos~1.7kAutomated safety check: PassNone
Patch CreationPiebald-AI/tweakcc2.5k—~1.6kAutomated safety check: PassMIT
Senior Prompt Engineermaslennikov-ig/claude-code-orchestrator-kit2603 repos~1.4kAutomated safety check: PassCustom licence
Codex Fable5baskduf/FableCodex437—~1.6kAutomated safety check: PassAGPL-3.0

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Categories

Questions about Prompt Optimizer

What does Prompt Optimizer do?

Turns rough intent or a weak prompt into a reliable one — diagnosing why output is inconsistent, restructuring the instruction, and adapting it across models. Prompt Optimizer is an agent skill from cbrock84/headcount. Turns rough intent or a weak prompt into a reliable one — diagnosing why output is inconsistent, restructuring the instruction, and adapting it across models.

When should I use Prompt Optimizer?

Prompt Optimizer fits situations like: tasks that involve Prompt engineering.

How do I install Prompt Optimizer in Claude Code?

Run `npx skills add cbrock84/headcount --skill prompt-optimizer -a claude-code`. Or copy the skill folder (plugins/technology/skills/prompt-optimizer in cbrock84/headcount) into .claude/skills/prompt-optimizer in your project. Claude Code loads it when a task matches its description.

How do I install Prompt Optimizer in Codex?

Run `npx skills add cbrock84/headcount --skill prompt-optimizer -a codex`. Or copy the skill folder (plugins/technology/skills/prompt-optimizer in cbrock84/headcount) into .agents/skills/prompt-optimizer in your project. Codex loads it when a task matches its description.

Can I use Prompt Optimizer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add cbrock84/headcount --skill prompt-optimizer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/prompt-optimizer, .gemini/skills/prompt-optimizer, .github/skills/prompt-optimizer and .opencode/skills/prompt-optimizer in your project.

What does Prompt Optimizer need to run?

SKILL.md names no scripts, command-line tools or credentials: Prompt Optimizer is instructions for the agent only.

Does Prompt Optimizer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Prompt Optimizer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Prompt Optimizer use?

Prompt Optimizer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Prompt Optimizer use?

About 1.2k tokens (SKILL.md is roughly 4.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Prompt Optimizer?

Skills that share tags, products or a category with Prompt Optimizer: Prompt Improver (severity1/claude-code-prompt-improver, 1.9k stars), Prompt Engineering Patterns (ynulihao/AgentSkillOS, 617 stars), Patch Creation (Piebald-AI/tweakcc, 2.5k stars) and Senior Prompt Engineer (maslennikov-ig/claude-code-orchestrator-kit, 260 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Prompt Optimizer?

cbrock84 (a GitHub user) maintains it in cbrock84/headcount, which has 2,016 GitHub stars. The repository holds 178 skills in this directory. The repository was last updated on September 17, 2026.

Source: cbrock84/headcount on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.