prompt-engineering

prompt-engineering is a skill for Claude Code, Codex from doodledood/manifest-dev. It costs 56 tokens per session (2,156 once invoked), scanned A, original, MIT.

A guide for writing, improving, and reviewing instructions given to language models, including system prompts, skills, and agents.

In plain words
What is it for?
It helps decide what an instruction should say, where it should appear, and whether it belongs in the prompt at all.
Why use it?
It helps remove unnecessary instructions and keep the lines that prevent known mistakes or express real requirements.

Skill for Claude CodeCodex

Part of the manifest-dev-tools plugin — 11 skills shipped together

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 skills/doodledood/manifest-dev/prompt-engineering
Any agent
npx skills add doodledood/manifest-dev --skill prompt-engineering
Clone the repo
git clone --depth 1 https://github.com/doodledood/manifest-dev

Made for: Claude Code, Codex.

Or install manifest-dev-tools, the plugin that ships this one along with the rest of its 11 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for prompt-engineering

README.md
[![agentmods](https://agentmods.dev/badge/skills/doodledood/manifest-dev/prompt-engineering.svg)](https://agentmods.dev/skills/doodledood/manifest-dev/prompt-engineering)
Your own site
<a href="https://agentmods.dev/skills/doodledood/manifest-dev/prompt-engineering"><img src="https://agentmods.dev/badge/skills/doodledood/manifest-dev/prompt-engineering.svg" alt="Measured on agentmods" height="20"></a>
Per session 56 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,156 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. 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 5.1 $0.00056 $0.02156
Opus 5 $0.00028 $0.01078
Sonnet 5 $0.00011 $0.00431
Haiku 4.5 $0.00006 $0.00216

Measured 5d ago against content hash 3bed3e263e84, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

prompt-engineering scanned grade A with 0 findings 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 5d 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.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

claude-plugins/manifest-dev-tools/skills/prompt-engineering/SKILL.md · 92 lines

How it starts

The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.

A prompt states a goal and gets out of the way. Everything else in it is load — spent on every run, taken from the attention the model would otherwise put on the work — so each line has to buy more than it costs. What follows decides three things: whether a line exists, where it sits, and how it is worded.

Where a line came from

Ask this before asking whether the line helps. A line can be true, relevant, and still worth cutting, so usefulness does not settle it. Provenance does, and you can answer it from memory:

  • The user ruled it — a decision, preference, or constraint they stated. Nothing derives it. Keep.
  • The world holds it, out of the run's reach — an unwritten convention, a failure you observed, a fact outside what the run will read. Keep, and prefer a pointer to the source over a copy that can go stale.
  • It counteracts a default you have watched the model take — keep, when the default is observed rather than feared.
  • You worked it out from material the run will also read — cut. A conclusion available in the corpus is one the run reaches itself, so writing it down buys nothing and spends load on every invocation.

That last class is the one that feels most earned, because you did the work to reach it, and it is where over-specification comes from. Two tells: a line you could justify from the same sources the run gets, and a sentence arguing for the instruction above it — an instruction does not need advocacy, and the run is not persuaded by it.

A choice the request left open belongs to whoever made the request. Some of what you would add is not padding but a decision — what ranks one candidate over another, an exception to a rule they gave you, a deliverable beyond what they asked for. It reads as thoroughness and it commits them to something they never chose, under their name, in a file they will quote back later. Ask where the answer would change the work; leave it out where it would not.

A finished prompt looks thinner than you expect it to, and that is the result rather than a warning sign. Two rulings and a goal make a short document; the pull at that point is to fill it out — a section on how to rank, a definition of the term in its own name, a third source of evidence nobody asked for — and everything reached for that way comes from the class above. When it reads as too slight, check the goal is stated and the rulings are in it, then ship it.

The older question — would the model do this without the line? — asks for a prediction about the model's own counterfactual behaviour, which is not reliably answerable from the inside. Ask it second, of what provenance already admitted: it can still cut a line provenance allowed, never keep one provenance rejected.

When two people disagree about whether a line is doing work, the argument is about the model's default, so settle it by running the document with the line removed and comparing. That is a tiebreak available when it is worth the trouble, not a step every line has to pass.

Read the full file on GitHub · 92 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 5d ago First seen · 92 lines · 56 tokens per session scan A 3bed3e263e84

Subscribe to this mod's changes

prompt-engineering is a skill published in the GitHub repository doodledood/manifest-dev (72 stars, last pushed today), licensed MIT. It adds 56 tokens to every session and 2,156 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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