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.
npx agentmods add skills/fmind/dot/prompt-designnpx skills add fmind/dot --skill prompt-designgit clone --depth 1 https://github.com/fmind/dotWrote 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.
[](https://agentmods.dev/skills/fmind/dot/prompt-design)<a href="https://agentmods.dev/skills/fmind/dot/prompt-design"><img src="https://agentmods.dev/badge/skills/fmind/dot/prompt-design.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00043 | $0.01347 |
| Opus 5 | $0.00022 | $0.00674 |
| Sonnet 5 | $0.00009 | $0.00269 |
| Haiku 4.5 | $0.00004 | $0.00135 |
Grade A, and why
prompt-design 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 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.
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.
How it starts
The opening of the file, as written. The whole thing — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Design
Turn a behavioral contract into the smallest production prompt stack that expresses it, then hand a frozen candidate to agent-evaluation, which owns proof of behavior. agent-skills owns reusable SKILL.md packages and agent-project repository-level instructions.
Workflow
- Name the success contract: user-visible behavior, failure semantics, forbidden actions, measurable acceptance criteria, and the cheapest development cases that separate success from failure; product ambiguity goes to product-loop first.
- Inspect the runtime input: trace every layer in precedence order (provider rules, system and developer text, tenant customization, memory, tool schemas, retrieval, history, user input), note truncation and caching, and design against the runtime prompt, not a file.
- Pin the baseline: code revision, prompt hash, assembly implementation, model version, tool and output schemas, retrieval snapshot, context limit, sampling, retries, and known results; separate prompt changes from system changes.
- Partition context by lifetime: stable policy and tool contracts early and cacheable, tenant or session context in a bounded layer, volatile request state last; define precedence and deterministic truncation before the window fills.
- Write one behavioral contract: each instruction in one authoritative place, stating goal, constraints, decision authority, success criteria, failure behavior, and output contract; cut persona flourishes, repeated rules, and speculative edge cases.
- Encode authority and autonomy: what the agent may read, write, call, spend, send, or publish, which actions need confirmation, and when it must stop; conflicts resolve by explicit priority, never by recency or persuasive wording inside data.
- Design tool contracts: unique action-oriented names, a concise purpose, and the field list in tool contracts; expose only the tools relevant to the task.
- Make outputs machine-checkable: a typed schema or discriminated result variants with required fields, enums, nullability, evidence fields, and refusal or partial-success shapes; validate in code and fail closed.
- Choose examples at decision boundaries: the fewest examples that resolve an ambiguous rule, output shape, tool choice, or refusal, including hard negatives; never copy sealed evaluation cases into the prompt.
- Harden dynamic insertion: typed template parameters, explicit delimiters, length bounds, format-appropriate escaping, provenance labels, and deterministic placement; reject missing variables instead of emitting placeholders.
- Run static checks: render the candidate with representative values and inspect it in final order for contradictions, unknown tools or fields, schema-invalid examples, unresolved variables, authority inversion, and rules the runtime cannot enforce.
- Hand off to evaluation: diff and hash baseline and candidate, state one change hypothesis with its guardrails, freeze the candidate, and deliver the prompt candidate to agent-evaluation for paired trials.
What ships with it
2 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.
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.
- 2d ago First seen · 45 lines · 43 tokens per session scan A e16cd63d2074
prompt-design is a skill published in the GitHub repository fmind/dot (4 stars, last pushed yesterday), licensed MIT. It adds 43 tokens to every session and 1,347 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
prompt-design
Design production LLM or agent prompt stacks: instructions, tool contracts, examples, outputs, and runtime context. Use for precedence, conflicts, dynamic or untrusted context; prove behavior with agent-evaluation.
agent-evaluation
Evaluate stochastic LLM/RAG/model/retrieval/tool agents in trials. Compare baseline/candidate on development/sealed holdouts with calibrated deterministic/model/trace graders; measure reliability, variance, leakage, safety, and cost.
prompt-engineering
Draft, review, or improve a prompt for an LLM or agent against a research-backed rubric — explicit instructions, instruction/data separation, output contracts, reasoning scaffolds, grounding, verification, and evals. Use when writing a prompt, auditing one that misbehaves, or explaining a prompting principle.
go-stack
Build Go projects, libraries, CLIs, TUIs, web apps, or ADK agents with the standard package layout and pinned tooling.
python-stack
Build typed Python projects with uv, Ruff, ty, pytest, Litestar, and Typer. Use for packages, CLIs, web apps, tests, typing, or API verification.
k8s-local
Create and manage local Kubernetes clusters (k3d or kind) and deploy to them with kubectl, helm, helmfile, and skaffold. Use for local k8s cluster setup, dev loops, and debugging.