prompt-engineering is agent-read markdown (skill) from nahid-sparktales/agent-dispatcher: Write or revise a prompt so it holds up — output contract, instruction placement, examples that earn their place, an escape hatch for bad input — and measure the change against a saved set of cases instead of one good-looking run. Use when a prompt is being authored or patched, when output is inconsistent or the wrong shape, when a model or version changes, or when someone reports a prompt as fixed. Not for scoping an agent's job and tools, not for deciding what material to load into the window,.
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
# Prompt engineering
A prompt that worked once is an anecdote. Almost every "fixed" prompt was judged on the input that
motivated the edit, on a single run, against no recorded baseline.
## When this fires
Authoring or editing any prompt whose output something depends on, and any time a prompt is
declared improved. It does not fire for a one-off question you ask and read yourself.
## Procedure
1. **Collect cases before editing.** At least five real inputs, including the two that fail now and
two that currently pass and must keep passing. No cases means no measurement is possible — say
that plainly instead of shipping an eyeballed change.
2. **Run the baseline and record it.** Every case, actual output saved, pass or fail marked. This
is the only thing a later claim of improvement can be checked against.
3. **Write the output contract first.** Exact shape, field names, ordering, units, and what the
output looks like when the model cannot comply. Unspecified format is the single most common
defect, and it is invisible until something downstream parses it.
4. **Separate durable instruction from variable data.** Keep the standing rules in one place and
…
Pin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
GET https://markdownregistry.com/api/v1/artifacts/art_iqwqo3fkjn7m6lfp
GET https://markdownregistry.com/api/v1/resolve?ref=nahid-sparktales/agent-dispatcher/prompt-engineering
GET https://markdownregistry.com/api/v1/blob/f61d86afdb407491aa6a253395a0ff10f7b0ddaa5246a9cb77ac5d4a86e5b05e
Your agent does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
nahid-sparktales/agent-dispatcher · skills/agent-dispatcher/SKILL.md · Route work to a specialist role and load its task-specific guidance. Use when the user invokes /agent-dispatcher, names…
nahid-sparktales/agent-dispatcher · skills/ai/agent-design/SKILL.md · Scope an agent or subagent before it is built — the one job it owns, the smallest tool set that closes that job, what…
nahid-sparktales/agent-dispatcher · skills/ai/agent-evals/SKILL.md · Build an eval suite that can actually detect a regression — cases pulled from real traffic, graders that check…
nahid-sparktales/agent-dispatcher · skills/ai/context-engineering/SKILL.md · Decide what actually occupies the model's window — progressive disclosure through an index, retrieval versus inlining…
nahid-sparktales/agent-dispatcher · skills/ai/llm-observability/SKILL.md · See what an agent actually did — one trace per run with nested model, tool and retrieval spans, token and latency…
nahid-sparktales/agent-dispatcher · skills/ai/mcp-design/SKILL.md · Build an MCP server, or bring an existing one into a project — choosing the transport, deciding which tools, resources…
nahid-sparktales/agent-dispatcher · skills/ai/memory-design/SKILL.md · Decide what an agent should remember, which layer holds it, who it is scoped to, and how a stale or contradicted memory…
nahid-sparktales/agent-dispatcher · skills/ai/model-routing/SKILL.md · Pick a model per job and degrade sensibly when one fails — a quality bar per call site, candidates compared on the same…
nahid-sparktales/agent-dispatcher · skills/ai/prompt-injection-defense/SKILL.md · Treat everything an agent reads but did not author as data rather than instructions — an explicit trust boundary, a…
nahid-sparktales/agent-dispatcher · skills/ai/retrieval-rag/SKILL.md · Build and fix retrieval that actually returns the right passage — structure-aware chunking, one pinned embedding model…
nahid-sparktales/agent-dispatcher · skills/ai/structured-output/SKILL.md · Get parseable, trustworthy structured results out of a model — schema design, the enforcement mechanism the provider…
nahid-sparktales/agent-dispatcher · skills/ai/tool-design/SKILL.md · Design the tools a model calls — names, parameter shapes, what a result returns, and error text written as an…
giuseppe-trisciuoglio/developer-kit · plugins/developer-kit-ai/skills/prompt-engineering/SKILL.md · Provides workflows to write, debug, and optimize prompts for LLMs, including few-shot example selection…
codealive-ai/ai-driven-development · skills/prompt-engineering/SKILL.md · Universal prompt engineering techniques for any LLM. Use when crafting, optimizing, or reviewing prompts for AI models…
doodledood/manifest-dev · claude-plugins/manifest-dev-tools/skills/prompt-engineering/SKILL.md · Create, update, review, or discuss an LLM prompt — a system prompt, a skill, or an agent. Use when writing or improving…
vodailocz/kilo-kit-mcp · skills/engineering/prompt-engineering/SKILL.md · Use when designing, optimizing, testing, or deploying robust prompt systems for AI agents. This skill provides…
yelmuratoff/agent_sync · example/.ai/src/skills/prompt-engineering/SKILL.md · Design, debug, and refine prompts for LLM-based coding tools, agents, and pipelines. Use this skill when writing or…
