prompt-pack
Skill0 starsChanged 49 days agoMITView on GitHub
Compress a loose task description into a structured prompt for a different coding agent to run. Use when the user asks to turn a task into a prompt, pack it, or tighten it, and when a request is clearly destined for another agent rather than for you to execute.
3 other projects use a version of this.
Adopted by
Most recently taken up by
- thensls/nsls-builder-toolkittheir own version, first seen Nov 8, 2026
- harshil-1411/claude_plugin_video_studiotheir own version, first seen Oct 17, 2026
- nelsonwerd/prompt-pack-skilltheir own version, first seen Jun 11, 2026
Other versions3 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 3
- nelsonwerd/prompt-pack-skill★ 2same name
- harshil-1411/claude_plugin_video_studio★ 0same name
- thensls/nsls-builder-toolkit★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository17 other files it gives its agents.
- AGENTS.mdAGENTS.md
- skills/humanizer/AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- cherrypickerSkill
- copyable-markdownSkill
- deltamaxxSkill
- docs-mirrorSkill
- ffmpegSkill
- humanizerSkill
- socials-mirrorSkill
- spamcheckSkill
- tool-call-repairSkill
- writing-beatsSkill
- writing-for-agentsSkill
- writing-fragmentsSkill
- writing-shapeSkill
- yt-ccSkill
The file
We split this skill into the rules it gives an agent
2 rules. 0 are common, 1 is distinctive, and 1 is very specific to this project.
0
Common
Most of the 3 edited versions we read keep these.
1
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 19
Confirm target + mode: if unstated; default to an agent that executes, editing files itself.
- Very specific: line 21
Flag gaps, never invent: Don't fabricate file paths, frameworks, versions, or environment details (OS, distro, versions, hardware).
“Common” is measured: kept by at least 6 in 10 of the 3 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Prompt Pack
Turn a loose, conversational task description into a compact prompt for a different coding agent to run. The user orchestrates; the agent executes. A good pack is skimmable in seconds and noticeably shorter than the conversation it came from.
Optimize for
- Semantic core — cut pleasantries, hedging, backstory, repetition. Keep only what changes agent behavior.
- Execution order — read → analyze → produce. Front-load grounding before action.
- Anchored output — name the exact deliverable, its format, location, and a checkable acceptance condition. Unanchored prompts cause drift and invented work.
Workflow
- Extract intent from the description. Reuse any files, constraints, or stack already given in the conversation — don't make the agent rediscover them.
- Confirm target + mode if unstated; default to an agent that executes, editing files itself. (See Targets and modes.)
- Fill the template below — imperative voice, no prose paragraphs.
- Flag gaps, never invent. Don't fabricate file paths, frameworks, versions, or environment details (OS, distro, versions, hardware). If one is missing or a command depends on it, add a
# NEEDS: <thing>line at the top instead of guessing. - Return one fenced code block — copy-pasteable, with nothing after it except a one-line note if a
# NEEDSflag exists.
Output template
Exact skeleton. Omit a line only if it genuinely has no content; never pad.
# Objective
<one sentence: the end state, not the activity>
# Context
- Read first: <files/dirs to ground in, most specific first>
- Stack: <only constraining facts — lang, framework, versions IF KNOWN>
- Constraints: <scope guards, do-not-touch, conventions>
# Task
1. Read: <what to inspect before acting>
2. Analyze: <what to decide>
3. Produce: <what to write/change>
# Output
- Deliverable: <exact artifact + format + path>
- Acceptance: <verifiable done condition — tests pass, file exists, lint clean>- Objective = one sentence. Two means two tasks — say so.
- Read first is the anti-drift lever: list the narrowest set of files that pins the work.
- Acceptance must be checkable ("renders without console errors"), not aspirational ("looks good").
Targets and modes
Targets agentic coding tools with full repo access — they read the codebase and edit files themselves. Adjust only for mode:
- Execute (the agent edits files — default): template as-is.
- Plan (the agent only reads and proposes): append
Output a plan only; do not edit files., change Deliverable toan ordered implementation plan, and emphasize the Analyze step.
Unfamiliar target: confirm it has autonomous file access before using this template; if not, ask how they intend to run the prompt.
Example
Input: "my Astro site's mobile nav is a mess — the hamburger works but the menu doesn't trap focus and screen readers skip it. It's in the components folder somewhere."
# Objective
Make the mobile nav menu keyboard- and screen-reader-accessible.
# Context
- Read first: src/components/ (locate the nav/hamburger component)
- Stack: Astro
- Constraints: WCAG 2.1 AA; don't restyle; preserve hamburger toggle behavior
# Task
1. Read: the nav component and its open/close logic
2. Analyze: focus trap while open, focus restore on close, ARIA roles/state for toggle and menu
3. Produce: the accessibility fixes in place
# Output
- Deliverable: edited nav component file(s)
- Acceptance: focus trapped while open; Esc closes and restores focus to toggle; toggle exposes aria-expanded; menu announced by screen readersFor Plan: same pack with Deliverable: an ordered implementation plan, an inventory-style Acceptance, and a trailing Output a plan only; do not edit files.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

