heavy-build
Skill4 starsChanged 5 months agoMITView on GitHub
Author a new heavy prompt for your heavy/library/. Interviews you for the goal and shape, decomposes it into phases → idempotent steps with anchored output sections, encodes defaults so the run never needs to ask, assigns the next ID, writes the file, and updates the library INDEX. Run /heavy-review after.
In this repository18 other files it gives its agents.
- biz-validateSkill
- braindumpSkill
- catch-upSkill
- clone-websiteSkill
- double-checkSkill
- explain-for-grandmaSkill
- heavy-reviewSkill
- heavySkill
- make-promptSkill
- monitor-claudeSkill
- orientSkill
- overnightSkill
- reelSkill
- sanity-checkSkill
- save-sessionSkill
- shipSkill
- what-breaksSkill
- yt-tranSkill
The file
We split this skill into the rules it gives an agent
5 rules. About 0 are common, 3 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 13
Read these so the new prompt matches exactly:
- Very specific: line 19
Use AskUserQuestion (or plain prose Q&A) to settle, in this order — batch related questions:
- Distinctive: line 29
If the user is vague, propose a concrete decomposition and confirm it — don't leave it open.
- Very specific: line 52
Add a row to heavy/library/INDEX.md (ID, Title, Goal, Cost, Subagents, Output dir).
- Distinctive: line 58
Do not commit unless the user asks (or follow the repo's normal commit discipline if they do).
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Heavy-prompt builder
Authoring tool. Runs with you present, so asking questions here is fine — the goal is to produce
a heavy prompt that is then launched-and-left with zero questions. Work in your project repo
(current working directory). Paths below are relative to its root (the library lives under heavy/).
Step 1 — Load the conventions
Read these so the new prompt matches exactly:
heavy/library/_TEMPLATE.md— the structure every prompt must follow (frontmatter + 7 sections).heavy/README.md— conventions (anchors, append-only, shared vocab, no-AskUserQuestion-mid-run, subagents off).heavy/library/INDEX.md— to find the next ID (highest existing NN + 1, zero-padded).
Step 2 — Interview the user
Use AskUserQuestion (or plain prose Q&A) to settle, in this order — batch related questions:
- Goal — the one outcome the run must produce (if
$ARGUMENTSwas given, start from that). - Context — what problem it serves; which repo files/areas it touches.
- Shape — roughly what phases/stages the work has (you'll refine into steps).
- Subagents — almost always
false(default; keeps the whole run in one session). Onlytrueif the user explicitly wants subagents and accepts that spawned agents add complexity and may bill differently from the interactive session. - WebSearch — does it need the web? set a sane
websearch_max(0 if not). - Cost — rough
est_cost(low/medium/high) from the scope.
If the user is vague, propose a concrete decomposition and confirm it — don't leave it open.
Step 3 — Decompose into phases → steps
- Break the job into ordered phases; each phase = a list of idempotent steps.
- Give every step an id
<phase>.<step>and a single defined output (a file + an anchored section<!-- SEC:id -->). Use the shared vocabulary where it fits:summary,findings.*,decisions,actions,assumptions,sources. - The last phase is always
synthesize(write/finish00-summary.md, regenerateMAP.md, updateheavy/output/INDEX.md).
Step 4 — Encode defaults for EVERY decision
This is the most important step. The run is unattended, so for any choice the prompt would otherwise
ask about, write the default into the prompt plus the rule "when uncertain, take the default and
record it under <!-- SEC:assumptions -->." A heavy prompt that needs a human mid-run is broken.
Step 5 — Write the prompt file
Copy _TEMPLATE.md structure into heavy/library/NN-slug.md (slug = short kebab-case of the goal),
filled in: frontmatter (id, title, goal, output_dir: heavy/output/NN-slug/, phases,
subagents, websearch_max, est_cost) + GOAL, CONTEXT, PHASES, OUTPUT SPEC (anchored, append-only),
GUARDRAILS, RESUME (read STATUS.json), DONE (checkable criteria). Be fully self-contained — every path,
every default, every output file named.
Step 6 — Register it
Add a row to heavy/library/INDEX.md (ID, Title, Goal, Cost, Subagents, Output dir).
Step 7 — Hand off to review
Tell the user the prompt is drafted and recommend /heavy-review NN to catch logical gaps before a
real run. Offer to run the review now. Do NOT launch the heavy prompt itself from here — that's /heavy NN.
Do not commit unless the user asks (or follow the repo's normal commit discipline if they do).
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.

