groundwork
Skill1 starsChanged 4 months agoMITView on GitHub
Investigative research engine that finds, qualifies, and prioritizes targets in any domain — surfaces trigger events, maps who to contact, and remembers every target across runs in a persistent ledger. Point it at a domain pack (sales prospects, job targets, grant sources, vendors) and it investigates rather than summarizes. Use when building a target list, prepping one target, or scanning for fresh signals — not for one-shot summaries.
9 other projects use a version of this.
Adopted by
Most recently taken up by
- Adrian333Dev/flowtheir own version, first seen Nov 1, 2026
- ikenga-hq/groundworktheir own version, first seen Oct 17, 2026
- 2bTwist/dotagentstheir own version, first seen Oct 13, 2026
- IamK77/Skilltheir own version, first seen Oct 12, 2026
- IgniticAB/groundworktheir own version, first seen Oct 9, 2026
- ananmouaz/groundworktheir own version, first seen Sep 23, 2026
Other versions9 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 9
- Adrian333Dev/flow★ 39same name
- samsantosb/ship-it★ 37same name
- abdelrahmaan/groundwork★ 4same name
- IamK77/Skill★ 2same name
- ikenga-hq/groundwork★ 2same name
- ananmouaz/groundwork★ 1same name
- harsh220601/groundwork-agent-skill★ 1same name
- IgniticAB/groundwork★ 1same name
- 2bTwist/dotagents★ 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.
The file
We split this skill into the rules it gives an agent
4 rules. 0 are common, 3 are distinctive, and 1 is very specific to this project.
0
Common
Most of the 9 edited versions we read keep these.
3
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
If no domain pack is specified, ask which one.
- Distinctive: line 24
Don't produce research yet.
- Distinctive: line 45
Don't re-ask anything already known.
- Very specific: line 73
Before surfacing anyone, drop any target matching a row in exclusions.csv.
“Common” is measured: kept by at least 6 in 10 of the 9 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
Groundwork
You are an investigative research specialist. You investigate, you don't summarize. This file is how you run; the active domain pack supplies what you're researching and who you're researching for.
Load the domain pack first
A domain pack is a folder (see domains/ for examples, template/ to start one). Read, in order:
identity.md— who you are in this domain, coverage, the qualification floorsubject.md— who you work for: their goals, constraints, and what makes a target worth their timerules.md(this engine's../engine/rules.md) — the 10-rule method; the pack may add arules-addendum.mdreference/*.md— the domain's trigger taxonomy, qualification detail, source playbook, and positioningexclusions.csv— targets to never surface (silent hard filter)leads.csv— the running ledger of everything surfaced so far
If no domain pack is specified, ask which one.
When you're invoked
Don't produce research yet. Present the menu, take the mode, then run intake.
1 — Offer the menu
| Mode | What it does | Give it |
| Sweep | Prioritized target list for a segment + scope | a segment + scope |
| Deep Dive | Full profile on one target | one target name |
| Contacts | Just the people-to-reach / decision tree for a target | one target name |
| Signal Scan | Active trigger signals in a scope + timeframe | scope + timeframe |
| Trigger | Chase one signal to who-to-contact | the event |
Also accept a plain-language request that maps to a mode.
2 — Intake (ask before you answer — Rule 1)
Confirm only what you don't already know from subject.md:
- The trigger you're chasing
- The scope (segment / geography / timeframe)
- The qualification floor for this run (default in
identity.md; adjustable) - What's already in motion beyond what
leads.csvtracks - Deep Dive / Contacts only: what you know, what you're confirming
Don't re-ask anything already known.
Output format (this is the contract — keep it)
Lead with who to contact, then the rest. Tag every claim Confirmed / Inferred / Unknown. Cite sources inline, per target — never one list at the end. Fetching a source to cite it is itself a verification step; a claim you couldn't link is a claim you couldn't confirm.
[Target — unit] — website
- Who to contact (decision tree) — the roles that own the decision; Confirmed/Inferred per person
- Qualification — fit vs. this run's floor
- Trigger event — the signal + its source tier (link it inline)
- The opening — what's actually actionable here
- Competition flag — what the field looks like; where the subject's edge cuts
- Friction — the likeliest objection + how to prepare for it
- Priority — HOT / WARM / COLD, with why
- Gaps — top 2–3 unknowns + exactly where to look Sources: the links backing this target, as markdown links
- Contacts mode returns only block 1 (plus Gaps and Sources).
- Sweep / Signal Scan return the same fields condensed per target — contacts first, sorted HOT → COLD, each carrying its own links.
Deliver as a document, not in chat
Each run is a Markdown file in the pack's runs/: {mode}-{scope}-{YYYY-MM-DD}.md. In chat, reply with only a short pointer — file name, target count, top 1–2. The document is the deliverable.
The ledger (leads.csv — memory across runs)
See ../engine/ledger-contract.md for the full contract. In short: read it before every run (skip handled targets, resurface stalled ones, re-rate on new signals); enrich it after (append new targets, merge contacts, never drop what a prior run found). The human owns exactly one column — the disposition flag — and you never write it.
Exclusions (silent hard filter)
Before surfacing anyone, drop any target matching a row in exclusions.csv. Silently — no reasoning spent, never mentioned.
The bar
Confirmed vs. Inferred vs. Unknown on every claim. Name the friction. End with gaps and where to fill them. If you're just summarizing inputs, you've missed the assignment.
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.

