assumption
Skill0 starsChanged 7 months agoMITView on GitHub
Make non-trivial agent assumptions explicit during execution. Critical for data analysis where implicit assumptions shape conclusions.
In this repository4 other files it gives its agents.
- briefSkill
- decision-markerSkill
- plan-firstSkill
- thoughtSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 18
When you discover or make a non-trivial assumption mid-task (most common after seeing data), output a standalone block:
≈ “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
Assumption Marker (F5)
Cockpit's fifth principle: make implicit assumptions explicit when they meaningfully affect output. Solves spec.md §2 FM2 specifically for data analysis scenarios.
Behavior — dual-track
Track 1: Plan-time declaration
Include known assumptions in the <cockpit-plan> block's Assumptions: field at task start.
Track 2: Mid-execution declaration
When you discover or make a non-trivial assumption mid-task (most common after seeing data), output a standalone block:
<cockpit-assumption>
Assuming: [the specific assumption]
Why: [why making this assumption — what data or context drove it]
Impact: [what changes if the assumption is wrong]
</cockpit-assumption>Frequency: only non-trivial
Declare assumptions that meaningfully affect conclusions. Skip trivia like:
- "Assuming UTF-8 encoding"
- "Assuming no NaN values"
- "Assuming integer overflow won't happen"
DO declare:
- "Assuming user_segment defined as last 30 days orders bucketed (high/mid/low) — data has no explicit segment field"
- "Assuming missing payment_status means failed (not pending) — no docs available"
- "Assuming cohort grouped monthly not weekly — stronger signal at month level"
The litmus test: if this assumption is wrong, would the user's interpretation of the result change? If yes, declare. If no, skip.
Why this exists
Data analysis tasks are full of implicit choices that shape conclusions:
- Which slice dimension
- How to handle missing values
- What proxy to use when explicit field unavailable
- What grouping cadence
Without assumption markers, these are invisible — user sees the chart but doesn't know what assumption made it look that way. With markers, user can push back early ("wait, why did you bucket by 30 days? I want 90 days").
Especially important for
- Data analysis — assumptions in slicing / grouping / methodology
- Deep research — assumptions about source authority / recency
- Code modifications — assumptions about user intent / API contract
Non-blocking design
Like thought blocks, assumption markers are visible but don't pause execution. Agent declares, user reads in real-time, agent proceeds.
Implementation
Pure skill-level instruction. The blocks appear in Claude Code's output stream and persist in transcript for later audit. v0.2 may add specialized parsing/logging.
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.

