From praxis
Forces intent alignment before starting work on ambiguous requests. Generates multiple interpretations, identifies likely misreads, and mirrors the request back to the user.
How this skill is triggered — by the user, by Claude, or both
Slash command
/praxis:intent-alignmentThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
EXTREMELY_IMPORTANT: This is a MANDATORY protocol, not a suggestion. Follow every step.
EXTREMELY_IMPORTANT: This is a MANDATORY protocol, not a suggestion. Follow every step. Do not skip steps. Do not combine steps. Do not summarize. Work through each step in order.
This is the Converge half of PRAXIS: make sure you and the user mean the same thing BEFORE any classification, design, or code. Misreading the request is the cheapest failure to prevent and the most expensive to discover after delivery.
Answer both, explicitly:
Both YES → run STEPS 1–3. Either NO → state "intent-alignment: skipped, request is
[trivial | unambiguous]" and proceed to problem-classification. Do not answer NO to
question 2 without completing the 10-second version of STEP 1 first — "obviously
unambiguous" is a conclusion, not an observation.
Internally, generate at least 3 plausible interpretations of what "done" looks like for this request. For each, write one line: what would be delivered and to what scope. Vary them along real axes: breadth (one file vs the system), depth (quick fix vs redesign), target (the symptom named vs the cause behind it), audience (for the user vs for their users).
Then answer: would these interpretations produce meaningfully different work?
Assume you completed the work, handed it back, and the user said "that's not what I meant." Work backward: what is the single most likely misunderstanding that produced that reaction? (Same inversion technique as gap-analysis CHECK 1, applied to your reading of the request instead of a finished design.)
State it in one line: "Most likely misread: [X]." Then decide:
Restate the request to the user in your own words — not a paraphrase of their sentence, a reconstruction of their intent. Use exactly this format:
SPEC MIRROR
My understanding: [1–2 sentences, your words]
In scope: [what you will do, explicitly]
Out of scope: [what you will NOT do, explicitly]
Assuming: [everything you inferred that the user did not say]
Open question: [the STEP 1 divergence and/or STEP 2 misread, as concrete options
— or "none"]
Convergence confidence: [HIGH / MEDIUM / LOW per the using-praxis scale]
Present this to the user and wait for confirmation. Do not treat your own mirror as self-confirming — the point is that the USER validates it, not that you wrote it.
Do NOT proceed to problem-classification, design, planning, or code on an ambiguous request until ONE of these holds: - The user has confirmed the spec mirror (corrections incorporated), OR - No user is available to confirm — apply the using-praxis "Autonomous mode" section as written: state the interpretation you are choosing and its concrete risk in writing, cap confidence at MEDIUM, proceed, and surface the open questions at the end of your output. Do not redefine autonomous mode here; that section governs.Red flags that this skill catches — the exact thoughts that precede skipping it:
After the spec mirror is confirmed (or the autonomous-mode fallback is applied):
If Superpowers is installed → do NOT hand off from here. Proceed to
problem-classification first — it owns the handoff to superpowers:brainstorming.
Pass the confirmed spec mirror (scope, exclusions, assumptions, resolved questions)
forward as context so it arrives in brainstorming intact. PRAXIS converged; now
PRAXIS classifies.
If Superpowers is NOT installed → proceed to problem-classification with the
confirmed spec mirror as its input framing. You are the only reasoning layer available.
npx claudepluginhub xd4o/praxis --plugin praxisDecomposes underspecified requests by classifying missing info, ambiguity, and false premises, then generates targeted clarifying questions to produce a confirmed problem statement before implementation.
Classifies ambiguity types in user requests and generates clarifying questions before acting. Use when requirements are unclear, have multiple interpretations, or lack critical details.
Mirrors back the user's request as a structured confirmation covering what, why, scope, and constraints. Ensures mutual understanding before proceeding.