intent-clarification
Skill4 starsChanged 5 months agoApache-2.0View on GitHub
Extract explicit and latent user goals, resolve ambiguous requests, detect unstated business constraints, and map natural language intents to concrete engineering execution paths.
In this repository48 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
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The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 3 are distinctive, and 0 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.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 31
If a prompt contains ambiguous or high-entropy phrasing, immediately classify and address the gap:
- Distinctive: line 44
When clarifying intent with the user:
- Distinctive: line 46
Proactively Suggest Industry Defaults: Never present open-ended confusion.
≈ “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
User Intent Comprehension & Goal Alignment Skill
This skill guides agents to accurately decipher what the user actually needs versus what they literally asked for, preventing misalignment before code is written.
1. Intent Deconstruction Framework (The 4-Layer Lens)
Whenever a user provides a prompt or feature request, analyze it through four concentric layers:
[Layer 1: Literal Request] → "Add a login screen"
↓
[Layer 2: Underlying Objective] → User needs secure session management and user identification
↓
[Layer 3: Latent Requirements] → Password reset, OAuth/SSO, session invalidation, CSRF protection
↓
[Layer 4: Non-Functional Drivers]→ Enterprise compliance, low latency, zero-downtime migration2. Ambiguity Detection & Resolution Matrix
If a prompt contains ambiguous or high-entropy phrasing, immediately classify and address the gap:
| Ambiguity Pattern | Example User Phrasing | Resolution Action |
| Vague Scope | "Make it faster", "Make it look modern" | Quantify targets: Ask for specific p95 latency targets (e.g. <100ms) or UI design references. |
| Architectural Ambiguity | "Connect to external data" | Determine sync vs async (Webhook vs Polling vs Kafka) and error retry semantics. |
| Missing Failure Modes | "Send email when user signs up" | Clarify transactional reliability: Should signup fail if the email provider is down? |
| Implicit Authorization | "Admins can manage team members" | Ask if roles are static or configurable (RBAC vs ABAC). |
3. High-Fidelity Confirmation Protocol
When clarifying intent with the user:
- Replay Understandings with Options: Present the extracted mental model back in plain language with concrete trade-offs.
- Proactively Suggest Industry Defaults: Never present open-ended confusion; provide a solid (Recommended) option.
- Check for Unintended Side-Effects: State potential impacts on existing workflows (e.g., "Note: Enabling MFA will require updating the mobile login flow").
**Intent Confirmation Example:**
"I understand you want to add **team workspace sharing**.
- **Primary Goal**: Allow users to invite team members with specific roles (Admin/Member/Viewer).
- **Recommended Default**: We'll use RBAC with invite links expiring in 48 hours and tenant-isolated data queries.
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