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intent-framed-agent

Frames coding-agent work sessions with explicit intent capture and drift monitoring. Use when a session transitions from planning/Q&A to implementation for coding tasks, refactors, feature builds, bug fixes, or other multi-step execution where scope drift is a risk.

68

Quality

83%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

Quality

Discovery

89%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

This is a well-structured description with a clear 'Use when' clause that provides explicit trigger conditions. Its main weakness is that the core capabilities (intent capture, drift monitoring) are described at a somewhat abstract level rather than listing concrete actions. The trigger terms are strong and cover multiple natural variations of when this skill should activate.

Suggestions

Add more concrete actions to improve specificity, e.g., 'Captures session goals in a structured format, checks progress against original intent, flags scope creep during implementation.'

DimensionReasoningScore

Specificity

The description names the domain (coding-agent work sessions) and some actions (intent capture, drift monitoring), but the concrete actions are somewhat abstract—'frames,' 'explicit intent capture,' and 'drift monitoring' are conceptual rather than listing specific concrete steps like 'creates a session plan, tracks scope changes, alerts on deviation.'

2 / 3

Completeness

Clearly answers both what ('frames coding-agent work sessions with explicit intent capture and drift monitoring') and when ('Use when a session transitions from planning/Q&A to implementation for coding tasks, refactors, feature builds, bug fixes, or other multi-step execution where scope drift is a risk'). The 'Use when' clause is explicit and detailed.

3 / 3

Trigger Term Quality

Good coverage of natural terms users and Claude would encounter: 'coding tasks,' 'refactors,' 'feature builds,' 'bug fixes,' 'multi-step execution,' 'scope drift,' 'implementation,' 'planning.' These are terms that naturally arise when transitioning from discussion to coding work.

3 / 3

Distinctiveness Conflict Risk

This skill occupies a clear niche—session framing and drift monitoring for coding work sessions. It's distinct from general coding skills, project management skills, or planning skills because it specifically targets the transition point and scope management during implementation.

3 / 3

Total

11

/

12

Passed

Implementation

77%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This is a well-designed process skill with highly actionable, clearly sequenced workflows and concrete output templates. Its main weakness is verbosity: the Entire CLI integration, learning signal pipeline explanation, and detailed interoperability section (especially the context-surfing relationship) add significant token cost without proportional value for execution. The core intent capture/monitor/resolve workflow is excellent.

Suggestions

Move the 'How intent frames become learning signals' section and detailed Entire CLI integration into a separate reference file — Claude doesn't need to understand the downstream pipeline to emit the correct structured blocks.

Condense the 'Relationship with context-surfing' section to 3-4 lines covering the precedence rule and cadence separation; the conceptual explanation of complementary failure modes is unnecessary for Claude.

DimensionReasoningScore

Conciseness

The core workflow (Phases 1-3) is reasonably lean, but the skill includes substantial sections on Entire CLI integration, learning signal explanations, and detailed interoperability with other skills (especially the context-surfing relationship) that add significant token overhead. The explanation of how intent frames become learning signals is information Claude doesn't need to act on ('You do not need to do anything special for this').

2 / 3

Actionability

The skill provides concrete, copy-paste-ready markdown templates for Intent Frame, Intent Check, and Intent Resolution blocks with specific fields. Trigger conditions, drift examples, and confirmation prompts are all explicit and directly executable. The workflow is fully specified with exact output formats.

3 / 3

Workflow Clarity

The three-phase workflow (Capture → Monitor → Resolution) is clearly sequenced with explicit validation checkpoints: user confirmation before proceeding in Phase 1, structured drift checks at natural boundaries in Phase 2, and resolution records in Phase 3. Multi-intent session rules provide clear sequencing and error recovery (resolve before opening next, handle pivots vs. scope creep).

3 / 3

Progressive Disclosure

The content is well-structured with clear headers and logical sections, but it's monolithic — the Entire CLI integration details, learning signal explanations, and extensive interoperability documentation could be split into separate reference files. For a skill with no bundle files, there's a lot of inline content that would benefit from being externalized.

2 / 3

Total

10

/

12

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository
pskoett/pskoett-ai-skills
Reviewed

Table of Contents

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