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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

Content

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-structured process skill with highly actionable templates and a clear three-phase workflow with proper validation gates. Its main weakness is verbosity in the supporting sections — the Entire CLI integration, learning signal pipeline explanation, and detailed interoperability discussion with context-surfing add significant token cost without proportional value for execution. The core workflow is excellent but is diluted by contextual information that could be externalized.

Suggestions

Move the 'How intent frames become learning signals' section and the detailed 'Relationship with context-surfing' subsection into separate reference files to reduce inline token cost.

Trim the Entire CLI integration section to just the detection command and the two behavioral branches (available vs. unavailable), removing the explanation of what learning-aggregator does with the data.

DimensionReasoningScore

Conciseness

The core workflow sections are reasonably lean, but the skill includes substantial sections on Entire CLI integration, learning signal explanations, and detailed interoperability descriptions (especially the context-surfing relationship) that add significant token overhead. The explanation of how intent frames become learning signals tells Claude things it doesn't need to know to execute the skill.

2 / 3

Actionability

The skill provides concrete, copy-paste-ready markdown templates for Intent Frame, Intent Check, and Intent Resolution blocks with exact field names and formats. Trigger conditions, drift examples, and confirmation prompts are all specific and directly executable.

3 / 3

Workflow Clarity

The three-phase workflow (Capture → Monitor → Resolution) is clearly sequenced with explicit validation checkpoints — notably the mandatory user confirmation before proceeding ('Do not proceed until the user confirms or adjusts') and the drift detection feedback loop (detect → ask → pivot or return). Multi-intent session rules add clear sequencing for complex scenarios.

3 / 3

Progressive Disclosure

The content is a single monolithic file with no bundle files or external references for detailed content. The Entire CLI integration details, the full interoperability section with context-surfing, and the learning signals explanation could be split into separate reference files. The skill is well-organized with clear headers but is longer than it needs to be inline.

2 / 3

Total

10

/

12

Passed

Description

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 covers multiple trigger scenarios. Its main weakness is that the core capabilities ('intent capture', 'drift monitoring', 'frames') are somewhat abstract and could benefit from more concrete action verbs. Overall it performs well on completeness and distinctiveness.

Suggestions

Replace abstract terms like 'frames' and 'intent capture' with more concrete actions, e.g., 'Records the stated goal and acceptance criteria at session start, then monitors for scope drift during multi-step coding execution.'

DimensionReasoningScore

Specificity

Names the domain (coding-agent work sessions) and some actions (intent capture, drift monitoring), but the actions are somewhat abstract rather than concrete. 'Frames' and 'explicit intent capture' are conceptual rather than specific operations like 'creates a session plan' or 'logs scope boundaries'.

2 / 3

Completeness

Clearly answers both what ('frames coding-agent work sessions with explicit intent capture and drift monitoring') and when ('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') with an explicit 'Use when' clause.

3 / 3

Trigger Term Quality

Good coverage of natural terms users would encounter: 'coding tasks', 'refactors', 'feature builds', 'bug fixes', 'multi-step execution', 'scope drift', 'planning', 'implementation'. These are terms a user or Claude would naturally associate with this kind of workflow management.

3 / 3

Distinctiveness Conflict Risk

Highly distinctive niche — session framing, intent capture, and drift monitoring for coding agents is a very specific workflow concern. The triggers around session transitions and scope drift are unlikely to conflict with standard coding, debugging, or project management skills.

3 / 3

Total

11

/

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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