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fabric-heal

Self-healing skill that improves signal mapper keyword coverage and capability coverage (ADR-0002) through iterative problem generation and keyword/capability patching. Use when user says "heal signal mapper", "improve keyword coverage", "improve capability coverage", "generate problem statements", "run healing loop", "patch signal mapper", or asks about "signal mapper gaps" or "capability gaps". Do NOT use for project architecture or deployment.

60

Quality

70%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

Fix and improve this skill with Tessl

tessl review fix ./.github/skills/fabric-heal/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

50%

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

The skill is reasonably structured with clear mode separation and specific tool references, but falls short on actionability by lacking executable command examples for the CLI tools it references. The self-healing workflow critically lacks validation checkpoints after keyword patching, and the orchestrator reporting section reads more like documentation of existing behavior than instructions for Claude to follow.

Suggestions

Add executable command examples for signal-categories-cli.py (e.g., `python signal-categories-cli.py add --category 3 --keyword 'data-pipeline'`) and signal-mapper.py invocations to make Mode 2 fully actionable.

Add an explicit validation step after keyword patching in Mode 2, such as re-running the benchmark to verify coverage improved and a feedback loop if it didn't.

Clarify the gap analysis mapping step with a concrete example showing how an uncovered term maps to a specific signal category and what the resulting CLI command looks like.

DimensionReasoningScore

Conciseness

Mostly efficient and avoids explaining concepts Claude already knows. However, the orchestrator reporting section includes details about ASCII bar charts and analytics persistence that feel like documentation of existing behavior rather than actionable instructions for Claude, adding some unnecessary bulk.

2 / 3

Actionability

Provides concrete output format for problem generation and names specific CLI tools and scripts, but lacks executable command examples (e.g., actual invocations of signal-categories-cli.py or signal-mapper.py). The gap analysis step ('Map uncovered terms to signal categories 1-11') is vague about how to actually perform the mapping.

2 / 3

Workflow Clarity

Two modes are clearly separated and Mode 2 has a numbered sequence, but there are no validation checkpoints or feedback loops. For a 'self-healing loop' involving iterative patching of a registry, the absence of a validate-after-patch step and error recovery guidance is a significant gap that caps this at 2.

2 / 3

Progressive Disclosure

References to external scripts and files are listed clearly at the bottom, and the content is reasonably structured with sections. However, the orchestrator reporting section contains inline detail that could be separated, and without bundle files it's hard to verify reference accuracy. The structure is adequate but not optimally organized.

2 / 3

Total

8

/

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 skill description with excellent trigger term coverage and completeness, including both positive triggers and explicit exclusions. The main weakness is that the capability descriptions use somewhat abstract/jargon-heavy language ('iterative problem generation', 'keyword/capability patching') that could be more concrete. Overall, it would perform well in a multi-skill selection scenario due to its highly distinctive domain and thorough trigger guidance.

DimensionReasoningScore

Specificity

The description names the domain ('signal mapper keyword coverage and capability coverage') and some actions ('iterative problem generation', 'keyword/capability patching'), but the actions are somewhat abstract and domain-specific jargon rather than concrete, universally understandable operations. It doesn't clearly list multiple distinct concrete actions.

2 / 3

Completeness

The description clearly answers both 'what' (improves signal mapper keyword/capability coverage through iterative problem generation and patching) and 'when' (explicit 'Use when' clause with multiple trigger phrases, plus a 'Do NOT use' exclusion clause). This is a well-structured complete description.

3 / 3

Trigger Term Quality

The description includes extensive natural trigger terms: 'heal signal mapper', 'improve keyword coverage', 'improve capability coverage', 'generate problem statements', 'run healing loop', 'patch signal mapper', 'signal mapper gaps', 'capability gaps'. These are specific phrases a user would naturally say when needing this skill.

3 / 3

Distinctiveness Conflict Risk

The description is highly specific to a particular internal tool ('signal mapper') with very distinct trigger terms and even includes explicit exclusions ('Do NOT use for project architecture or deployment'), making it very unlikely to conflict with other 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
microsoft/fabric-task-flows
Reviewed

Table of Contents

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