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he-reinforce

Create or refresh evidence-bound Harness Engineering learning artifacts from verified solved problems. Use when a fix worked, a repeated failure should become durable knowledge, or .harness/solutions and Project Brain need maintenance.

51

Quality

55%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./Plugins/harness-engineering/skills/he-reinforce/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

35%

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

This skill is heavily process-oriented and organizational, reading more like an internal framework specification than actionable guidance for Claude. It suffers from excessive jargon, abstract descriptions, and a proliferation of external references without providing concrete examples, templates, or executable code that would make the workflow immediately usable. The output format section lists many fields but never shows an actual example output.

Suggestions

Add a concrete, complete example of a learning artifact capture — show the actual structured output JSON with realistic field values so Claude knows exactly what to produce.

Reduce jargon and abstract organizational language; replace phrases like 'Compound learning only after proof' with direct instructions like 'Only create artifacts for problems with verified fixes.'

Provide a copy-paste-ready template for the primary artifact format (e.g., a .harness/solutions/ markdown file) with frontmatter and required sections filled in.

Consolidate the scattered 'See references/...' lines into a single References section at the bottom, with brief descriptions of when each reference is needed, rather than interrupting every section with folded-context pointers.

DimensionReasoningScore

Conciseness

The skill is extremely verbose with heavy jargon, internal system terminology, and repeated references to folded context files. Much of the content describes abstract organizational concepts rather than providing lean, actionable instructions. Phrases like 'Compound learning only after proof' and extensive lists of status fields add bulk without proportional value.

1 / 3

Actionability

The procedure section provides a numbered sequence and there is one concrete command (`python3 Plugins/harness-engineering/scripts/check_bluf_structure.py`), but most guidance is abstract and organizational rather than executable. There are no concrete code examples, template snippets, or copy-paste-ready artifact formats that would let Claude immediately act.

2 / 3

Workflow Clarity

The procedure has numbered steps and the validation section mentions gate statuses (pass/fail/blocked), but critical details are folded away into external references. The steps themselves are high-level mode selections and eligibility checks rather than concrete operations with explicit validation checkpoints and feedback loops for error recovery.

2 / 3

Progressive Disclosure

The skill makes extensive use of references to external files (hot-path-folded-context.md, contract.yaml, evals.yaml, etc.), which is good progressive disclosure in principle. However, without bundle files provided, the references cannot be verified, and the sheer number of 'See references/...' callouts scattered throughout every section makes navigation confusing rather than clear. The main body itself still contains too much inline detail mixed with the reference pointers.

2 / 3

Total

7

/

12

Passed

Description

75%

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

The description has strong completeness with explicit 'Use when' triggers and is highly distinctive due to its niche domain focus. However, the specific capabilities could be more concretely enumerated (e.g., what exactly does 'refresh' entail?), and some terminology like 'evidence-bound' and 'durable knowledge' is jargon that may not match natural user language.

Suggestions

List more concrete actions beyond 'create or refresh' — e.g., 'document root cause analyses, update solution indexes, link evidence to failure patterns'

Include more natural trigger terms users might say, such as 'save this fix', 'document this solution', 'update knowledge base', or 'log this workaround'

DimensionReasoningScore

Specificity

The description names a domain ('Harness Engineering learning artifacts') and some actions ('create or refresh'), but the concrete actions are not comprehensively listed. Terms like 'evidence-bound' and 'learning artifacts' are somewhat abstract rather than describing specific concrete operations.

2 / 3

Completeness

The description clearly answers both 'what' (create or refresh evidence-bound learning artifacts from verified solved problems) and 'when' (when a fix worked, when a repeated failure should become durable knowledge, or when .harness/solutions and Project Brain need maintenance) with explicit trigger conditions.

3 / 3

Trigger Term Quality

Includes some relevant keywords like '.harness/solutions', 'Project Brain', 'fix worked', 'repeated failure', and 'durable knowledge', but these are fairly domain-specific jargon. A user might naturally say 'the fix worked' or 'update solutions', but many trigger terms are specialized rather than naturally occurring phrases.

2 / 3

Distinctiveness Conflict Risk

The description is highly specific to a particular workflow involving Harness Engineering artifacts, .harness/solutions directory, and Project Brain. This niche focus makes it very unlikely to conflict with other skills.

3 / 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
jscraik/Agent-Skills
Reviewed

Table of Contents

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