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learning-aggregator-ci

[Beta] CI-only learning aggregation workflow using gh-aw (GitHub Agentic Workflows). Scans .learnings/ files on a schedule, groups entries by pattern_key, identifies promotion-ready patterns, and posts a gap report as a PR or issue comment. Use when: you want automated cross-session pattern detection in CI/headless pipelines without interactive prompts. For interactive use, use learning-aggregator.

Quality

85%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

The canonical home for this skill is learning-aggregator-ci in pskoett/pskoett-ai-skills

SKILL.md
Quality
Evals
Security

Quality

Content

75%Weight 40%Scale 1-5

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

A highly actionable, well-structured CI skill definition with concrete commands, an exact promotion threshold, and a copy-paste workflow template. Its main weaknesses are token efficiency — the workflow rules and cache/provenance details duplicate references/workflow-example.md — and inlining the full output-schema YAML instead of moving it to a reference.

Suggestions

Deduplicate the 12 'Workflow Rules' against the 18 numbered steps in references/workflow-example.md — keep a short summary of the grouping, deduplication, and ranking rules in the body and point to the reference for the full sequence.

Move the full ~48-line Output Schema YAML (and the duplicated trigger-configuration YAML) into a reference file such as references/output-schema.md, keeping only the top-level keys and one promotion-ready example inline.

Add an explicit error-recovery checkpoint for the authoring workflow, e.g. what to do when `gh aw compile` fails or how to verify the gap-report comment posted correctly before completing the run.

DimensionReasoningScore

Conciseness

The body mostly adds non-obvious domain knowledge (metadata fields, promotion thresholds, gh-aw specifics) rather than explaining known concepts, but the 12 'Workflow Rules' substantially duplicate the 18 steps in references/workflow-example.md (e.g. 'Legacy unknown-lineage evidence counts as at most one distinct task' appears nearly verbatim in both), and the provenance-v1 cache paragraph repeats the reference's cache steps. This fits the 'mostly efficient but could be tightened' anchor rather than level 4, where over-explanation would be only minor.

3 / 5

Actionability

Provides copy-paste-ready commands ("gh skill install pskoett/pskoett-skills learning-aggregator-ci", "gh aw compile") and a complete, executable workflow template in references/workflow-example.md, plus a concrete promotion threshold (">= 3 deduplicated recurrences, seen in >= 2 distinct tasks... within a 30-day window") and a fully specified YAML output schema with worked examples, matching the 'fully executable' top anchor.

5 / 5

Workflow Clarity

The authoring workflow is clearly sequenced with an explicit validation checkpoint ("Validate: `gh aw compile` (optionally add `--actionlint --zizmor`)"), and the CI rules are ordered 1-12 with a determinism constraint; however there is no error-recovery feedback loop (nothing on what to do when compile fails or how to verify the posted comment), so it matches 'clear sequence with most checkpoints present' rather than the level-5 anchor requiring feedback loops.

4 / 5

Progressive Disclosure

The single bundle file references/workflow-example.md is real, one level deep, and clearly signaled ("Copy `references/workflow-example.md` into `.github/workflows/learning-aggregator-ci.md`"). However, the ~48-line Output Schema YAML and the trigger-configuration YAML are inlined in the body when they arguably belong in references, and the body/reference duplication shows content that should be split is inline, fitting 'good structure; minor organization gaps' rather than the cleanly split level-5 anchor.

4 / 5

Total

16

/

20

Passed

Description

87%Weight 40%Scale 1-5

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

A strong description that explicitly covers what the skill does, when to use it, and how it differs from its interactive sibling. Its only flaws are a second-person 'Use when: you want...' construction and keyword coverage that omits a few natural synonyms like GitHub Actions or cron.

DimensionReasoningScore

Specificity

Lists four concrete actions ("Scans .learnings/ files on a schedule, groups entries by pattern_key, identifies promotion-ready patterns, and posts a gap report as a PR or issue comment"), which matches the level-5 anchor; however the second-person phrasing "Use when: you want automated cross-session pattern detection" incurs the rubric's third-person voice penalty, reducing this to 4.

4 / 5

Completeness

Clearly answers both what ("Scans .learnings/ files... groups entries by pattern_key... posts a gap report") and when ("Use when: you want automated cross-session pattern detection in CI/headless pipelines without interactive prompts") with a concrete trigger clause, matching the top anchor rather than the level-4 anchor where 'when' is only adequate.

5 / 5

Trigger Term Quality

Good keyword coverage including "CI", "headless pipelines", "on a schedule", "cross-session pattern detection", and "interactive prompts", but common variations users might naturally say such as "GitHub Actions", "cron", or "learning review" are missing, fitting the 'good coverage; a few natural terms missing' anchor rather than the comprehensive one.

4 / 5

Distinctiveness Conflict Risk

Clear niche ("CI-only learning aggregation workflow") with explicit disambiguation against the sibling skill ("For interactive use, use learning-aggregator"), giving minimal conflict risk and matching the 'clear niche with distinct triggers' anchor.

5 / 5

Total

18

/

20

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

pskoett/pskoett-ai-skills

Path

agent-plugin/skills/learning-aggregator-ci/SKILL.md

Reviewed

Table of Contents

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