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cekura-self-improving-agent

Use to close the loop on agent quality — turn a failure signal into a verified fix. Triggers: "improve my agent", "self-improving agent", "auto-tune / iterate on my prompt", "fix my agent from test results", "optimize my prompt based on failures", "rewrite my prompt". ALSO for production-call bug fixing: "fix this prod call issue", "debug and fix call ID", "reproduce this production bug", "regression test before a PR", "fix the bug from this call and open a PR". Works across VAPI, ElevenLabs, and self-hosted agents, and across three fix surfaces — prompt, tool config, and (self-hosted) owned source code, including infra-flavored / forked-SDK bugs, which are reproduced and validated on Cekura (never a code test) and, for source edits, shipped as a PR.

74

Quality

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Advisory

Suggest reviewing before use

SKILL.md
Quality
Evals
Security

Quality

Content

85%

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

The body is an exceptionally well-structured orchestrator: sequenced phases, explicit validation gates, feedback loops, and clean progressive disclosure into per-phase and per-provider files. The main weakness is reinforcement redundancy across the loop, Invariants, and pause sections.

Suggestions

State the must-fail-first gate and the CodeBug-vs-Upstream rule once in their canonical location and reference them elsewhere rather than restating them in the loop, Invariants, and When-to-pause sections.

The "When to pause and ask" list is long; consider moving the exhaustive conditions into a referenced file and keeping only the highest-frequency pause triggers inline.

DimensionReasoningScore

Conciseness

Dense and free of concepts Claude already knows, but critical rules are reinforced redundantly — the must-fail-first gate and the "owned code is a CodeBug, not Upstream" rule each appear in the loop, Invariants, and When-to-pause sections — so it could be tightened.

2 / 3

Actionability

Provides concrete, specific guidance: exact field paths (conversation_config.agent.prompt.prompt), numeric thresholds (dataset_size 8, range 5–10, ≥ M of N gates), per-phase file links, and explicit stop conditions — fully actionable for an instruction skill.

3 / 3

Workflow Clarity

Ten phases run in a strict sequence with hard pre-conditions, explicit must-fail-first and must-pass validation gates, and named feedback loops (drift rolls back to Apply; regression hands back to Collect; Eval→Collect loop), matching the score-3 anchor.

3 / 3

Progressive Disclosure

The body is a concise overview with clearly signaled one-level-deep links into phases/ and providers/, a dedicated Files tree, and real references/ files — content is appropriately split and easy to navigate.

3 / 3

Total

11

/

12

Passed

Description

100%

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 is specific, trigger-rich, and clearly states both capability and use-conditions in third person. Its only weakness is verbosity — the trigger list is long — but every clause is concrete rather than fluff.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — "turn a failure signal into a verified fix", reproduce bugs, regression test, and ship a PR — across three named fix surfaces (prompt, tool config, owned source code), matching the score-3 anchor.

3 / 3

Completeness

Explicitly answers both what (close the loop / verified fix across providers and surfaces) and when via a labelled "Triggers:" clause plus "ALSO for production-call bug fixing:" guidance, satisfying the explicit-trigger requirement.

3 / 3

Trigger Term Quality

Strong coverage of natural phrasings users would actually say ("improve my agent", "fix my agent from test results", "debug and fix call ID", "regression test before a PR"), well beyond a single keyword.

3 / 3

Distinctiveness Conflict Risk

Scoped to a clear Cekura niche with provider and fix-surface qualifiers and distinctive triggers; unlikely to fire for unrelated skills despite nearby sibling cekura skills.

3 / 3

Total

12

/

12

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 15 missing, 6 deeper-than-1-level

Warning

Total

15

/

16

Passed

Repository
cekura-ai/cekura-skills
Reviewed

Table of Contents

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If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.