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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" — and production-call bug fixing: "fix this prod call issue", "debug and fix call ID", "reproduce this production bug", "regression test before raising PR". Works on any stack: provider-dashboard agents (VAPI, Retell, ElevenLabs, Bland) AND agents whose config lives in the customer's own repo, database, or prompt registry with the provider agent created at deploy time, or with custom mock servers. Fixed safety invariants (must-fail-first reproduction, attestation, no-prod-in-loop) + a per-project capability manifest declaring where config lives and how to read/render/apply/deploy/verify it.

70

Quality

88%

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SKILL.md
Quality
Evals
Security

Quality

Content

73%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.

The body is exceptionally well-engineered process documentation — precise invariants, concrete tool calls, validation checkpoints, and stop conditions at every step — and it functions as the hub of a well-designed progressive-disclosure architecture. But that architecture is broken against the actual bundle: 11+ referenced paths (all phase and recipe files, plus the repro-gate hook) are missing while three real reference files are orphaned, so an agent following any phase pointer would hit a dead end.

Suggestions

progressive_disclosure: make the referenced paths match the bundle — either add the missing phases/*.md, recipes/*.md, and hooks/repro-gate.sh files, or repoint the phase table and Recipes section at the files that actually exist (references/phase-2-failure-collection.md, references/phase-3-diagnosis.md, references/dynamic-variables-debugging.md).

progressive_disclosure: link every file in references/ from the SKILL.md body (e.g., add dynamic-variables-debugging.md and the phase-2/phase-3 files to the Reference Files section) so no bundle content is orphaned.

conciseness: state the deterministic/stochastic reproduction run-count rules in one canonical place (invariant 1 or Parameters) and cross-reference from the other instead of repeating the sizing formula and thresholds twice.

DimensionReasoningScore

Conciseness

The body is dense and high-signal (exact artifact fields, formulas, naming formats), but the reproduction-mode sizing rules are stated twice — invariant 1 ('deterministic → exactly 1 run, must fail 1/1... stochastic → N = clamp(⌈2/p̂⌉, 4, 10)') and again nearly verbatim in Parameters ('deterministic → 1 must-fail run... stochastic → N = clamp(⌈2/p̂⌉, 4, 10)'), and invariant 1's single paragraph packs the gate, override JSON, and hook backstop into one hard-to-parse block. This is more than minor trimmable over-explanation, so it sits at 3 rather than 4; it is not 2 because nearly all content is operational rather than padded or teaching known concepts.

3 / 5

Actionability

The body gives copy-paste-ready directives for an instruction-only skill: the exact startup call 'mcp__cekura__cekura_skill_started with skill_name="cekura-self-improving-agent", verification_tag="ack:..."', the exact repro.json field list '{session_id, signal, mode, scenario_ids, result_id, n_runs, fails, injections, config_hash, timestamp}', the batch-name format '[selfimprove:<session_id>] <phase> — <detail>' with worked examples, the gate-override JSON with all required fields, and concrete numeric defaults and thresholds. Specific examples cover the common cases, matching the top anchor.

5 / 5

Workflow Clarity

A 7-phase table with per-phase purpose, a marked loop re-entry point ('Loop re-entry point' in Collect), sequencing rules ('Announce every phase entry as Iteration N · <Phase>... Never parallelize across a phase boundary'), and explicit validation checkpoints at every step (must-fail-first gate, dry-run validate, readback attestation, ≥ ⌈0.8·N⌉ verify threshold, overfitting gate, regression sweep, 'revert on any'). The Drift and failure classification section adds explicit stop conditions and error-recovery feedback loops, fully matching the top anchor.

5 / 5

Progressive Disclosure

Scored against the actual bundle: the body's core pointers — phases/setup.md through phases/promote.md (7 files), recipes/provider-managed.md, recipes/runtime-created.md, recipes/custom-mocks.md, hooks/repro-gate.sh, and providers/<mode>/ — do not exist in the bundle; only references/manifest.schema.json and references/manifest-guide.md resolve. Meanwhile the real bundle files references/phase-2-failure-collection.md, references/phase-3-diagnosis.md, and references/dynamic-variables-debugging.md are never referenced from the body, leaving existing content unnavigable. It is not 3 because the problem is not organization but a navigation layer that mostly dangles; it is not 1 because genuine structure and two valid one-level-deep references exist.

2 / 5

Total

15

/

20

Passed

Description

96%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 dense, trigger-rich description that explicitly states what the skill does and when to use it, with natural trigger phrases, named stack coverage, and concrete invariants rather than buzzwords. Its only weakness is minor conflict risk from generic debugging phrases that overlap non-agent skills, and its length borders on padded in the stack-enumeration sentence.

DimensionReasoningScore

Specificity

Multiple specific concrete actions span the whole loop: 'turn a failure signal into a verified fix', 'must-fail-first reproduction, attestation, no-prod-in-loop', and the manifest 'declaring where config lives and how to read/render/apply/deploy/verify it' — reproduce, fix, verify, attest, and promote are all named. Not 4: coverage is comprehensive across the workflow rather than several actions with minor gaps.

5 / 5

Completeness

Both questions are explicitly answered: the 'what' ('close the loop on agent quality — turn a failure signal into a verified fix', with invariants and the capability manifest) and the 'when' via a dedicated 'Triggers:' section with concrete trigger phrases. This matches the top anchor exactly; a 'Use when...' clause is present in all but name.

5 / 5

Trigger Term Quality

Natural user phrases are comprehensive and include synonyms: 'improve my agent', 'self-improving agent', 'auto-tune / iterate on my prompt', 'fix my agent from test results', 'fix this prod call issue', 'debug and fix call ID', 'reproduce this production bug', 'regression test before raising PR'. These are things a user would plausibly say verbatim; not 4 because synonym and variation coverage leaves little missing.

5 / 5

Distinctiveness Conflict Risk

The niche is clear — closing the agent-quality loop with named providers ('VAPI, Retell, ElevenLabs, Bland') and Cekura-specific invariants — but broadly applicable phrases like 'reproduce this production bug' and 'regression test before raising PR' could also match general debugging or regression-testing skills, creating minor overlap risk. Not 5 because of that overlap with generic bug-fixing skills; not 3 because the core triggers ('improve my agent', 'fix my agent from test results') are strongly distinctive.

4 / 5

Total

19

/

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

cekura-ai/cekura-skills

Path

cekura/skills/cekura-self-improving-agent/SKILL.md

Reviewed

Table of Contents

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