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cekura-generate-scenarios

Turn a set of flagged production call logs into evaluator scenarios for a Cekura agent — cluster the failures into reproducible modes and create one scenario per cluster. Use when the user says "create scenarios from failed calls", "build evaluators for prod failures", "turn call logs into scenarios", "simulate the failures we saw in prod", "replay these bad calls as tests", "regression-test the agent on prod issues", or hands over a set of flagged call IDs to harden the agent against. The flagged set normally comes from `cekura-flag-call-log-failures`; if the user hasn't triaged yet, run that skill first. This skill takes the flagged calls as given — it does NOT re-mine or re-triage — clusters by failure mode, drafts one scenario per cluster, and optionally creates them via the scenarios API (or the call-logs → scenarios endpoint). Also supports a single-call fast path: given one call log ID (or an observe URL), reproduce just that call as one evaluator scenario.

Quality

91%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

77%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.

Operationally excellent content: highly concrete MCP orchestration guidance, explicit validation gates and feedback loops for a batch create workflow, and rich domain-specific failure lore that Claude could not know. The weaknesses are redundancy — the write-path, end-call-tool, and metric-reuse rules are each restated several times — and the total absence of progressive disclosure, with ~475 lines of mixed policy, reference tables, and templates all inlined in one SKILL.md.

Suggestions

Consolidate the repeated governing rules: state the write-path split (conditional_actions → direct create, instruction → generate) and the always-on end-call `tool_ids` rule once in an authoritative section and cross-reference it, instead of restating each in the Write path table, Picking `scenario_type`, single-call step C, and every Step 6 subsection.

Move stable reference material — the conditional-action tag table, the Step 5 report template, the metric-selection policy, and the failure-mode construction map — into files under references/ and link to them, cutting the SKILL.md body to the decision workflow.

Fix the internal inconsistency in the Step 5 example payload: it shows "tool_ids": [] while the always-on rule (Step 4) requires the testing-agent end-call tool in every scenario's `tool_ids` — the worked example should model the rule.

DimensionReasoningScore

Conciseness

Most content is genuinely novel, non-obvious operational detail (camelCase dynamic-variable casing, `<endcall />` compilation semantics, ElevenLabs missing-variable hard-fails) with no explanation of concepts Claude already knows — but the same governing rules are repeated many times: the write-path split is stated in ~5 places ("Write path" table, "Picking `scenario_type`", Step 6 intro and both sub-paths, "A subset never changes the write path"), the always-on end-call `tool_ids` rule in ~4, and the metric-reuse policy appears twice in full (its own section and single-call step E). Anchor 3 ("mostly efficient but... could be tightened") fits; it is not 2 because nothing is padded filler, and not 4 because the rule repetition goes beyond minor over-explanation.

3 / 5

Actionability

Guidance is fully executable: exact MCP tool names with arguments, per-field payload tables for every write call, a concrete condition example ("{when: "The agent asks yet another open-ended hypothetical question (3rd+ time)", say: "Okay, I think that covers it — thanks. <endcall />"}"), a complete report template, and worked payload JSON. Per the code-vs-instruction note, absence of code in an orchestration skill is not penalized when guidance is this actionable, and the examples cover the common cases (both scenario types, the fast path, dedup, escalation).

5 / 5

Workflow Clarity

Steps 0–6 are clearly sequenced with the single-call fast path explicitly cross-referenced, and validation checkpoints are pervasive for a batch operation: "read-first: it never creates a scenario without an explicit user OK", "Do not proceed until the agent ID is confirmed", the STOP-and-surface rule for weak agent descriptions, "read the scenario back and PATCH if the phone is null", the post-generation audit for `test_profile == null`, and a 'When to escalate instead' section. Error-recovery feedback loops exist too (re-generate a thin cluster with sharper `extra_instructions`, fall back from the transcript endpoint on timeout).

5 / 5

Progressive Disclosure

The body has good internal structure (clear headers, tables, a bottom quick-reference) but is ~475 lines in a single file with no bundle files at all — no references/, scripts/, or assets/ exist, and reference-type material (the conditional-action tag syntax table, the full report template, the metric-selection policy, the failure-mode construction map) is entirely inlined. This matches anchor 3 ("content that should be separate is inline" with structure present); it is not 2 because the structure is strong and navigable, not minimal, and not 4 because nothing is actually split out — the under-50-lines exception clearly does not apply at this size.

3 / 5

Total

16

/

20

Passed

Description

100%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.

An exemplary description: concrete third-person action statements, an explicit 'Use when' clause with six verbatim user phrasings, comprehensive capability coverage including the single-call fast path, and an explicit boundary against the sibling triage skill. Its only weakness is length (~160 words with some restatement of the triage boundary), but every sentence carries trigger or capability information rather than padding.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions covering the whole workflow: "cluster the failures into reproducible modes and create one scenario per cluster", "drafts one scenario per cluster", "optionally creates them via the scenarios API (or the call-logs → scenarios endpoint)", and the single-call fast path "reproduce just that call as one evaluator scenario". Nothing is vague or generic, and the coverage is comprehensive for the skill's scope, matching the anchor-5 example's breadth. Not 4: there are no meaningful gaps in the action inventory.

5 / 5

Completeness

Both halves are explicit: the 'what' is the opening sentence (cluster flagged call-log failures into modes, one scenario per cluster, optional creation via API, plus a single-call fast path), and the 'when' is the verbatim "Use when the user says..." clause with concrete trigger phrases and a defined input handoff. This is a direct match for the anchor-5 example; the 'Use when' clause is present and explicit, so no cap applies.

5 / 5

Trigger Term Quality

It quotes six full natural user phrasings — "create scenarios from failed calls", "build evaluators for prod failures", "turn call logs into scenarios", "simulate the failures we saw in prod", "replay these bad calls as tests", "regression-test the agent on prod issues" — plus "hands over a set of flagged call IDs". This is comprehensive synonym-level coverage of how users would actually ask for this, exceeding the anchor-4 'good coverage, a few missing' level.

5 / 5

Distinctiveness Conflict Risk

It carves out a clear niche (flagged prod call logs → evaluator scenarios) and explicitly disambiguates against the closest sibling skill: "The flagged set normally comes from `cekura-flag-call-log-failures`; if the user hasn't triaged yet, run that skill first... it does NOT re-mine or re-triage". Triggers are specific to scenario-building rather than triage, so wrong-skill triggering risk is minimal.

5 / 5

Total

20

/

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-generate-scenarios/SKILL.md

Reviewed

Table of Contents

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