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santa-method

Multi-agent adversarial verification: two independent reviewers with the same rubric must both pass before output ships, with a fix-and-re-review convergence loop and human escalation cap. Use when gating publishing, production deploys, compliance or brand-sensitive content, or hallucination-prone claims before they ship.

Quality

82%

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SecuritybySnyk

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SKILL.md
Quality
Evals
Security

Quality

Content

70%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 delivers an unusually clear, well-validated workflow with genuinely reusable artifacts (the reviewer prompt and rubric tables), but it is a monolithic 300-line file that duplicates content between its diagram, phase details, and implementation patterns. Splitting reference material into bundle files and de-duplicating the pseudocode would lift the two weakest dimensions.

Suggestions

Move the domain-specific rubric extensions, metrics list, and cost analysis into a references/ file (e.g. references/rubric-extensions.md) and link them from a short overview section, converting the monolithic SKILL.md into a one-level-deep progressive disclosure structure.

De-duplicate the content repeated across the ASCII architecture diagram, Phase Details, and Implementation Patterns — keep the diagram as the map and let the phase sections carry only the operational detail (prompt template, verdict logic, loop rules) that the diagram omits.

Replace or explicitly justify the pseudocode blocks: either make santa_verdict/santa_batch runnable Python or annotate them as deliberately language-agnostic templates, since the rest of the guidance (REVIEWER_PROMPT) is copy-paste ready.

DimensionReasoningScore

Conciseness

The body is mostly efficient — the rubric criteria table, failure-modes table, and reviewer prompt carry real information — but the ASCII architecture diagram restates the Phase Details, the Phase 2 spawn pseudocode restates Pattern A, and the 'Integration with Other Skills' table references skills with no counterpart in this bundle. It matches anchor 3 (mostly efficient but includes some unnecessary explanation or could be tightened), not anchor 4, because the duplication is systematic rather than a couple of trimmable lines.

3 / 5

Actionability

The REVIEWER_PROMPT template is copy-paste ready and the verdict-gate logic, fix-loop instruction ('Fix ONLY the flagged issues'), and Pattern B inline steps are concrete and executable in practice. However the Python blocks (santa_verdict, santa_batch, the Agent(...) invocations) are pseudocode/sketches rather than executable code, and that flexibility is not explicitly justified — matching anchor 4 (mostly executable with minor gaps) rather than anchor 5.

4 / 5

Workflow Clarity

The four phases are explicitly sequenced with a hard validation checkpoint (the verdict gate: 'B passes AND C passes → NICE'), a fix-then-re-review feedback loop, a max-iteration cap with escalation, and fresh-reviewer rules to prevent anchoring. The batch path also validates (re-sample and re-verify after pattern fixes). This matches anchor 5 — clear sequence, explicit validation steps, and feedback loops — and the destructive/batch cap does not apply because validation is the skill's core content.

5 / 5

Progressive Disclosure

The 300-line single file has no bundle files at all (no references/, scripts/, or assets/), and domain-specific rubric extensions, metrics, cost analysis, and the integration tables are all inlined where a reference file split would fit. Section headers are clear, matching anchor 3 ('some structure but content that should be separate is inline'), not anchor 4 — well above unstructured (anchor 2) but with substantial reference-worthy material left inline in the overview.

3 / 5

Total

15

/

20

Passed

Description

88%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: it states a concrete multi-mechanism 'what' and an explicit 'when' with natural trigger phrases, in third person and without padding. The only weaknesses are a few missing synonyms in the trigger set and slight breadth that could overlap generic review/QA skills.

DimensionReasoningScore

Specificity

The description names multiple concrete mechanisms — 'two independent reviewers with the same rubric must both pass', 'fix-and-re-review convergence loop', 'human escalation cap' — giving comprehensive coverage of what the method does. It fits the anchor 5 example (multiple specific concrete actions) rather than anchor 4, since no meaningful capability of the skill is left unstated.

5 / 5

Completeness

It explicitly answers both questions: the 'what' is the dual-reviewer both-pass gate with convergence loop and escalation cap, and the 'when' is stated verbatim as 'Use when gating publishing, production deploys, compliance or brand-sensitive content, or hallucination-prone claims before they ship.' This matches anchor 5; it is not anchor 4 because the when-clause is explicit and uses concrete trigger phrases rather than being merely present.

5 / 5

Trigger Term Quality

Trigger terms like 'publishing', 'production deploys', 'compliance', 'brand-sensitive content', and 'hallucination-prone claims' are natural phrases a user would say, but common synonyms such as 'verify', 'quality gate', 'fact-check', or 'review before shipping' are absent. This sits between anchor 4 (good coverage, a few natural terms missing) and anchor 5 (comprehensive synonyms), closer to 4.

4 / 5

Distinctiveness Conflict Risk

The dual-independent-reviewer gating mechanism is a clear niche unlikely to trigger for unrelated skills, though phrases like 'verification' and 'before they ship' create minor overlap with generic review/verify skills. Anchor 4 (mostly distinct, minor overlap risk) fits better than anchor 5 because the when-triggers are broad enough to collide with single-reviewer QA skills.

4 / 5

Total

18

/

20

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.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

Total

15

/

16

Passed

Repository

affaan-m/ECC

Path

skills/santa-method/SKILL.md

Reviewed

Table of Contents

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