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prompt-injection

Hunt LLM prompt injection and tool-call hijacking in modern AI-integrated applications (CWE-1427). Covers indirect injection via RAG, tool abuse, exfiltration chains, and jailbreak-to-RCE pivots on agentic systems.

Quality

80%

Does it follow best practices?

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SecuritybySnyk

Critical

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Fix and improve this skill with Tessl

tessl review fix ./packages/decepticon/decepticon/skills/standard/analyst/prompt-injection/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

82%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 offensive-security playbook whose grep recipes, payload library, and validation contract give Claude everything it needs to execute. The only notable gaps are the absence of an explicit fix-and-retry loop in the audit workflow and the lack of any one-level-deep reference files for the payload and CVSS material.

Suggestions

Number the audit workflow end-to-end (inventory → grep sweep → tool-by-tool review → PoC → validate_finding) and add an explicit retry step for when a payload fails to trigger a success pattern.

Move the PoC payload library and/or the default CVSS table into a references/ file (e.g., references/payloads.md) and signal it from the body, keeping SKILL.md as the overview.

Trim the motivational opening paragraph and drop the '2026' framing so the content is not time-sensitive.

DimensionReasoningScore

Conciseness

The body is a lean, dense playbook that assumes Claude's competence — no basic-concept explanations, just target inventory, vectors, grep recipes, payload tables, and CVSS vectors, matching the 'efficient; minor instances of over-explanation' anchor. The motivational intro ('Every product shipping an LLM interface in 2026 has this surface. The bug bounty payouts are high...') and the time-sensitive '2026' date reference are the minor trimming candidates that keep it below anchor 5.

4 / 5

Actionability

Fully executable throughout: concrete grep -rE commands for finding LLM call sites and tool definitions, five copy-paste-ready PoC payloads covering distinct vectors (HTML-comment indirect exfil, RAG poisoning, tool-call hijack, markdown image exfil, cross-plugin forgery), explicit success/negative patterns for validate_finding, and exact CVSS vectors per variant. This matches the 'fully executable; copy-paste ready' anchor.

5 / 5

Workflow Clarity

The sectioned flow (inventory → vectors → audit → exploitation goals ranked by impact → PoC → validate_finding contract → CVSS → chains) is a coherent sequence with a concrete per-tool checklist ('Does the tool perform filesystem/network/shell/DB operations?' etc.) and a validation checkpoint via the negative-command/negative-pattern contract. It sits below anchor 5 because the audit workflow is presented as sections with embedded questions rather than a numbered end-to-end procedure, and there is no explicit fix-and-retry error-recovery loop.

4 / 5

Progressive Disclosure

No bundle files exist and the body references none, so there are no broken or nested references; the ~130 lines are organized into 8 clearly labeled, well-scoped sections, matching the 'good structure; most content is appropriately placed' anchor. It does not reach anchor 5 because the PoC payload library and CVSS table are reference-grade material that could be split into a references/ file at the top level, and the skill is somewhat over the under-50-lines threshold where inline-only organization would merit a 5.

4 / 5

Total

17

/

20

Passed

Description

70%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 specific, domain-distinct description with strong natural trigger terms that is held back only by the absence of an explicit 'Use when...' clause in the description field itself. Moving the when-to-use guidance from metadata into the description would raise completeness to the top band.

Suggestions

Append a 'Use when...' sentence to the description, e.g., 'Use when auditing AI-integrated applications, agentic frameworks, chatbots with tool access, or RAG pipelines for prompt injection.'

Fold the strongest natural keywords currently stranded in metadata.when_to_use (e.g., 'OWASP LLM01', 'AI agent', 'chatbot') into the description body so trigger matching does not depend on a metadata field.

DimensionReasoningScore

Specificity

The description names a concrete action ("Hunt LLM prompt injection and tool-call hijacking") and several specific coverage areas ("indirect injection via RAG, tool abuse, exfiltration chains, and jailbreak-to-RCE pivots") plus a concrete CWE scope, matching the 'lists several specific actions; minor gaps' anchor. It falls short of anchor 5 because verbs are limited to 'Hunt'/'Covers' and the items listed are attack classes rather than a comprehensive set of concrete actions performed.

4 / 5

Completeness

The 'what' is clear and specific, but the description contains no 'Use when...' clause or equivalent trigger guidance — when-to-use information lives only in the separate metadata.when_to_use field, not in the description being evaluated. Per the judging guideline, a missing 'Use when...' clause caps completeness at 3, matching the 'clear what but when is missing' anchor.

3 / 5

Trigger Term Quality

Phrases like "LLM prompt injection", "tool-call hijacking", "RAG", "exfiltration", "jailbreak", and "RCE" are exactly what a user would naturally say when needing this skill, giving good keyword coverage. A few natural terms (e.g., "AI agent", "chatbot", "OWASP LLM01") are missing from the description itself — they appear only in metadata.when_to_use — so it does not fully reach the comprehensive anchor 5.

4 / 5

Distinctiveness Conflict Risk

"Hunt LLM prompt injection and tool-call hijacking ... (CWE-1427)" carves out a clear niche with distinct triggers (RAG poisoning, jailbreak-to-RCE, tool-call hijacking) that are unlikely to collide with other skills' descriptions. It is written in third-person imperative voice with no first/second-person phrasing, so no specificity penalty applies.

5 / 5

Total

16

/

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

PurpleAILAB/Decepticon

Path

packages/decepticon/decepticon/skills/standard/analyst/prompt-injection/SKILL.md

Reviewed

Table of Contents

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