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agents-autogpt

Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.

Quality

67%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./cli-tool/components/skills/ai-research/agents-autogpt/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

61%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 well-structured and highly actionable, with concrete commands, API examples, and tables covering the platform's main workflows. Its weaknesses are duplicated content between the inline Deployment/Common Issues sections and the reference files, and validation steps that live in 'Best practices' rather than inside the workflows that need them.

Suggestions

Move the Deployment (docker-compose.prod, env vars, encryption key) and Benchmarking sections into references/advanced-usage.md and the Common issues section into references/troubleshooting.md, keeping only brief pointers in SKILL.md to remove the duplication.

Embed validation checkpoints directly in the build/deploy workflows (e.g., after 'docker compose up -d --build', add a step to verify services with 'docker compose ps' before continuing) instead of listing testing only as a general best practice.

Complete or trim placeholder code, such as the credentials example ending in '# ...' and the custom_search ability calling the undefined perform_search(query), so examples are copy-paste ready.

DimensionReasoningScore

Conciseness

The content is dense and platform-specific rather than padded with concepts Claude already knows, but at ~400 lines it inlines Deployment, Benchmarking, and Common Issues sections that duplicate the stated scope of references/advanced-usage.md ('Custom blocks, deployment, scaling') and references/troubleshooting.md ('Common issues, debugging').

3 / 5

Actionability

Mostly executable guidance: concrete docker/CLI commands, complete HTTP request bodies, a WebSocket snippet, and env-var/provider tables. Minor gaps remain, such as the truncated credentials example ending in '# ...' and the undefined perform_search(query) placeholder.

4 / 5

Workflow Clarity

Sequences are listed (install -> access the platform; the 5-step visual build; forge setup) and the Common issues section gives recovery commands, but validation checkpoints are only implied via best practices ('Test incrementally', 'Version agents') rather than embedded in the workflows themselves.

3 / 5

Progressive Disclosure

Good section structure with clearly signaled one-level-deep references (references/advanced-usage.md, references/troubleshooting.md) that exist as real files with descriptive labels. The minor gap is that inline Deployment and Common Issues content overlaps the reference files' stated scope instead of being split out.

4 / 5

Total

14

/

20

Passed

Description

58%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.

The description has a clear 'what' and an explicit 'Use when...' clause, giving it solid completeness. However, it lacks the platform name and natural trigger variations, leans on buzzwords, and risks conflicting with the many similar agent-framework skills it itself lists as alternatives.

Suggestions

Include the platform name and natural synonyms in the description (e.g., 'AutoGPT', 'AutoGPT platform', 'agent builder') so users who name the tool trigger it directly.

Replace buzzword phrasing like 'complex multi-step AI automation systems' with concrete capabilities (e.g., 'build agents in a drag-and-drop visual editor, trigger them via webhooks or cron schedules').

Sharpen the 'when' clause toward distinct user situations (e.g., 'Use when the user mentions AutoGPT, wants persistent/event-driven agents, or asks for a no-code agent workflow builder') to differentiate it from LangChain/CrewAI-style requests.

DimensionReasoningScore

Specificity

The description names the domain and two concrete actions ("building and deploying continuous agents"), but "complex multi-step AI automation systems" is buzzword padding and it stops short of the several-specific-actions coverage of the 4-5 anchors.

3 / 5

Completeness

Both a clear 'what' ("platform for building and deploying continuous agents") and an explicit "Use when..." clause are present, but the when-clause largely restates the capability rather than naming distinct user situations, so it is not fully at the concrete-trigger-phrase level of a 5.

4 / 5

Trigger Term Quality

Relevant keywords are present ("autonomous agents", "workflow agents", "AI automation"), but the most natural trigger users would say — the platform name "AutoGPT" — is absent, along with common variations like "agent builder" or "webhook/scheduled triggers".

3 / 5

Distinctiveness Conflict Risk

"Autonomous AI agent platform" overlaps heavily with rival frameworks the skill itself names as alternatives (LangChain, CrewAI, OpenAI Assistants); without mentioning AutoGPT, requests to build agents could trigger this skill for the wrong framework.

3 / 5

Total

13

/

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

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository

davila7/claude-code-templates

Path

cli-tool/components/skills/ai-research/agents-autogpt/SKILL.md

Reviewed

Table of Contents

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