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autogpt-agents

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.

59

Quality

70%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./backend/cli/skills/llm-tools/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 largely actionable, with runnable commands and clear use-case guidance up front (including honest alternatives and a complexity warning). Its weaknesses are the absence of any validation checkpoints in the setup and execution workflows, and inlining of deployment/benchmark/troubleshooting detail that the bundle's reference files already exist to hold.

Suggestions

Add explicit verification steps to the quick-start and agent-creation workflows (e.g., after 'docker compose up -d --build', check 'docker compose ps' or hit http://localhost:8006/api before proceeding to the frontend), and link the 'Common issues' section as the recovery path inline.

Move the Deployment, Environment variables, Integrations/credentials, and Benchmarking sections into references/advanced-usage.md (they are already advanced-usage-shaped), leaving a one-line pointer each in SKILL.md.

Trim the 'Key features', 'Architecture overview', and 'Core concepts' sections to a few lines each, and make the custom-ability and MyLLMBlock examples fully runnable (define perform_search and complete the class body).

DimensionReasoningScore

Conciseness

The body is mostly lean (tables, short code blocks, command lists), but at ~400 lines it inlines several sections that duplicate what the 535- and 420-line reference files already cover (deployment, benchmarking, troubleshooting) plus padding Claude does not need ('Blocks are reusable functional components', 'Credentials are encrypted and stored securely', ASCII architecture diagrams). It fits anchor 3 — mostly efficient with some unnecessary explanation that could be tightened — rather than 4, because the 'Key features' list, 'Architecture overview', and 'Core concepts' sections together add a noticeable amount of low-value tokens; not 2 because there is no long prose explanation of things Claude already knows.

3 / 5

Actionability

Most guidance is executable: copy-paste git/docker/npm commands, concrete REST endpoints with payloads, ./run forge and benchmark invocations. Minor gaps remain: the custom-ability example calls an undefined perform_search(), MyLLMBlock shows '# ...' instead of a complete class, and the scheduled-execution snippet has no surrounding command showing where the JSON goes. This matches anchor 4 (mostly executable with minor gaps); not 5 because two illustrative code blocks are not copy-paste runnable, and not 3 because the majority of examples are complete and runnable.

4 / 5

Workflow Clarity

Sequences exist (install → configure → start services; setup → create → start agent), but no validation checkpoints: after 'docker compose up -d --build' there is no step verifying services are healthy before starting the frontend, and error recovery is deferred to a separate 'Common issues' section rather than wired into the flow. This matches anchor 3 (steps listed but checkpoints missing/implicit); the destructive/batch cap does not apply, and it is not 4 because no inline verify step appears anywhere in the main workflows.

3 / 5

Progressive Disclosure

Good structure: a References section clearly signals two one-level-deep files (references/advanced-usage.md, references/troubleshooting.md) with one-line descriptions, and both files exist. It is not 5 because substantial reference-shaped content (deployment config, environment variables, integrations, benchmarking) is inlined in SKILL.md instead of pushed into the existing bundle files; not 3 because references are well signaled and the split is already reasonable.

4 / 5

Total

14

/

20

Passed

Description

78%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 that clearly states both what the skill covers and when to use it, with natural trigger phrasing. Its main weakness is that the capability verbs remain generic (build/deploy/create) rather than naming concrete operations, and it omits the platform's own name as a trigger term.

DimensionReasoningScore

Specificity

The description names the domain ('Autonomous AI agent platform') and a couple of actions ('building and deploying continuous agents', 'creating visual workflow agents'), but the verbs are generic build/deploy/create rather than concrete operations like extract/fill/convert. It sits between anchor 3 (1-2 concrete actions) and anchor 4 (several specific actions) — closer to 3 because no action is truly concrete; not 2 because the domain and objects ('visual workflow agents', 'multi-step AI automation systems') are named specifically.

3 / 5

Completeness

Explicitly answers both questions: 'Autonomous AI agent platform for building and deploying continuous agents' (what) and 'Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems' (when, with concrete trigger phrases). This matches the anchor-5 example pattern almost exactly; not 4 because the 'when' clause is already fully explicit rather than only partially so.

5 / 5

Trigger Term Quality

Good natural keyword coverage: 'visual workflow agents', 'persistent autonomous agents', 'multi-step AI automation', 'continuous agents' — phrases a user would plausibly say. A few natural terms are missing: the product name 'AutoGPT', file/tool synonyms, and simpler variants like 'agent that runs on a schedule/webhook', so it falls short of the comprehensive anchor 5; clearly above anchor 3 because multiple natural phrasings are present, not just one keyword.

4 / 5

Distinctiveness Conflict Risk

The 'continuous/visual-workflow autonomous agent platform' niche is mostly distinct with clear triggers, but overlaps with closely related agent-framework skills (e.g., CrewAI/LangChain skills) where a user asking to 'build an AI agent' could match either. Minor overlap risk matches anchor 4; not 5 because 'AI agent' triggers are inherently crowded, and not 3 because the persistent/visual-workflow framing meaningfully narrows it.

4 / 5

Total

16

/

20

Passed

Validation

87%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

frontmatter_unknown_keys

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

Warning

Total

14

/

16

Passed

Repository
synthetic-sciences/openscience
Reviewed

Table of Contents

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