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self-evolving-single-agent

Use when generating a single installable agent that should keep learning, track sources, refresh research, propose repairs, or improve itself over time without becoming a multi-agent team.

Quality

76%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./.agents/skills/self-evolving-single-agent/SKILL.md

The canonical home for this skill is self-evolving-single-agent in agentlas-ai/Agentlas-OS

SKILL.md
Quality
Evals
Security

Quality

Content

71%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 lean, well-structured instruction body with an explicit human-approval checkpoint for self-modification. Its weaknesses are a broken reference to a nonexistent contract file that step 2 depends on, and several cryptic, unexplained directives (approval receipts, host capabilities, Curator verdicts) that leave the guidance only partially executable.

Suggestions

Fix the broken dependency: either add 'contracts/builder-interview-research-gate.md' to the bundle or inline its 8-12 question interview checklist, since step 2 currently points to a file that does not exist.

Define what a valid 'exact one-use owner approval receipt' looks like and how to issue or verify one, and either explain or drop the undefined jargon ('Curator verdict', 'legacy chip', 'revision-v1/evolution-v2 host capabilities').

Specify the expected contents of '.agentlas/capability-eval-plan.json' and what the 'verification' output must check, so the generation step can actually be validated.

DimensionReasoningScore

Conciseness

Every line is directive — numbered steps, exact file paths, and output keys — with zero padding and no explanation of concepts Claude already knows. This matches the 'lean and efficient; every token earns its place' anchor; there is no trimmable over-explanation that would place it at 4.

5 / 5

Actionability

Concrete guidance exists (explicit file lists like '.agentlas/memory-map.json' and the six 'docs/' files, plus 'ask an 8-12 question first batch'), but key operations are missing executable detail: 'research official sources, similar agent repositories or comparables, academic/professional theory' gives no method, 'Use revision-v1/evolution-v2 host capabilities; block activation on older hosts' explains no mechanism, and jargon like 'Curator verdict' and 'legacy chip' is undefined. This sits at 'some concrete guidance but incomplete' rather than the mostly-executable level of 4, but well above the high-level-hints-only level of 2.

3 / 5

Workflow Clarity

The 7-step numbered Procedure gives a clear sequence, and the key checkpoint is explicit: 'wait for human approval before changing tools, connectors, secrets, or core instructions', reinforced by a dedicated 'File revision approval' section. It falls short of 5 because the 'verification' output has no corresponding validation step or feedback loop in the workflow, but the explicit approval gate keeps it above the implicit-checkpoint level of 3.

4 / 5

Progressive Disclosure

The body is under 50 lines with well-organized sections (Procedure, Output, File revision approval), but step 2 instructs 'Run contracts/builder-interview-research-gate.md before generation' and no such file exists in the bundle — no references/, scripts/, assets/, or contracts/ files are present at all. That broken, load-bearing reference leaves navigation dead-ending, which is more than the 'minor organization gaps' of 4, while the good section structure keeps it above the 'minimal structure' of 2.

3 / 5

Total

15

/

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 well-formed description with an explicit 'Use when' trigger, good natural trigger phrases, and a distinctive single-vs-multi-agent boundary. Its main weakness is specificity: it describes properties of the generated agent rather than the concrete actions the skill itself performs, so the 'what' is thinner than the skill's actual scope.

Suggestions

Lead with the concrete actions the skill performs (e.g., 'Generates an installable agent package with memory architecture, research-refresh commands, and approval-gated self-evolution') instead of qualities of the target agent like 'keep learning' or 'improve itself'.

Name one or two key deliverables (memory files, global commands) so the 'what' reflects the skill's real scope rather than only 'generating a single installable agent'.

DimensionReasoningScore

Specificity

The only concrete skill action is 'generating a single installable agent'; the listed items ('keep learning, track sources, refresh research, propose repairs, or improve itself') are qualities of the artifact being generated, not actions the skill performs. It names the domain clearly with more than generic coverage, matching the 'names domain and 1-2 concrete actions' anchor rather than the 'lists several specific actions' anchor above.

3 / 5

Completeness

Both parts are explicit: the 'what' is 'generating a single installable agent' and the 'when' is the 'Use when generating...' clause with concrete trigger conditions. It is not a 5 because the 'what' undersells the skill's actual scope (memory architecture, approval-gated evolution, global commands), and not a 3 because the 'when' is explicit rather than weakly implied.

4 / 5

Trigger Term Quality

Natural trigger phrases are present ('keep learning', 'track sources', 'refresh research', 'propose repairs', 'improve itself', 'without becoming a multi-agent team'), giving good keyword coverage. It falls short of the 5 anchor because common synonyms like 'self-improving agent', 'self-evolving', or 'autonomous learning' are missing, but coverage exceeds the 'some relevant keywords' level of 3.

4 / 5

Distinctiveness Conflict Risk

The boundary 'a single installable agent... without becoming a multi-agent team' clearly distinguishes this from multi-agent orchestration skills, and the self-evolution focus carves a niche. Minor overlap risk remains with generic agent-generation skills, keeping it below the 'clear niche with minimal conflict' of 5 but above the 'could still overlap with similar skills' of 3.

4 / 5

Total

15

/

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

agentlas-ai/Agentlas-OS

Path

.agents/skills/self-evolving-single-agent/SKILL.md

Reviewed

Table of Contents

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