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fractal

Delegate analysis- and context-heavy work to Fractal, an agentic CLI powered by a self-harnessed Recursive Language Model (predict-rlm), by running it non-interactively (fractal -p). Reach for it when a task needs reasoning over a large or deep codebase, synthesizing an answer across many files, auditing, or open-ended investigation — work that would otherwise flood your own context. The RLM reasons over context programmatically (no context rot) and returns a distilled answer. Use when asked to run Fractal headless, script it, call it from CI or another agent, or offload a heavy analysis/large-context task. Less suited to trivial single-file edits you can do directly.

69

Quality

87%

Does it follow best practices?

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SecuritybySnyk

Critical

Do not install without reviewing

SKILL.md
Quality
Evals
Security

Quality

Content

78%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 highly actionable and information-dense, with a clean install→preflight→run structure and an excellent output/exit-code contract. Its main weaknesses are a broken main reference (RECIPES.md is cited twice but missing from the bundle), a duplicated stream-capture snippet, and the absence of an explicit failure-recovery loop.

Suggestions

Ship RECIPES.md alongside SKILL.md (or inline its four patterns) — the body twice defers to a file that is not present in the bundle, leaving the CI-gate, multi-turn-driver, diff-review, and structured-output recipes unreachable.

De-duplicate the stream-capture guidance: `out=$(fractal -p "..." 2>err.log)` appears in both Quick start and Output contract; state it once and cross-reference.

Add an explicit remediation loop after the output contract, e.g., exit 1 → check `fractal: failed:` on stderr and re-run preflight; exit 2 → `--resume <session_id>` or re-run with a higher `--max-iterations`.

DimensionReasoningScore

Conciseness

The body is dense with tool-specific facts Claude cannot know (exit codes, flag semantics, output contract) and avoids explaining known concepts, but the stream-capture snippet `out=$(fractal -p "..." 2>err.log)` is duplicated in both Quick start and Output contract, and the RLM rationale paragraph partly repeats the frontmatter. Efficient with minor trimming opportunities — not the lean 5 anchor.

4 / 5

Actionability

Fully executable throughout: a copy-paste install one-liner, preflight commands with non-interactive fallbacks (`fractal config set`, env vars), a precise quick-start invocation, an exit-code contract, and exact flag syntax including stdin piping and session-resume details.

5 / 5

Workflow Clarity

The sequence is clear and ordered (check/install → preflight Docker and config → run → interpret streams/exit codes) with checkpoints and error-interpretation guidance, but there is no explicit run→check-exit-code→remediate loop (e.g., exit 2 → resume or raise --max-iterations) tying the pieces together.

4 / 5

Progressive Disclosure

References are clearly signaled and one level deep ([RECIPES.md](RECIPES.md) with an anchor link), but the referenced file does not exist anywhere in the skill bundle, so the deferred content (CI gate, multi-turn driver, diff review, structured output) is unreachable — navigation breaks at the main reference.

3 / 5

Total

16

/

20

Passed

Description

92%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 concrete capabilities, gives explicit "use when" triggers with concrete scenarios, delimits the niche with a when-not clause, and names the tool so it cannot be confused with generic analysis skills. The only gap is modest synonym coverage in trigger terms.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — "Delegate analysis- and context-heavy work... by running it non-interactively (fractal -p)", "synthesizing an answer across many files, auditing, or open-ended investigation", "script it, call it from CI" — with comprehensive coverage of the tool's use cases.

5 / 5

Completeness

Explicitly answers both what ("Delegate... to Fractal... running it non-interactively (fractal -p)... returns a distilled answer") and when ("Reach for it when a task needs...", "Use when asked to run Fractal headless, script it, call it from CI or another agent, or offload a heavy analysis/large-context task"), with concrete trigger phrases and a when-not clause.

5 / 5

Trigger Term Quality

Good natural keyword coverage ("auditing", "open-ended investigation", "large or deep codebase", "headless", "CI", "large-context task") but a few natural synonyms users would say are missing (e.g., "analyze", "explore", "search", "summarize"). Not the 5 anchor, which requires comprehensive synonym coverage; clearly above the 3 anchor's partial coverage.

4 / 5

Distinctiveness Conflict Risk

Names a specific tool (Fractal / predict-rlm) with a clear niche — delegating context-heavy analysis to an external headless CLI — and distinct triggers (headless runs, CI, large-codebase offload) that are unlikely to fire for unrelated skills.

5 / 5

Total

19

/

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

relative_links

Relative link issues: 2 missing

Warning

Total

15

/

16

Passed

Repository

jasonkneen/tiny-world-builder

Path

.agents/skills/fractal/SKILL.md

Reviewed

Table of Contents

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