/efficient-fableSAFE
Skills for agents
Overview
From the repository's own README, as read at the audited commit. Badges and raw HTML are left out.
Use Claude Fable where it is worth paying for judgment.
/efficient-fable helps a Fable-class model orchestrate codebase-heavy work without spending premium tokens on every scan, log, browser run, and bounded edit. Fable keeps the hard calls: decomposition, architecture, product tradeoffs, synthesis, risk, and final review. Lighter agents do the repeatable heavy passes.
What It Does
- Splits large work into research, coding, and testing lanes.
- Tells Fable to choose the strategy, validation direction, and review bar.
- Encourages cheaper agents to scan repos, summarize docs, reduce logs, run
browser checks, and attempt bounded patches.
- Keeps evidence flowing back to Fable as files, commands, diffs, failures, and
uncertainties.
- Uses self-contained handoff packets so delegated agents can work without
hidden context from the orchestrator chat.
- Reminds Fable to verify important delegated claims before relying on them.
When To Use It
Use this when the task is too broad for one expensive model pass: unfamiliar repos, long test output, multi-file changes, product/architecture ambiguity, or validation work that can run in parallel.
Skip it for tiny fixes, highly coupled edits, or judgment-sensitive debugging where delegating would create more coordination cost than it saves.
Testing Guidance
Fable should suggest the validation direction and name the scripts or browser flows that matter. Lighter agents can run those checks, collect screenshots, reduce logs, and report whether failures look real, flaky, or environmental.
The point is not to outsource responsibility. Fable still decides what signal is trustworthy.
Delegation Quality
Good handoffs
3e67e99960c8OBSERVED · 2026-10-20What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
--- name: efficient-fable description: Use when running Claude Fable on codebase-heavy or token-heavy work and the user wants Fable to orchestrate research, coding, and testing while cheaper subagents do bounded heavy lifting. --- # Efficient Fable Use Claude Fable as the orchestrator, architect, synthesizer, and final judge. Use cheaper subagents for token-heavy research, coding, testing, and summarization that do not require Fable's full judgment. ## Where Fable Shines Reserve Fable for: - Decomposing ambiguous work into clean parallel slices. - Architecture, product, and safety tradeoffs. - Reading conflicting subagent reports and deciding what matters. - Integrating partial implementations into one coherent plan. - Final review, risk assessment, and user-facing synthesis. ## Delegation Pattern 1. Name the expensive-token risk: large repo search, long logs, broad docs, or repetitive edits. 2. Split independent work into subagents before reading everything yourself. 3. Use cheaper models for research scans, inventory, search summaries, narrow bug hunts, browser/testing passes, test output reduction, and bounded code edits. 4. Ask subagents for concise evidence: files, line references, commands run, diffs, uncertainties, and stop conditions they hit. 5. Spend Fable tokens on the decision layer: compare results, resolve conflicts, choose the implementation path, and review the final patch. Prefer parallel subagents when the slices do not depend on each other. Keep blocking or highly coupled work local. ## Handoff Packets Write delegated prompts as if the subagent has no useful chat context. Include only the context it needs: - The repo path and exact objective. - The files, packages, or surfaces in scope and anything explicitly out of scope. - The evidence format to return: files, line refs, commands, diffs, failures, screenshots, and uncertainty. - The verification commands or browser flows to run, plus what success should look like when that is knowable. - Stop conditions: if the code does not match the prompt, a command fails after a reasonable retry, or the task needs out-of-scope files, stop and report instead of improvising. ## Vetting Delegated Work Treat subagent reports as leads, not facts. Before using a high-impact finding, opening a PR, or telling the user the work is done, Fable should reopen the important cited files, confirm the relevant line refs or failures, and review the final diff against the task. Let lighter agents gather signal; keep truth-judgment with Fable. ## Common Scenarios Treat these as soft defaults, not rigid rules: - Research: ask lighter agents to scan docs, prior art, APIs, and repo surfaces; Fable decides what evidence changes the plan. - Coding: give cheaper agents bounded edits or candidate patches; Fable owns shared-file coordination, integration, and final review. - Testing: have Fable suggest the validation direction and the scripts or browser checks that matter. Let lighter agents run targeted tests, browser flows, screenshots, and log reduction, then report exact commands, failures, likely causes, and whether failures look flaky, environmental, or real. - Debugging: use cheaper agents to cluster logs, reproduce issues, and try small fixes; Fable decides which diagnosis is most trustworthy. If a task is tiny or the validation itself needs delicate judgment, keep it with Fable. ## Diagram Use `assets/fable-orchestrator.excalidraw` when a visual explanation helps. ## Claims For codebase-heavy work, it is reasonable to describe this as up to 3-5x more cost-efficient and 2-4x faster when independent research, coding, or testing slices can run in parallel. Treat those as workload-dependent estimates, not guarantees. Good launch copy: > Make Claude Fable more efficient by using cheaper subagents for token-heavy > research, coding, and testing, saving Fable for judgment, architecture, > synthesis, and final review.
Trust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (0)
No findings outside the package's declared scope.
Gates applied: no_behavioural_pass.
3e67e99960c8full audit observations/trust-audit/skill/builderio__efficient-fable.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-20 | 3e67e99960c8 | SAFE | B | 89 | first audit |
Questions
What does the efficient-fable — builderio/skills skill do?
Skills for agents
Is efficient-fable — builderio/skills safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can efficient-fable — builderio/skills access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (3e67e99960c8), read on 2026-10-20. The repository is watched, and a new audit runs when it changes — this is the first audit.
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