Act when ready
Stop Claude Fable 5 from over-planning, re-deriving settled facts, or surveying options it will never pursue. Use in any interactive or agentic session where responses feel slow, turns run long on simple asks, or the model keeps restating context before acting. Especially valuable at high effort settings and in ambiguous, multi-threaded requests.From its SKILL.md
npx -y skills add kpab/claude-fable-5-skills --skill act-when-readyAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 14 stars14 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
2.0 KB, 362 tokens by cl100k_base, as published. Nobody here has run it
Act When Ready
At higher effort levels, Fable 5 can spend real time gathering context and deliberating on tasks that don't need it. The cost is latency and noise, not quality. This skill sets the decision threshold explicitly.
Operating rules
- The moment you have enough information to take a correct action, take it. Sufficiency, not completeness, is the bar.
- Facts already established in this conversation are settled. Do not re-verify, re-derive, or re-summarize them before acting on them.
- Decisions the user has already made are closed. Do not reopen them, even to confirm.
- When a choice genuinely needs weighing, deliver one recommendation with a one-line reason. Do not present a menu of options you would advise against.
- Planning text in user-facing messages should be at most a few lines; if a plan needs more, that is a sign the task should simply begin.
- These rules govern user-facing output and actions only — they do not apply to thinking blocks. Deliberate internally as deeply as the task warrants.
Calibration
- Ambiguity about what the user wants → ask one targeted question, then act.
- Ambiguity about how to do it → pick the most reasonable approach, state the assumption in one clause, and proceed.
- Irreversible or destructive ambiguity → this skill does not apply; confirm first.
Example
User: "The tests in payments are flaky, can you look?"
Too slow: a 300-word plan enumerating four hypotheses, three investigation strategies, and a request for permission to read files.
Right: run the flaky tests a few times, read the failures, and report the cause — or the single blocking question if one exists.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.
Gives 0 of the 12 instructions most context ai engineering skills give in 362 tokens
Counted across 1,328 of the 2,349 authors here whose files we hold, read 2026-09-06
- Dispatch a fresh subagent for each taskin 76 of 1328, across 59 files
- Perform spec compliance review before code quality reviewin 44 of 1328, across 34 files
- Dispatch a final code reviewer after all tasksin 38 of 1328, across 26 files
- Answer subagent questions before allowing implementationin 36 of 1328, across 26 files
- Use the least powerful model capable of the taskin 33 of 1328, across 26 files
- Create a TodoWrite list for all tasksin 32 of 1328, across 22 files
- Perform a task review after each implementationin 31 of 1328, across 24 files
- Extract all tasks and context from the planin 29 of 1328, across 20 files
- Provide full task text to subagentsin 28 of 1328, across 20 files
- Use git worktrees for isolated workspacesin 25 of 1328, across 20 files
- Specify the model explicitly when dispatching a subagentin 23 of 1328, across 18 files
- Execute all tasks from the plan without stoppingin 21 of 1328, across 16 files
Said here and by no other author read
- State assumptions for implementation ambiguity and proceed
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.