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Sweep 22 Oct 2026 · 04:32Z Build v2.1.308 527 read Stable v2.1.308 Latest v2.1.308 Next v2.1.308 Feeds RSS JSON llms.txt llms-full.txt Unofficial
Prompt changes · v2.1.307 against v2.1.306

1 change since v2.1.306, landing in 20 of 23 prompts.

Every prompt v2.1.307 sent, compared word for word with the capture before it. Each card is one edit, with every prompt it reached; the table after them shows where each one landed.

What changed 1

1

Agent tool description · under “When to use”

Agent tool now warns of subagent costs and defaults to not spawning

The guidance now opens by listing what a fresh agent costs: lost detail in both handoffs, no visibility into its work, confident-sounding mistakes, a tendency to confirm a hypothesis it is handed, and token bursts the user pays for. Claude is told to do work itself when it takes a handful of tool calls or a known lookup, to delegate review only for an unanchored second read (giving the code, not its conclusion), and to not spawn when in doubt. Matching an available agent type is no longer listed as a reason to delegate, side quests that shouldn't block the main thread are added, and new instructions say to brief an agent with the goal, what is ruled out, which files to read, and a narrow scope.

+284 −41 words

Copy 1 of 2 · 20 prompts, 13 model strings · under “When to use”

Reach for this when the task matches an available agent type, when you have independent work to run in parallel, when the user asks for a side quest that shouldn't block your main thread, or when answering would mean reading across several files — delegate itthat and you keep the conclusion, not the file dumps. For a single-fact lookup where you already know the file, symbol, or value, search directly. Once you've delegated a search, don't also run it yourself — wait for the result.

A fresh agent costs more than it looks. It knows only what you put in the prompt, and you see only the summary it sends back — both handoffs drop detail, and neither of you can tell what the other missed. You can't watch it work, only wait or cancel. Its mistakes come back in the same confident register as its findings, and an agent handed your hypothesis tends to return it confirmed. Several at once spend tokens in a burst the user didn't ask for. Weigh those tokens against the accuracy they buy: the user pays for agents you did not need, and pays again for work you redo because you skipped one.

Do the work yourself when it is a handful of tool calls or a lookup whose target you already know; don't delegate a check you could run inline. Delegate review only when you want a read that isn't anchored on yours — then give it the code, not your conclusion. Once you've delegated something, don't also run it yourself; wait for the result. When in doubt, don't spawn.

When you do spawn one, brief it like the peer it is: state the goal and what you have already ruled out, point it at the files and docs worth reading instead of retyping them, and keep the scope explicit and narrow. That brief is the only context it will have, so it is your first lever on every cost above — and if you cannot write a clear one, you do not understand the task well enough to hand it off.

Copy 2 of 2 · 1 prompt, 2 model strings · under “When to use”

For a single-fact lookup whereDo the work yourself when it is a handful of tool calls or a lookup whose target you already know the file, symbol, or value, search directly; don't delegate a check you could run inline. Delegate review only when you want a read that isn't anchored on yours — then give it the code, not your conclusion. Once you've delegated a searchsomething, don't also run it yourself —; wait for the result. When in doubt, don't spawn.

A fresh agent costs more than it looks. It knows only what you put in the prompt, and you see only the summary it sends back — both handoffs drop detail, and neither of you can tell what the other missed. You can't watch it work, only wait or cancel. Its mistakes come back in the same confident register as its findings, and an agent handed your hypothesis tends to return it confirmed. Several at once spend tokens in a burst the user didn't ask for. Weigh those tokens against the accuracy they buy: the user pays for agents you did not need, and pays again for work you redo because you skipped one.

Reach for this when you have independent work to run in parallel, when the user asks for a side quest that shouldn't block your main thread, or when answering would mean reading across several files — delegate that and you keep the conclusion, not the file dumps.

When you do spawn one, brief it like the peer it is: state the goal and what you have already ruled out, point it at the files and docs worth reading instead of retyping them, and keep the scope explicit and narrow. That brief is the only context it will have, so it is your first lever on every cost above — and if you cannot write a clear one, you do not understand the task well enough to hand it off.

Where each change landed

Every prompt the release sent, one row each. A number is a change from the list above; click it to jump there. Rows with more than one model string count them, because each model gets its own copy of the prompt.

Prompt 1 Status
Main prompt
Claude Code, default27 models 13/27 1 change
Claude Code, seeded with this account's feature flags27 models 6/27 1 change
Claude Code, seeded with anonymous feature flags27 models 6/27 1 change
Agent SDK, default27 models 13/27 1 change
Agent SDK, seeded with this account's feature flags27 models 6/27 1 change
Agent SDK, seeded with anonymous feature flags27 models 6/27 1 change
Auto-mode classifier
Auto-mode classifier1 model · unchanged
Session agents
claude1 model ● 1 change
Slash commands
/init1 model ● 1 change
/security-review1 model ● 1 change
/team-onboarding1 model ● 1 change
Compaction
Compaction instruction1 model ● 1 change
System reminders
Claude md1 model ● 1 change
Hook pretool block1 model ● 1 change
Read dup1 model ● 1 change
Read empty1 model ● 1 change
Read offset1 model ● 1 change
Skill run1 model ● 1 change
Todo nag1 model ● 1 change
Toolsearch deferred1 model ● 1 change
Subagents
explore1 model · unchanged
general-purpose1 model ● 1 change
plan1 model · unchanged

Did not change

30 tool descriptions are word for word the same, 1 moved and is listed above.

AskUserQuestion Bash CronCreate CronDelete CronList DeferredToolPlaceholder Edit EnterPlanMode EnterWorktree ExitPlanMode ExitWorktree ListAgents Monitor NotebookEdit PushNotification Read ReportFindings ScheduleWakeup SendMessage Skill TaskCreate TaskGet TaskList TaskStop TaskUpdate ToolSearch WebFetch WebSearch Workflow Write

3 prompts did not move at all: Auto-mode classifier, Subagents: explore, Subagents: plan.

Set aside as capture noise

These change on every capture by construction, so they are masked before comparing and never counted as a change.

  • 358 billing header lines
  • 224 capture date lines
  • 224 host kernel lines
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