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Edit an agent's free-form planning markdown and adjust weekly tasks from a single entry point
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Basierend auf der SOC-Berufsklassifikation
| name | plan |
| description | Edit an agent's free-form planning markdown and adjust weekly tasks from a single entry point |
| trigger | aweek plan, adjust plan, edit plan, update plan, plan markdown, aweek add task, aweek add new task, aweek new task, aweek edit task, aweek update task, aweek change task, aweek remove task, aweek delete task, aweek drop task, aweek reschedule, aweek reschedule task, aweek reschedule everything, aweek move task, aweek change priority, aweek change prompt, aweek update prompt, aweek edit prompt, aweek tasks |
Single entry point for every planning operation on an aweek agent:
.aweek/agents/<slug>/plan.md.The planning markdown is the source of truth for long-term intent. It is intentionally free-form — the weekly generator reads it as context rather than enforcing a schema. If you find yourself wanting a structured field that plan.md can't express, add it as prose; the model will pick it up.
Plans are always active. There is no approval gate — every weekly plan is immediately eligible for the heartbeat the moment it lands on disk. Adjustments take effect on the next tick.
Weekly plan logic lives in src/skills/plan.ts, composed over the services in src/services/plan-adjustments.ts. Markdown logic lives in src/storage/plan-markdown-store.ts. Never write agent JSON or the plan file directly — always go through the skill module or the plan-markdown dispatcher.
Follow this exact workflow when invoked. Use AskUserQuestion for every interactive prompt.
List all agents:
echo '{"dataDir":".aweek/agents"}' \
| aweek exec agent-helpers listAllAgents --input-json -
Response is an array of { config } records. Render a numbered list using agent name and role, then ask the user to pick one via AskUserQuestion.
If the list is empty, tell the user "No agents found. Use /aweek:hire to create one first." and stop.
Ask via AskUserQuestion:
plan.md.Route to the matching branch. After each branch finishes, loop back to Step 2 until the user picks Done.
The markdown file is a free-form authoring surface. Its H2 conventions (Long-term goals / Monthly plans / Strategies / Notes) are a template, not a schema — the user can restructure freely.
echo '{"agentsDir":".aweek/agents","agentId":"<AGENT_ID>"}' \
| aweek exec plan-markdown path --input-json -
echo '{"agentsDir":".aweek/agents","agentId":"<AGENT_ID>"}' \
| aweek exec plan-markdown read --input-json -
path returns the absolute path; read returns the body (or null when the file is missing — in that case offer to seed it with plan-markdown buildInitial).
Show the user:
plan-markdown parse to extract titles + first 3 lines of each section).Ask via AskUserQuestion:
$EDITOR <path> as a suggestion for the user to run themselves. Do NOT spawn an editor from the skill.AskUserQuestion (multi-line input) and write it:
echo '{"agentsDir":".aweek/agents","agentId":"<AGENT_ID>","body":"<MARKDOWN>"}' \
| aweek exec plan-markdown write --input-json -
## <title>\n\n<body>\n, and write back.AskUserQuestion, then overwrite with plan-markdown buildInitial.Never write directly via node -e snippets. Always route through aweek exec plan-markdown <fn> so markdown edits are auditable and one-path.
All weekly-plan edits share one atomic execution path via plan.adjustPlan(...). Operations are validated up front — if any single operation fails validation, nothing is applied.
The heartbeat picks one task per distinct track per tick. Tracks are independent lanes that each fire at the cron cadence, so you can express "publish 3 X.com posts AND 4 Reddit posts in parallel this hour" without the two chains interfering.
track string on a task (e.g. "x-com", "reddit") opts that task into a specific lane.track is omitted, the task's objectiveId — treated here as a free-form string linking back to the markdown plan — is the default lane key. Tasks sharing an objectiveId pace together unless you set track to split them.Throughput budget: at */15 cron (4 ticks/hour), each track fires up to 4 tasks/hour. Per-agent throughput is roughly active_tracks × cron_frequency, bounded by how long each session runs (the per-agent lock is held across the full per-tick drain).
Load existing weekly plans and show the active week with its tasks: id · description · track · status · runAt. If no weekly plans exist, tell the user and route them to the create action below — this is the bootstrap path for a freshly-hired agent.
Use AskUserQuestion to pick the action, then collect required fields. Loop until done.
create → week (YYYY-Www, must NOT already exist on this agent), optional month (YYYY-MM, free-form tag linking the week to a monthly section of plan.md), optional seed tasks. Each task: description (required), optional objectiveId (free-form string, typically the monthly section heading it traces to), priority (critical / high / medium / low, default medium), estimatedMinutes (1-480), track, runAt. Newly created weekly plans are immediately eligible for the heartbeat — there is no approval gate.add → week (must exist), description (required), optional objectiveId, optional track, optional runAt.update → week, taskId, then at least one of: description, status (pending / in-progress / completed / failed / delegated / skipped), track (pass null to fall back to objectiveId pacing), runAt (pass null to clear).When the user picks create, run the four interview-trigger checks before the layout check or any task collection:
echo '{"agentId":"<AGENT_ID>","dataDir":".aweek/agents"}' \
| aweek exec plan checkInterviewTriggers --input-json -
Returns Array<{ trigger, reason, details }>. If the array is empty, skip directly to B2b.
If any triggers fire, offer the user two paths via AskUserQuestion:
{N} concern(s) found before generating this week's plan:
{for each trigger:
• {trigger.reason}}How would you like to proceed?
- Answer questions — I'll ask one question per concern (recommended for the best-fit plan).
- Skip questions — I'll apply best-guess assumptions for each concern, show them to you, and ask for your approval before continuing.
Route based on the choice:
Run an Ouroboros-style adaptive loop: every answer is scored across four weighted clarity dimensions, and the loop keeps drilling into the weakest one until the completion gate passes (overall ambiguity ≤ 0.20, every per-dimension floor met, two consecutive qualifying turns). Trigger-tailored questions seed the loop; once triggers are exhausted, follow-up questions target the weakest dimension.
Dimensions (matching src/skills/plan-ambiguity.ts):
| Key | Weight | Floor | Focuses on |
|---|---|---|---|
goalClarity | 35% | 0.75 | Specific, non-aspirational weekly outcomes |
taskSpecificity | 30% | 0.70 | Tasks concrete enough to dispatch without asking back |
prioritySequencing | 20% | 0.65 | Ordering / what to do first if the week compresses |
constraintClarity | 15% | 0.65 | Deadlines, dependencies, budget, availability |
echo '{"agentsDir":".aweek/agents","agentId":"<AGENT_ID>"}' \
| aweek exec plan-interview-store interviewExists --input-json -
true → ask via AskUserQuestion whether to resume the prior state (loadInterviewState) or discard and restart (clearInterviewState, then createInterviewState).false → call createInterviewState with initialContext set to the user's top-level plan intent (capture this from the opening exchange when create was chosen).Hold the returned state in session memory as STATE. After every mutation below, write it back with saveInterviewState.
Emit one AskUserQuestion per fired trigger, in array order. Tailor each question using the trigger's details object:
first-ever-plan — This is {agentName}'s first weekly plan. To make it actionable rather than a placeholder, tell me: what are the 2–3 outcomes you most want {agentName} to accomplish this week? Be specific — I'll use these to anchor every task I generate.conflicting-or-vague-goals (details.vague) — I looked at {agentName}'s plan.md and found: {details.vagueReason}. Describe what you want this agent to accomplish in the next 30 days — 2–3 specific, measurable outcomes.conflicting-or-vague-goals (details.conflicting) — I found {details.conflictingPairs.length} conflicting goal pair(s) in plan.md: {list pairs}. Which direction should take precedence for this week's plan?prior-week-problems — Last week ({details.priorWeekKey}), {details.totalFailed} task(s) failed ({failure rate}). {failed descriptions}. What was the main cause, and should I reduce load, re-scope, or shift areas?deadline-approaching — Heads-up: {approaching deadlines summary}. Prioritise deadline-critical work this week, or maintain balance? List any specific deliverables that must land.Run the scoring loop (P1-3) between every pair of trigger questions. Do not ask multiple trigger questions back-to-back without scoring in between — the model needs to know whether a dimension is already satisfied before drilling.
Once every fired trigger has been asked AND the gate still says qualifies: false, generate the next question adaptively:
echo '{"breakdown":<STATE.lastBreakdown>,"streak":<STATE.streak>}' \
| aweek exec plan-ambiguity buildAmbiguitySnapshot --input-json -
echo '{"breakdown":<STATE.lastBreakdown>}' \
| aweek exec plan-ambiguity weakestDimension --input-json -
STATE.turns transcript as context, compose one follow-up question targeting the weakest area. Rules for the question:
AskUserQuestion with options derived from the weakest dimension (e.g. for constraintClarity, offer "There is a deadline — let me specify", "No hard deadline", "Unclear / need to check").After each answer lands:
echo '{"state":<STATE>,"turn":{"question":"<Q>","answer":"<A>"}}' \
| aweek exec plan-interview-store appendTurn --input-json -
# then:
echo '{"agentsDir":".aweek/agents","agentId":"<AGENT_ID>","state":<STATE>}' \
| aweek exec plan-interview-store saveInterviewState --input-json -
echo '{"initialContext":"<STATE.initialContext>","transcript":<STATE.turns>}' \
| aweek exec plan-ambiguity buildScoringPrompt --input-json -
{ system, user }.Task tool with subagent_type: "general-purpose", model: "haiku", and the concatenated system + user strings as the prompt. The subagent's final text response is the raw input for parseScoreResponse.echo '{"raw":"<LLM_JSON>"}' \
| aweek exec plan-ambiguity parseScoreResponse --input-json -
echo '{"prevStreak":<STATE.streak>,"breakdown":<BREAKDOWN>}' \
| aweek exec plan-ambiguity updateStreak --input-json -
echo '{"breakdown":<BREAKDOWN>,"streak":<NEW_STREAK>}' \
| aweek exec plan-ambiguity qualifiesForCompletion --input-json -
qualifies: true → announce completion (see P1-5), clearInterviewState, proceed to B2b.qualifies: true, stop and ask the user whether to answer two more questions or continue with a lower-confidence plan.weakestDimension for 3 consecutive turns with no score improvement, rotate to the second-weakest dimension on the next turn.partial: true when closing.When qualifies: true OR the cap is reached AND the user chose to continue, recap and suggest tasks before B2b. clearInterviewState once the plan is saved in B3.
echo '{"triggers":<TRIGGERS_JSON>}' \
| aweek exec plan generateSkipAssumptions --input-json -
echo '{"assumptions":<ASSUMPTIONS_JSON>}' \
| aweek exec plan formatAssumptionsBlock --input-json -
Echo the returned markdown block verbatim, then ask the user via AskUserQuestion whether to apply the assumptions, run the full interview, or cancel.
After the interview gate (B2a), run the ambiguity detector before collecting week/month/task details:
echo '{"agentsDir":".aweek/agents","agentId":"<AGENT_ID>"}' \
| aweek exec plan detectLayoutAmbiguity --input-json -
If confident === true — display a one-line note about the detected mode and continue collecting week/month/tasks.
If confident === false — ask the user via AskUserQuestion to pick between Theme Days / Priority Waterfall / Mixed before collecting any task details.
When an objective needs the same action done multiple times within one day+hour, emit a separate task per repetition with distinct runAt minutes instead of sharing a runAt. Pick runAt minutes from {00, 10, 20, 30, 40, 50} so the heartbeat picks them up one tick apart.
Show queued adjustments grouped by action. Ask "Apply these N adjustments? (yes / no)" via AskUserQuestion. On yes, call plan.adjustPlan(...) once and display the result. On no, discard and return to B2.
echo '{"agentId":"<AGENT_ID>","weeklyAdjustments":[...],"dataDir":".aweek/agents"}' \
| aweek exec plan adjustPlan --input-json -
One action MUST collect an explicit AskUserQuestion confirmation before the skill module proceeds:
| Operation | Branch |
|---|---|
| Reseed plan.md from the template | A |
The underlying adapters refuse to run without confirmed: true — do not bypass the gate.
/aweek:hire — create a new agent + seed its plan.md./aweek:calendar — visualize the active weekly plan as a day × hour grid./aweek:summary — cross-agent status dashboard./aweek:recurring — manage per-agent recurring task rules that the heartbeat materializes into weekly plans automatically (no create needed even on weeks with no hand-authored plan)..aweek/agents/<slug>.json — agent scheduling wrapper..aweek/agents/<slug>/plan.md — free-form planning markdown (this skill's primary authoring surface)..aweek/agents/<slug>/weekly-plans/<YYYY-Www>.json — per-week task lists.Show the project's `.aweek/config.json` knobs (plus the hardcoded scheduler/lock constants the dashboard surfaces) and edit the config-backed fields with a confirmation gate
Manage Google-Calendar-style recurring tasks per agent — daily/weekly/biweekly/monthly rules that expand into occurrences on the calendar and execute through the heartbeat loop
Agent → CEO report. Send a status report or question to the user (CEO) on behalf of an agent. Notifies the dashboard inbox and, when a Slack `ceoChannel` is configured, pushes a Slack message.
Bootstrap an aweek-branded Slack app and persist its credentials so the embedded SlackAdapter inside `aweek serve` can chat with the project's Claude through Slack
Set up an aweek project — create the data directory and optionally install the scheduled heartbeat (launchd on macOS, crontab elsewhere)
Remove the aweek heartbeat and/or the .aweek/ data directory from a project