بنقرة واحدة
task
Structured operational hygiene for research, data gathering, and novel tasks that don't have their own play
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
القائمة
Structured operational hygiene for research, data gathering, and novel tasks that don't have their own play
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
Refresh priority signals, manage active epics, and refresh Near Term pool
Multi-session deliverable play for projects spanning 3+ sessions with concrete outputs (proposals, strategies, wireframes). Provides project-level structure, evidence provenance, and cross-session handoff.
Investigation-driven card grooming — investigate across data sources, synthesize findings into card content, present for approval
Use when acting as a reviewer in an AgenTerminal review conversation. Handles both code reviews (REVIEW_APPROVED) and plan reviews (PLAN_APPROVED).
Match Slack
Weekly release impact review — pull PostHog data for Released cards and tracked PRs, classify, draft observations, post to Slack
استنادا إلى تصنيف SOC المهني
| name | task |
| description | Structured operational hygiene for research, data gathering, and novel tasks that don't have their own play |
| disable-model-invocation | true |
For any task that doesn't have its own play: research, data gathering, one-off analysis, building something new. The task itself is unstructured, but the operational hygiene around it is not.
$ARGUMENTS is optional:
{task-name}: Used as the filename for the task brief
(box/research/{task-name}-brief.md). If omitted, derive from the task
description.Before starting, read these references:
reference/framework.md: tendency catalog, intervention model — you need
these for unstructured terrainreference/tooling-logistics.md: tested recipes for any data source
you're about to hit. Check this for gotchas, access patterns, and token
loading before attempting to access external sources yourself.box/posthog-events.md: if querying PostHog, check known event names
before guessingThe play moves through five phases. Set todos at play start and mark each phase transition explicitly.
[ ] Start: task brief created at box/research/{name}-brief.md
[ ] Plan: queries/steps written in task brief before executing
[ ] Execute: intermediate results saved to durable files
[ ] Synthesize: deliverable produced
[ ] Close: code committed, log updated, play promotion evaluated
Resuming a prior investigation (.agent/state/active.json exists):
active.json for the investigation ID and brief_pathbrief_pathpython3 box/agent-state.py views --investigation I-NNN to regenerate
brief.md from current state (always regenerate on resume regardless of
status — this eliminates stale views from interrupted sessions).agent/views/brief.md for current investigation state (verified vs.
tentative claims, open questions, recommended next action)python3 box/agent-state.py log session_start --investigation I-NNN --session-id $SESSION_IDStarting a new investigation (no active.json, or starting fresh):
box/research/task-brief-template.md to box/research/{name}-brief.mdreference/tooling-logistics.md for gotchas relevant to your data
sourcespython3 box/agent-state.py log session_start --session-id $SESSION_ID --brief-path box/research/{name}-brief.md (auto-generates investigation ID)The task brief is the durable artifact. If context compaction hits, the brief survives. If the session crashes, the next session can resume from the brief.
Write your queries, steps, and merge logic in the task brief before executing any of them. This is the phase gate: nothing runs until the plan is written down.
If you discover during planning that the task shape is unclear, say so. Planning is where ambiguity gets resolved, not mid-execution.
Get user approval on the brief before moving to Execute. The plan is a checkpoint, not just a file. Present it inline or via plan approval and wait for explicit approval before executing.
Save each intermediate result to a durable file as it comes back. The file paths should already be in the task brief from the Plan phase.
Log every data source interaction via the agent memory system:
python3 box/agent-state.py log source_read --source {name} --record-id {id}python3 box/agent-state.py log query_run --tool {name} --query "{text}"python3 box/agent-state.py log source_skipped --source {name} --reason "{why}"python3 box/agent-state.py log delegate_dispatched|delegate_collected --task-id {id} ...The --investigation flag is optional when active.json exists. Intermediate
results still save to box/research/ files as before — the event log tracks
that they were read.
When results look suspicious, stop and investigate. Do not rationalize unexpected data. The tendency is to explain why wrong data is acceptable rather than fix it. If a number looks off, the default is "this is wrong and I need to understand why," not "this is probably fine because..."
If you need to modify code (scripts, client libraries, etc.) as part of execution, note the changes in the task brief's Decisions section.
Context cost of data gathering. Subagent delegation protects main context
from volume reading but collect results stay in context for the rest of the
session. Use output_instructions to constrain return shape (file paths + key
findings, not full excerpts). For direct file reads, use line ranges — Grep
for structure first. Every KB here is a KB unavailable for Synthesize, Close,
and session-end.
⚠ SCOPE GATE. Before writing the deliverable, generate scope accounting from the event log:
python3 box/agent-state.py accounting
Present the machine-derived output to the user. Fill in the agent-supplied sections (plan commitments, additional context) before presenting. Wait for the user to confirm coverage is sufficient before writing.
The machine-derived sections (sources read, queries run, delegates, claim status) are authoritative — they come from the event log, not from memory. The agent-supplied sections are explicitly marked as manual input.
Re-read intermediate files before composing. The data was read earlier but the compose step must work from the files, not from memory of what they said. This activates even when the data feels recent and in-context — "I already read this" is the exact instinct that suppresses the re-read. Proved twice: 2026-03-27 (proposal reading), 2026-04-09 (phase assignments, blocking analysis).
Produce the deliverable. The shape depends on the task — could be a CSV, a report, a card draft, a script.
Log claims in the deliverable. Claims proposed in the deliverable should be logged:
python3 box/agent-state.py claim propose --body "..." --basis "..." --confidence tentative --source-refs E-NNN ...python3 box/agent-state.py claim verify --claim C-NNN --method "..." --result "..." --source-refs E-NNN ...python3 box/agent-state.py claim manifest --claims C-NNN ... --deliverable {path}
python3 box/agent-state.py validate
Correctness claims require primary source verification. When the deliverable includes judgments about whether an external system (bot, API, feature) produced a correct or incorrect result, label those judgments as candidates pending verification — not findings — until verified against the system's code, data, or actual behavior. Conversation text, user reports, and error descriptions are intermediaries, not primary sources. Verification is per-claim: verifying one claim does not validate others about different features. Proved 2026-04-16: 5 bot conversations labeled "bot was incorrect" from conversation evidence; codebase verification of 1 found the bot described a real feature applied to the wrong problem.
Writing to large target files. Edit returns the full file as confirmation.
For end-of-file appends of approved content, Bash cat >> avoids the
echo-back.
Compaction insurance. Use approve_content for composed prose going to
production surfaces (card descriptions, analysis comments, observations, email
drafts, findings messages). Formulaic mutations (tracked replies, "This
shipped!" replies, reactions, link-back lines, state changes) go through
execute_approved only. Saved files at
.agenterminal/approved/{content_type}/ survive compaction. After compaction,
read the saved file to recover approved text. See play-specific approval
points in box/shortcut-ops.md for content_type and filename conventions
per play.
⚠ NARRATION GATE. Close-phase steps feel procedural after the deliverable ships — completion bias activates here reliably. Before EACH step below: say one sentence to the user about what you are doing and wait for acknowledgment before tool calls that commit, write log entries, or update reference docs. Proved 2026-04-15: commit to main + two log entries + tooling update executed as autonomous stream, user had to interrupt.
python3 box/agent-state.py log session_end --session-id $SESSION_ID
python3 box/agent-state.py views
python3 box/log-cli.py write --date ... --topic ... --lesson ... --bullet ...This play is for tasks that hit external data sources and produce durable artifacts: CSVs, reports, scripts. It is not for conversation-shaped work like process design, brainstorming, or retrospectives, where the conversation itself is the deliverable. The user triggers this play explicitly; don't self-activate based on the word "novel."
Those flex to the task. The phases and their gates don't.