القائمة
Analyze task scope and recommend a bestwork team composition before executing
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/rlaope/bestwork-agent --skill planيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
Evidence-driven causal tracing — competing hypotheses, falsification, rebuttal round, recommended next probes
Feature validation gate — research, define purpose, stress-test from real user perspective before building
Maximum parallelism burst — all tasks execute simultaneously with no coordination overhead
عرض SKILL.md
استنادا إلى تصنيف SOC المهني
| name | plan |
| description | Analyze task scope and recommend a bestwork team composition before executing |
When this skill is invoked, IMMEDIATELY print:
[BW] analyzing scope...
The user's request follows after the skill invocation. Analyze it thoroughly before doing anything.
Gather concrete data about what the task requires. Do ALL of the following:
Identify affected files — based on the user's request, determine which files will need to be created, modified, or deleted. Use git ls-files, find, and your knowledge of the codebase to list them. Count them.
Identify domains — classify each file into a domain:
Estimate complexity — score 1-5 based on:
Based on the analysis, recommend ONE team mode:
Conditions: 1-2 files, single domain, complexity 1-2.
Pick the best-fit specialist from:
Print:
[BW] plan: SOLO
scope: {N} files, {domain}
complexity: {score}/5
agent: {specialist}
tasks:
1. {what will be done}
2. {what will be done}
confirm? (y/n)
Conditions: 3-5 files, 2 domains, complexity 2-3.
Pick 2 complementary specialists. Common combos:
Print:
[BW] plan: PAIR
scope: {N} files across {domains}
complexity: {score}/5
agents: {specialist1} + {specialist2}
tasks:
1. [{agent}] {what will be done}
2. [{agent}] {what will be done}
confirm? (y/n)
Conditions: 5-10 files, 3+ domains, complexity 3-4, or any task benefiting from quality gates.
Pick the best-fit trio from all 49 specialists:
Print:
[BW] plan: TRIO
scope: {N} files across {domains}
complexity: {score}/5
agents:
tech: {tech-specialist} — implements
pm: {pm-specialist} — verifies requirements
critic: {critic-specialist} — reviews quality
execution:
1. [{tech}] {implementation task}
2. [{pm}] verify: {what PM checks}
3. [{critic}] review: {what critic checks}
(max 3 feedback rounds if rejected)
confirm? (y/n)
Conditions: 10+ files, 4+ domains, complexity 4-5, feature work.
Pick a squad preset or custom composition:
Print:
[BW] plan: SQUAD
scope: {N} files across {domains}
complexity: {score}/5
squad: {preset or "custom"}
- {specialist1} ({role})
- {specialist2} ({role})
- {specialist3} ({role})
- {specialist4} ({role})
execution (parallel):
1. [{agent1}] {task}
2. [{agent2}] {task}
3. [{agent3}] {task}
4. [{agent4}] {task}
resolution: majority vote on disagreements
confirm? (y/n)
Conditions: 10+ files, architecture changes, complexity 5, refactoring, or security-critical.
Pick a hierarchy preset:
Print:
[BW] plan: HIERARCHY
scope: {N} files, architecture-level
complexity: {score}/5
chain: {preset}
4. {C-level} — final approval
3. {Lead} — architecture review
2. {Senior} — implementation review
1. {Junior} — implementation
execution (bottom-up, review top-down):
1. [{junior}] implement: {task}
2. [{senior}] review + improve
3. [{lead}] architecture check
4. [{c-level}] final decision
confirm? (y/n)
Wait for user input. Accept:
[BW] plan cancelled.Before execution, ALWAYS save the plan to disk:
mkdir -p .bestwork/plans
Write the plan as JSON to .bestwork/plans/<timestamp>-<slug>.json:
{
"created": "<ISO timestamp>",
"task": "<user's original request>",
"mode": "solo|pair|trio|squad|hierarchy",
"scope": { "files": ["..."], "domains": ["..."], "complexity": N },
"agents": [{ "name": "tech-backend", "role": "tech", "tasks": ["..."] }],
"status": "pending|executing|complete|cancelled"
}
Also write a human-readable summary to .bestwork/plans/<timestamp>-<slug>.md.
This enables:
./plan --last — re-load and resume the most recent plan./plan --list — list all saved plansWhen the user says ./plan --last:
.json file from .bestwork/plans/Once confirmed, update the saved plan's status to "executing". Then route to the appropriate team mode. Print:
[BW] executing plan...
Then execute based on the confirmed mode:
SOLO: Load the specialist's prompt from prompts/tech/{name}.md (or the matching category). Execute the task directly using that agent's expertise and perspective.
PAIR: Execute sequentially — first specialist handles their domain files, second specialist handles theirs. Cross-review at the end.
TRIO: Execute as ./trio — spawn Tech agent (implement), PM agent (verify), Critic agent (review). Feedback loop up to 3 rounds. Record to meeting log at .bestwork/state/meeting.jsonl.
SQUAD: Spawn all agents in parallel using run_in_background. Each agent works on their assigned files. Disagreements resolved by majority vote. Record to meeting log.
HIERARCHY: Execute bottom-up (Junior implements first), review top-down (C-level approves last). Each level can send work back down with feedback. Record to meeting log.
For trio/squad/hierarchy, write the meeting log:
mkdir -p ~/.bestwork/state && echo '{"type":"header","teamName":"plan","mode":"<MODE>","task":"<TASK>","classification":"PLAN","developerCount":<N>,"routingReason":"plan skill recommendation"}' > .bestwork/state/meeting.jsonl
After execution completes, update the saved plan's status to "complete" and print:
[BW] plan complete. mode={MODE}, agents={N}, files={N}.
saved: .bestwork/plans/<filename>.md