pilot-map-reduce
Distributed map-reduce over agent swarms for parallel data processing. Use this skill when: 1. You need to process large datasets across multiple workers 2. You want parallel map phase followed by aggregating reduce phase 3. You have embarrassingly parallel tasks with combine step Do NOT use this skill when: - Tasks are not parallelizable (use single worker) - You need streaming results (use pilot-load-balancer)
Informações da origem
- Repositório
- TeoSlayer/pilot-skills
- Última atividade na origem
- 20 de abril de 2026 às 17:42
- Idioma detectado do SKILL.md
- inglês
- Estrelas
- 8
- Forks
- 2
Opções de instalação
Por padrão, está selecionado o prompt que primeiro revisa a origem. Você pode mudar para um comando direto ou baixar uma cópia local.
Revise os arquivos de origem
Leia o SKILL.md e os arquivos complementares exibidos pelo SkillsMP antes de decidir se vai instalar.
Explorador de arquivos
2 arquivosExibindo SKILL.md
SKILL.md
Instruções da origem · Visualização somente leitura- name
- pilot-map-reduce
- description
- Distributed map-reduce over agent swarms for parallel data processing. Use this skill when: 1. You need to process large datasets across multiple workers 2. You want parallel map phase followed by aggregating reduce phase 3. You have embarrassingly parallel tasks with combine step Do NOT use this skill when: - Tasks are not parallelizable (use single worker) - You need streaming results (use pilot-load-balancer)
- tags
- ["pilot-protocol","map-reduce","distributed-computing","parallel-processing"]
- license
- AGPL-3.0
- compatibility
- Requires pilot-protocol skill and pilotctl binary on PATH. The daemon must be running (pilotctl daemon start).
- metadata
- {"author":"vulture-labs","version":"1.0","openclaw":{"requires":{"bins":"[Truncated]"},"homepage":"https://pilotprotocol.network"}}
- allowed-tools
- ["Bash"]
# pilot-map-reduce
Implement distributed map-reduce patterns for parallel data processing across agent swarms.
## Commands
### Submit map tasks to workers
```bash
TOTAL_WORKERS=$(pilotctl --json peers --search "role:mapper" | jq 'length')
for i in $(seq 0 $((TOTAL_WORKERS - 1))); do
WORKER=$(pilotctl --json peers --search "role:mapper" | jq -r ".[$i].address")
pilotctl --json send-message "$WORKER" \
--data "{\"type\":\"map_task\",\"job_id\":\"$JOB_ID\",\"chunk_start\":$((i * 1000)),\"chunk_end\":$(((i + 1) * 1000))}"
done
```
### Collect map results
```bash
EXPECTED_RESULTS=$TOTAL_WORKERS
RECEIVED=0
while [ $RECEIVED -lt $EXPECTED_RESULTS ]; do
RESULTS=$(pilotctl --json received \
| jq '[.messages[] | select(.payload.type == "map_result" and .payload.job_id == "'$JOB_ID'")] | length')
RECEIVED=$RESULTS
sleep 1
done
```
### Shuffle and reduce
```bash
MAP_RESULTS=$(cat /tmp/map-results-$JOB_ID.json)
KEYS=$(echo "$MAP_RESULTS" | jq -r '.[].payload.results | to_entries | .[].key' | sort -u)
for key in $KEYS; do
VALUES=$(echo "$MAP_RESULTS" | jq -r '[.[].payload.results."'$key'" // empty] | flatten')
pilotctl --json send-message "$REDUCER" \
--data "{\"type\":\"reduce_task\",\"job_id\":\"$JOB_ID\",\"key\":\"$key\",\"values\":$VALUES}"
done
```
## Workflow Example
Word count across distributed text corpus:
```bash
#!/bin/bash
JOB_ID="wordcount-$(date +%s)"
# MAP phase
MAPPERS=$(pilotctl --json peers --search "role:mapper" | jq -r '.[].address')
for i in $(seq 0 9); do
pilotctl --json send-message "${MAPPERS[$i]}" \
--data "{\"type\":\"map_task\",\"job_id\":\"$JOB_ID\",\"chunk\":$i}" &
done
wait
# REDUCE phase
sleep 5
MAP_RESULTS=$(pilotctl --json received \
| jq '[.messages[] | select(.payload.type == "map_result")]')
FINAL=$(echo "$MAP_RESULTS" | jq 'map({(.payload.word): .payload.count}) | add')
echo "$FINAL"
```
## Dependencies
Requires pilot-protocol skill, jq, and sort.
Ver no GitHub