RFP pipeline
268 AI coding assistant skills, organized across 12 workflow layers. Sources include Anthropic official, FRM, SKC, LRN, SKA, and other mainstream AI coding frameworks.
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- 18 days oldThe repository was created 18 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
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What its author says it does
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RFC-driven multi-agent DAG execution pattern with quality gates, merge queues, and work unit orchestration.
SKILL.md
1.5 KB, 301 tokens by cl100k_base, as published. Nobody here has run it
RFP RFC Pipeline
Inspired by style RFC decomposition patterns and multi-unit orchestration workflows.
Use this skill when a feature is too large for a single agent pass and must be split into independently verifiable work units.
Pipeline Stages
- RFC intake
- DAG decomposition
- Unit assignment
- Unit implementation
- Unit validation
- Merge queue and integration
- Final system verification
Unit Spec Template
Each work unit should include:
iddepends_onscopeacceptance_testsrisk_levelrollback_plan
Complexity Tiers
- Tier 1: isolated file edits, deterministic tests
- Tier 2: multi-file behavior changes, moderate integration risk
- Tier 3: schema/auth/perf/security changes
Quality Pipeline per Unit
- research
- implementation plan
- implementation
- tests
- review
- merge-ready report
Merge Queue Rules
- Never merge a unit with unresolved dependency failures.
- Always rebase unit branches on latest integration branch.
- Re-run integration tests after each queued merge.
Recovery
If a unit stalls:
- evict from active queue
- snapshot findings
- regenerate narrowed unit scope
- retry with updated constraints
Outputs
- RFC execution log
- unit scorecards
- dependency graph snapshot
- integration risk summary