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skill-cslib-research
Research CSLib formalization patterns and Mathlib API for CSLib contributions. Invoke for cslib research tasks.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
メニュー
Research CSLib formalization patterns and Mathlib API for CSLib contributions. Invoke for cslib research tasks.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | skill-cslib-research |
| description | Research CSLib formalization patterns and Mathlib API for CSLib contributions. Invoke for cslib research tasks. |
| allowed-tools | Agent, Bash, Edit, Read, Write |
Thin wrapper that delegates CSLib research to cslib-research-agent subagent.
This skill activates when:
Validate task_number exists and task_type is "cslib".
Update status to "researching" BEFORE invoking subagent.
Domain-specific context for the cslib-research-agent:
.claude/extensions/cslib/context/{
"session_id": "sess_{timestamp}_{random}",
"delegation_depth": 1,
"delegation_path": ["orchestrator", "research", "skill-cslib-research"],
"timeout": 3600,
"task_context": {
"task_number": N,
"task_name": "{project_name}",
"description": "{description}",
"task_type": "cslib"
},
"focus_prompt": "{optional focus}",
"metadata_file_path": "specs/{NNN}_{SLUG}/.return-meta.json"
}
Retrieve relevant memories and literature briefing to inject into the delegation context.
Skip memory if: clean_flag is true (from --clean command flag).
# Check clean_flag
if [ "$clean_flag" != "true" ]; then
memory_context=$(bash .claude/scripts/memory-retrieve.sh "$description" "$task_type" "$focus_prompt" 2>/dev/null) || memory_context=""
fi
# memory_context will be empty string if:
# - clean_flag is true (skipped)
# - memory-index.json missing or empty
# - no keywords matched any entries
# - script exited with error
# Literature briefing injection (independent of clean_flag)
lit_context=""
if [ "$lit_flag" = "true" ]; then
lit_context=$(bash .claude/scripts/literature-briefing.sh 2>/dev/null) || lit_context=""
fi
# lit_context will be empty string if:
# - lit_flag is not "true" (skipped)
# - specs/literature/ sub-index is empty or missing
# - script exited with error
Note: lit_flag is independent of clean_flag. Using --clean --lit suppresses memory retrieval but still injects literature briefing. Literature briefing is gated solely on lit_flag == "true".
Use Agent tool with subagent_type: "cslib-research-agent".
Include memory_context and lit_context in the prompt if non-empty:
memory_context is non-empty, include it as a <memory-context> block after the delegation context.lit_context is non-empty, include it as a <literature-briefing> block after the memory context.CRITICAL: If you performed the work above WITHOUT using the Agent tool (i.e., you read files,
wrote artifacts, or updated metadata directly instead of spawning a subagent), you MUST write a
.return-meta.json file now before proceeding to postflight. Use the schema from
return-metadata-file.md with the appropriate status value for this operation.
If you DID use the Agent tool, skip this stage -- the subagent already wrote the metadata.
The following stages MUST execute after work is complete, whether the work was done by a subagent or inline (Stage 4b). Do NOT skip these stages for any reason.
Read the metadata file from specs/{N}_{SLUG}/.return-meta.json.
Update state.json and TODO.md based on result.
Add research artifact to state.json. Update TODO.md per @.claude/context/patterns/artifact-linking-todo.md with field_name=**Research**, next_field=**Plan**.
Commit changes with session ID.
Brief text summary (NOT JSON).
Orchestrate multi-agent implementation with parallel phase execution. Spawns teammates for independent phases and coordinates dependent phases. Includes debugger teammate for error recovery.
Orchestrate multi-agent planning with parallel plan generation. Spawns 2-3 teammates for diverse planning approaches and synthesizes into final plan with trade-off analysis.
Orchestrate multi-agent research with wave-based parallel execution. Spawns 2-4 teammates for diverse investigation angles and synthesizes findings.
Full structural hard-mode orchestration state machine with per-phase dispatch (H1), adversarial verification (H4), convergence policing (H6), territory contracts (H7), and churn detection (H5). Invoke for /orchestrate --hard.
Autonomous state machine that drives a task through its full lifecycle (research -> plan -> implement -> complete) without user confirmation between phases. Invoke for /orchestrate command.
Execute hard-mode implementation with anti-analysis contracts, per-phase dispatch, and territory-aware execution. Invoke for --hard implementation tasks.