---
name: "mkl-prune-context"
description: "Prune verbose histories, logs, and resolved turns during long debugging or triage sessions into a stateful compact handoff."
---

<skill name="mkl-prune-context">
  <description>Prune verbose histories, logs, and resolved turns during long debugging or triage sessions into a stateful compact handoff.</description>
  <context>
    Compress accumulated conversation history, trial outputs, and resolved turns into a compact handoff artifact (< 300 tokens). Use during long sessions or before subagent delegation to prevent context degradation and eliminate token waste.
  </context>
  <section name="trigger-conditions">
    Initiate context pruning when:
    - The session reaches 15+ turns or approaches token limits.
    - Iterative debugging left long stack traces, compiler dumps, or reverted edits in history.
    - The user requests a session summary, checkpoint, `/compact`, or clean handoff.
    - Preparing to hand off state to an isolated subagent or fresh prompt session.
  </section>
  <section name="pruning-strategy-retain-vs-discard">
    - **Retain (Keep in Handoff)**: Exact user objective and constraints, deterministic repro command and exit code, verified architectural invariants, target file paths and lines, and 1-line list of disproven theories.
    - **Discard (Strip Entirely)**: Chit-chat, full tracebacks, multi-page build logs, speculative opinions, unmodified file reads, and reverted patch diffs.
  </section>
  <workflow name="structured-handoff-template">
    Format the compressed state using this schema (< 250 tokens):

    ```markdown
    ## 🧭 Compact Session Handoff

    - **Objective**: [1-line problem statement or task goal]
    - **Environment & Invariants**: [Python/OS versions, constraints, zero-dependency rules]
    - **Verified Findings**:
      - [Root cause anchor or verified reproduction fact]
      - [Key requirement confirmed by tests or maintainer guidelines]
    - **Disproven Hypotheses**:
      - [Discarded path 1 — do not re-investigate]
    - **Current Suspect Scope**: `path/to/file.py:SymbolName` (lines X-Y)
    - **Reproduction**: `pytest tests/test_target.py -k test_case`
    - **Next Action**: [Single concrete step to execute next]
    ```
  </workflow>
  <example type="authored">
    ### Anti-pattern (Lossy Pruning)
    Summarizing as *"Tested a few fixes that failed, now trying the parser"*. This loses negative constraints and the reproduction command, forcing future turns to repeat disproven dead ends.

    ### Recommended Worked Example
    An 18-turn, 4,500-token multi-GPU deadlock debugging log is pruned down to:

    ```markdown
    ## 🧭 Compact Session Handoff

    - **Objective**: Fix rank-0 NCCL deadlock when worker queue is empty in `torch.distributed`.
    - **Environment & Invariants**: Linux, PyTorch 2.4, CUDA 12.4. Zero external dependencies.
    - **Verified Findings**:
      - Deadlock triggers only when batch size is indivisible by world size on rank 0.
      - Repro verified: `python3 -m torch.distributed.run --nproc_per_node=2 test_deadlock.py`
    - **Disproven Hypotheses**:
      - Not an NCCL timeout (`NCCL_ASYNC_ERROR_HANDLING=1` still hangs).
    - **Current Suspect Scope**: `src/distributed/sampler.py:ShardBatchSampler.__iter__` (lines 88-104)
    - **Reproduction**: `python3 test_deadlock.py` (exits 124 on hang, 0 on fix)
    - **Next Action**: Pad remainder batch with sentinel index on rank 0 instead of early break.
    ```

    Acceptance: The 4,500-token history is replaced with 160 high-signal tokens containing all facts required to continue without prior context.
  </example>
</skill>