/hk-local-diagnose — Diagnostic Script Runner
SkillProductivityRun diagnostic scripts on tasks, specs, boards, or reflections. Wraps testing_tools/ with proper working directory and output mode selection. Use this skill whenever the user wants to inspect, debug, or check the state of a task, spec, board, or reflection — even if they don't say 'inspect' explicitly. Triggers on: 'why did task X fail', 'show me spec Y', 'what's on the board', 'check task', 'inspect', 'diagnose', or /hk-local-diagnose.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the /hk-local-diagnose — Diagnostic Script Runner skill
What this skill tells your AI
The instructions your AI receives, as published by deepklarity/harness-kit in .claude/skills/hk-local-diagnose/SKILL.md and read by ahel’s review.
Run the right diagnostic script with the right flags, from the right directory. No more remembering paths or cd-ing around.
Usage
/hk-local-diagnose task 42
/hk-local-diagnose task 42 --brief
/hk-local-diagnose spec 15 --json --sections tasks,problems
/hk-local-diagnose board
/hk-local-diagnose board 3
/hk-local-diagnose reflection 8 --full
/hk-local-diagnose snapshot sp25 ../../tests/e2e_snapshots/smoke
Arguments
Parse $ARGUMENTS to extract:
- type (required):
task,spec,board,reflection, orsnapshot - id (required for all except
board): the numeric ID or spec prefix - flags (optional):
--brief,--full,--json,--slim,--sections <list>
If no flags are provided, default to --brief — this is the token-efficient choice for LLM consumption. The user can always ask for --full if they need more.
Script mapping
| Type | Script | Required args |
|---|---|---|
task | task_inspect.py <id> | id |
spec | spec_trace.py <id> | id |
board | board_overview.py [id] | id optional |
reflection | reflection_inspect.py <id> | id |
snapshot | snapshot_extractor.py <id> <output_dir> | id + output_dir |
Execution
All scripts run from the taskit/taskit-backend/ directory. The working directory for execution is always:
REPO_ROOT/taskit/taskit-backend/
Where REPO_ROOT is the git repository root (find it with git rev-parse --show-toplevel).
Step 1: Resolve the repo root
REPO_ROOT=$(git rev-parse --show-toplevel)
Step 2: Build and run the command
cd "$REPO_ROOT/taskit/taskit-backend" && python testing_tools/<script> <id> [flags]
Pass through any --brief, --full, --json, --slim, or --sections flags directly to the script.
Step 3: Display the output
Print the script output directly. Do not summarize or interpret — the scripts already produce well-structured output with problem detection built in.
If the script exits with a non-zero code, show the error and suggest:
- Check that the ID exists: "Is task/spec/board {id} a valid ID?"
- Check that the Django app is set up: "Is the taskit-backend database accessible?"
Error handling
If $ARGUMENTS is empty or missing the type, print usage help:
Usage: /hk-local-diagnose <type> <id> [flags]
Types: task, spec, board, reflection, snapshot
Flags: --brief (default), --full, --json, --slim, --sections <list>
Examples:
/hk-local-diagnose task 42
/hk-local-diagnose spec 15 --json --sections tasks,problems
/hk-local-diagnose board
Signals
- GitHub stars
- 99
- Forks
- 12
- Last commit
- Jul 2026
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hk-local-diagnose- Source
- github.com/deepklarity/harness-kit