| name | case-study-capture |
| description | Log a manual observation about the current session. Invoke when the user runs /case-study-capture to record something noteworthy that automatic hooks cannot detect -- a successful pattern, a human override, a context architecture insight, or friction the hooks missed. |
| disable-model-invocation | true |
| allowed-tools | Bash, Read |
| argument-hint | ["observation description"] |
Capture Manual Observation
Record a single observation to the case study harness log. Follow these steps in order.
Step 1: Get the observation
The user's observation is: $ARGUMENTS
If the arguments are empty, ask the user to describe what they observed and wait for their response
before continuing.
Step 2: Classify the category
Based on the user's description and the current conversation context, assign exactly one category from
the list below. Do NOT ask the user to choose -- determine the category yourself.
| Category | Use when |
|---|
successful_pattern | Something the agent did well that is directly attributable to a harness decision (rule, skill, CLAUDE.md section, hook) |
human_override | The user stepped in to correct something the harness should have prevented or guided |
context_architecture | An observation about where context lives (rule vs. skill vs. CLAUDE.md vs. memory) and whether that placement worked |
friction | The agent misunderstood, went off-track, or hit a wall -- use when the automatic friction hook did not capture the event |
other | The observation does not fit the above categories |
Step 3: Generate a context summary
Write a 1-3 sentence summary of what was happening in the session when this observation occurred.
Include: what task was in progress, what the agent just did, and why the observation matters. This
summary provides context that the user's description alone may not capture.
Step 4: Run the logging script
Invoke the script with the three values you determined above:
python3 "${CLAUDE_SKILL_DIR}/scripts/write_manual_entry.py" \
"<category>" \
"<user_description>" \
"<context_summary>"
Replace placeholders with the actual values. Quote each argument to preserve spaces and special
characters.
The script validates the category, constructs the JSONL entry with a UTC timestamp, and appends it
to case-study-harness/data/manual-observations.jsonl. It prints a confirmation line on success.
Step 5: Confirm
Report to the user:
- The category you assigned and why
- The context summary you generated
- That the observation was logged (relay the script's confirmation)
