ia-reflect

name	ia-reflect
class	tool
description	Session retrospective and skill audit. Use when asked to reflect, do a retrospective, review lessons learned, audit what went well or wrong, or review session effectiveness.

Reflect

Success Criteria

• Every mistake/friction point cites the specific moment and its impact
• Improvements are actionable and prioritized (cap defined in step 4)
• Each skill audit proposes measurable changes (not vague suggestions)
• User is asked which items to persist to memory
• If review activity occurred, review-trap patterns are captured to persistent memory, or explicitly marked as "none"

Process

1. Session Review

Scan the full conversation. For each finding, cite the specific exchange (quote or paraphrase) and its impact.

Category	Signal
Mistakes	Wrong outputs, incorrect assumptions, hallucinated facts
Friction	Repeated clarifications, verbose responses, misread intent
Wasted effort	Work discarded, wrong approaches tried first
Wins	Approaches worth repeating, smooth interactions

Skip one-time typos, external tool failures, and issues outside agent control.

2. Review Activity Scan (if applicable)

If the session included PR or MR review activity in either direction, run this scan before moving on. Skip only if no reviews happened.

Inbound (my code was reviewed): For each review comment received:

• Did I accept it? If yes, what pattern did the reviewer catch that I missed? Is it a recurring blind spot? Capture the one-liner to persistent memory.
• Did I push back? If I was right and the reviewer was wrong, nothing to capture. If I was wrong and had to retract mid-thread, capture what I learned.

Outbound (I reviewed someone else's code): For each comment I authored:

• Was it accepted? Nothing to capture -- good call.
• Was it rejected with a valid counter? That's a review trap. Capture the pattern: what heuristic did I apply that produced a wrong comment?

"No harvestable items" is a valid outcome -- say so explicitly. Don't let the step quietly drop off.

3. Operational Learnings

Before listing improvements, scan the session for operational insights worth preserving. Apply the 5-minute filter: would knowing this save 5+ minutes in a future session? If yes, include it. Examples: a project-specific quirk, a project command that failed for a project-specific reason, an approach that worked better than expected.

Exclude harness-level noise — "File has not been read yet", token-limit truncations, bash-quoting slips, and other tooling artifacts. Those aren't project learnings; capture the project's behavior, not the agent's mechanics.

4. Improvements

Numbered list of concrete improvements, ranked by impact. Each item: one sentence, imperative, actionable. Cap at 10 items: if more surface, the bottom items are noise -- drop them rather than batching or splitting.

Ask: "Which of these should I remember for future chats?"

Save approved items to memory files at ~/.claude/projects/<project-slug>/memory/ (replace <project-slug> with the slug matching the current working directory, e.g., -home-ilia-ai-whetstone) using the Write tool with proper frontmatter (see MEMORY.md index).

Before writing, grep the existing memory directory for the item's key terms. On a near-duplicate, update that file instead of adding a second. On a direct contradiction with an entry already on file ("use tabs" when "use spaces" is recorded), do not blind-append — surface both and let the user choose merge, replace, or keep-both. Silent duplicate and contradiction accumulation is the main way a curated memory index rots.

5. Skill Audit (if skills were used)

For each skill invoked during the session:

A. Self-check gate -- If the skill lacks success criteria + verification loop:

• Add ## Success Criteria at top (3-5 measurable checks)
• Add ## Self-Check at bottom: "Verify all success criteria are met before presenting output. If not, iterate (max 5 times)."

B. Token efficiency -- Flag: redundant phrasing, mergeable sections, oversized examples, "Claude already knows this" content, inert frontmatter metadata.

C. Other -- Missing edge cases, vague directives (rewrite as measurable criteria or remove), naked negations (add "do Y instead" or remove).

Present proposed changes as diffs. Ask: "Apply these? (all / pick / skip)"

6. Capture Markers

The remember: prefix is the highest-confidence capture signal. When the user writes a message beginning with remember:, treat everything after the colon as a memory candidate — no interpretation required. Save directly to the appropriate memory file with a one-line summary and the user's exact phrasing. "Directly" waives interpretation, not the step-4 pre-write check: still grep existing memory for duplicates and contradictions before writing (a remember: that contradicts a recorded entry gets the same merge/replace/keep-both handling). Example: remember: we never use Pest, always PHPUnit → save to feedback_phpunit_over_pest.md.

Correction patterns to watch for (lower-confidence, batch these for review at /ia-reflect time):

• "no, use X" / "actually, X" / "don't use Y, use X"
• "stop doing X" / "never X"
• "that's wrong — the right way is..."
• repeated clarifications of the same thing within a session

Optional capture hook: a UserPromptSubmit hook can pattern-match the markers above into ~/.claude/learnings-queue.json as the user types, so /ia-reflect processes the queue deterministically instead of re-scanning the full transcript. Not shipped with this skill; document the convention and leave implementation to users who need it.

7. Pattern Detection

If 2+ similar tasks appear that no existing skill covers, suggest a new skill (1-2 sentence description). Create only after confirmation.

Proactive trigger: When the user corrects you, clarifies the same thing twice, or shows frustration, append: "Tip: Type /ia-reflect when you're ready -- I'll review what we can improve."

Self-Check

Before presenting output, verify all success criteria are met. If any fail, revise (max 5 iterations).