Retro distill

Self-Distillation Retrospective

You run structured retrospectives on recent AI coding-agent sessions to extract reusable patterns, anti-patterns, and workflow improvements. This is your self-improvement loop – inspired by simple self-distillation (SSD), where a model improves by sampling its own outputs, truncating the bad tails, and reshaping what remains.

The loop: Sessions produce outputs (samples) --> retro identifies what worked and what didn't (truncation) --> learnings get codified into skills, memories, and rules (reshaping) --> future sessions benefit.

Before Starting

1. Determine retro mode

Mode	Trigger	Depth	Time
Quick	User says "retro", or a major chunk or failure merits learning capture	Review current session only	2-3 min
Deep	After 5-10 sessions, user says "deep retro" or "distill"	Batch review of recent session logs	10-15 min
Failure	Something went wrong, user says "failure retro" or "what went wrong"	Root-cause analysis of a specific failure	5-10 min

Default to Quick unless the user specifies otherwise.

2. Gather inputs

Quick mode:

• Review the current conversation context
• Check recent git log for what was committed

Deep mode:

• Read the last 5-10 session logs from your session-logs directory (e.g. {your session-logs dir})
• Search relevant feedback memories by touched domain and failure pattern; do not bulk-load unrelated history
• Read the current anti-pattern lists in any skill references/

Failure mode:

• Get the specific failure context (error, bad output, wasted effort)
• Read the session log where it happened
• Read the relevant skill/prompt that produced the failure

Quick Retro Protocol

Run through these four questions for the current session:

Q1: What worked? (Fork preservation)

Identify approaches that produced good results – especially non-obvious ones the system doesn't already codify. These are "forks" that led to correct solutions.

• Was there a skill that worked well?
• Did a specific prompt structure produce good Codex output?
• Did a debugging approach find the root cause quickly?
• Was there a convention that prevented a mistake?

Q2: What didn't work? (Support compression)

Identify approaches that wasted time or produced bad results. These are "distractor tails" to suppress.

• Did Codex go down a wrong path? Why?
• Was there a missing constraint that caused scope creep?
• Was there a stale reference that caused confusion?
• Did a skill fail to trigger or produce poor output?

Q3: Lock or fork misclassification?

The most common failure mode. Check if any part of the session:

• Treated a lock as a fork – left something open-ended that should have been constrained (e.g., "choose the best approach" for something with one right answer)
• Treated a fork as a lock – over-constrained something that needed exploration (e.g., specifying exact implementation for a design decision)

Q4: What should change?

Before picking an artifact, run the skill-update triage. Memory is the easy default but often the wrong one – memories are passive context whereas skills are active templates that ship their rules into every invocation. If a finding describes a how-to-do-this-kind-of-work pattern, it belongs in a skill, not a memory.

Skill-update triggers (check these first):

• A prompt this session used a pattern the skill template didn't enforce → update the skill, not just memory. Memory won't auto-apply to future prompts; skill content will.
• A prompt template missed a constraint that had to be added inline (e.g., "use a non-destructive swap, not DROP CASCADE", "verify at every dependent view layer", "check internal sources before external") → update the relevant skill's task-type section.
• A workflow produced a pre-commit hook block, lint warning, or CI failure that a better template would have avoided → update the skill.
• A pattern appeared in 2+ sessions in the last week → promote to skill content even if each individual session fixed it with memory.
• A claim the skill makes turned out false or stale (referenced file moved, command renamed) → update the skill.

If a finding passes any of the above, default target is skill content. Memory is for project/user/feedback context that applies across many skills, not for skill-internal rules.

Then classify each finding into an action:

Finding type	Action	Target artifact
New reusable pattern (how-to)	Add to relevant skill	{your skills dir}/{skill}/SKILL.md or references/
New anti-pattern	Add to anti-pattern list in skill	{your skills dir}/{skill}/references/anti-patterns.md
Skill template missed a constraint	Edit the template section in the skill	{your skills dir}/{skill}/SKILL.md
Stale skill claim (wrong file path, wrong command)	Fix the skill	{your skills dir}/{skill}/SKILL.md or references/
Workflow correction (general, cross-skill)	Save feedback memory	Memory system
Project context learned (data, infra, business)	Save project memory	Memory system
Convention change (global rules)	Update rules	AGENTS.md
Stale reference in docs (not in a skill)	Fix or remove	The referenced doc

Note on skills directories: OpenAI Codex reads .agents/skills/ (repo-scoped) and $HOME/.agents/skills/ (user-scoped); other Agent-Skills-compatible agents use their own conventions. Substitute {your skills dir} with whatever your agent loads.

When in doubt between skill vs memory: ask "will future sessions need this as part of doing the work, or as context before doing the work?" Part-of-the-work → skill. Context-before → memory.

Quick Retro Output

Produce a brief summary:

## Quick Retro: {date}

**Session:** {session description}

**Worked well:**
- {pattern} --> {already codified / NEW: save to X}

**Didn't work:**
- {anti-pattern} --> {already known / NEW: add to X}

**Lock/fork misclassification:**
- {none / description}

**Actions taken:**
- {list of actual changes made}

Then execute the actions – don't just list them. Update the smallest authoritative artifacts.

Bias correction: if every action targets memory and none targets a skill, rerun skill-update triage once. Never edit a skill merely to hit a ratio; manufactured churn bloats context and weakens triggering.

Evidence threshold: promote a lesson only when session contains observable support. One surprising event may justify a safety rule when impact is severe; ordinary workflow preference should recur before becoming global guidance.

Deep Distillation Protocol

Step 1: Collect session data

Read the last N session logs (default 10, or since last deep retro). For each, extract:

• Task type (fix, build, ops, audit, refactor, content, design)
• Outcome (success, partial, failure, abandoned)
• Time spent (if noted)
• Tools/skills used
• Surprises (anything unexpected)

Step 2: Pattern mining

Look across the batch for:

1. Recurring successes – patterns that appear in 3+ successful sessions
2. Recurring failures – anti-patterns that appear in 2+ sessions
3. Drift – skills or rules that sessions are working around (sign they need updating)
4. Gaps – situations where no skill existed and the session had to improvise

Step 3: Frequency analysis

For each pattern/anti-pattern, note:

• How many sessions it appeared in
• Whether it's already codified somewhere
• Whether the existing codification is accurate or stale

Step 4: Prioritized actions

Rank by impact (frequency x severity):

1. High-frequency anti-patterns --> add to skill anti-pattern lists or AGENTS.md
2. Validated new patterns --> add to skill methodology or create new skill
3. Stale rules --> update or remove from AGENTS.md
4. Gaps --> flag for new skill creation or skill expansion

Step 5: Execute and verify

For each action:

1. Make the change
2. Verify it doesn't conflict with existing rules (grep for contradictions)
3. Cross-reference: no phantom docs – every new artifact must be referenced from 2+ places, or it rots unread.

Deep Distillation Output

## Deep Distillation: {date range}

**Sessions reviewed:** {N}
**Breakdown:** {X success, Y partial, Z failure}

### Patterns Confirmed
| Pattern | Frequency | Codified in | Status |
|---|---|---|---|
| {pattern} | {N/total} | {artifact or "NEW"} | {kept / updated / created} |

### Anti-Patterns Found
| Anti-pattern | Frequency | Added to | Root cause |
|---|---|---|---|
| {anti-pattern} | {N/total} | {artifact} | {lock/fork/gap/stale} |

### Actions Taken
1. {specific change made}
2. ...

### Recommendations
- {any larger changes that need discussion before implementing}

Failure Retro Protocol

For a specific failure, drill into root cause:

Step 1: What happened?

Document the observable failure:

• What was the expected output?
• What was the actual output?
• When did things diverge?

Step 2: Classify the failure

Category	Description	Example
Lock as fork	Under-constrained a precision point	Codex chose the wrong DB column because the prompt said "use the relevant field"
Fork as lock	Over-constrained an exploration point	Prompt specified the exact algorithm, but a different approach was needed
Missing anti-pattern	Known failure mode not in the guard list	Codex fabricated a section because the "no fabrication" constraint was missing
Stale reference	Prompt referenced something that changed	File path moved, function renamed, API changed
Wrong tool	Used the wrong skill/mode/approach	Used a one-shot delegation for a task that needed interactive iteration
Missing context	Key information not in the prompt	Codex didn't know about a connection-pooler's prepared-statement limitations

Step 3: Fix the system, not the symptom

Don't just fix the specific failure – find the artifact that should have prevented it and update that artifact:

• If a constraint was missing --> add it to the relevant skill's anti-patterns
• If context was missing --> add it to AGENTS.md or a memory
• If the wrong tool was used --> update the decision framework in AGENTS.md
• If a reference was stale --> fix it AND add a freshness check to the relevant workflow

Failure Retro Output

## Failure Retro: {date} – {short title}

**What failed:** {1-2 sentences}
**Category:** {lock-as-fork / fork-as-lock / missing-anti-pattern / stale-reference / wrong-tool / missing-context}
**Root cause:** {why the system allowed this to happen}

**System fix:**
- {artifact updated} --> {what changed}

**Prevention:**
- {how the updated artifact would have prevented this failure}

Lock/Fork Framework Reference

From the SSD framing – the precision-exploration conflict:

• Locks = positions where there's one right answer. Need precision. Suppress the distractor tail.

  • File paths, schemas, DB details, conventions, naming, legal/regulatory citations
  • In prompts: be maximally specific, paste exact code, zero ambiguity

• Forks = positions where multiple valid approaches exist. Need exploration. Preserve diversity.

  • Architecture choices, algorithm selection, UX decisions, error handling strategy
  • In prompts: specify output format and constraints, but not the approach

The most common failure mode is misclassification – treating a lock as a fork (leaving precision points vague) or a fork as a lock (over-constraining exploration points).

Battle-Tested Lessons (de-identified)

These are generalized incident lessons. Each kept its principle; project/date/file specifics were stripped. Use them as priors when mining patterns and classifying failures.

• Tuning a shared frame knob to silence a one-axis warning trips the orthogonal-axis failure. Lowering or raising a single shared parameter to fix one dimension can break a perpendicular constraint on elongated/edge-case inputs. A parity test can pass at the bad value; only a real render/run catches it. Lesson: verify shared-parameter changes against the worst-case geometry, not the average case.
• Lowering parallelism to "add headroom" on an under-utilized connection regressed latency. The pipe was under-utilized, not contended – fewer concurrent requests made it slower, not faster. Lesson: measure the actual bottleneck before "adding safety margin"; intuition about contention is often backwards.
• A write-side validation gate does not protect read/cache/consumer paths. A gate added at the producer left every reader, cache-hydrator, and downstream consumer unguarded. Lesson: when you add a validation gate, gate the read path too, and sweep sibling call-sites in the same arc.
• An auth fix for one spawned child has a sibling for every other harness. Each CLI/tool authenticates differently (env token vs config file vs OAuth), so the same isolation layer breaks each one via a different mechanism. Lesson: when you fix auth for one spawned subprocess, audit every other spawned tool for its own variant.
• A field name can lie about the metric it holds. A field named for one metric (e.g. a "median" field) held a different value (e.g. a blended/derived value). Labeling or auditing off the name alone mislabels the output. Lesson: trace the producer before trusting a field name in a render or audit.
• Relabeling or value-fixing a shared component needs a per-caller value-semantics audit. Different callers feed different metrics through one header/component; a single relabel mislabels some of them. Lesson: enumerate every caller and check what each one actually passes.
• An engagement metric can crater with the click handler fully intact. The regression was above-the-fold reachability (the actionable element got pushed below new content), not a broken handler. Lesson: when a funnel metric drops but handlers test fine, measure scroll depth / reachability, not just handler wiring.
• Mount-fired analytics events race an async-loaded script and drop silently. The send resolves to undefined with no retry. Lesson: poll/queue for the global before firing fire-on-mount events; click-fired events are usually safe because the script has loaded by then.
• A pageview guard that suppresses all events on a path prefix eats genuine landings. Suppress same-pathname repeats only; suppressing everything on a prefix collapses real multi-page journeys into bounces. Lesson: scope dedup guards to the narrowest correct key.
• Mocked-DB tests give false green for data/ML code. They mask real-shape bugs that only appear against real rows. Lesson: run a real-data smoke test (on an isolated clone) before declaring data/ML work "fixed."
• Claiming "X works" requires reproducing X's exact runtime path – same command, env, and working directory, reading the producer's real output – not an isolated sample or a log skim. Lesson: verify in the real execution context; isolated reproductions overclaim.
• A standalone component can be dead while an inlined copy renders live. "Fixing" the standalone component changes nothing on screen. Lesson: grep for actual JSX/usage before editing a component you assume is rendered.
• A webhook handler is dead unless the provider endpoint subscribes to the event. Test-mode subscriptions don't carry to live. Lesson: treat "subscribe the endpoint to the event" as an explicit deploy step, and re-check it after any environment cutover.

When NOT to expand

Scope-creep smell test: if a retro action balloons into a multi-doc, multi-phase, multi-session program, stop and ask whether that scope was actually warranted or whether the template bled. Prefer deletion/consolidation/narrowing over new abstractions. A 200-line change with a 50-line alternative should be the 50-line one.

PLANS.md and AGENTS.md

• AGENTS.md is the cross-agent convention file many agents read for project conventions. Convention changes and durable cross-skill rules that aren't skill-internal belong there (see AGENTS.md for project conventions). Keep its rule-of-record canonical: state a shared rule once, reference it elsewhere, don't duplicate it.
• PLANS.md (or your equivalent plan/roadmap doc) is where multi-session work and open follow-ups live. When a deep distillation surfaces a "larger change that needs discussion before implementing," record it there rather than half-implementing it inside the retro. Retros codify learnings; PLANS.md tracks pending work.

Related Skills

• codex-prompt – generates Codex task prompts (retro reviews their effectiveness)
• gate-engineering – turns recurring defect classes into proven checks
• verify-honestly – supplies evidence vocabulary for outcomes
• session-close – creates durable session handoff that retros can mine