distill

name	distill
description	One-time snapshot extracting patterns from work history and accumulated lessons, distills into concrete improvements — new agent/skill suggestions, memory pruning, consolidating lessons into rules/agent updates, or performing bin/ extraction from /audit --efficiency candidates. Roster boundary analysis → /foundry:audit agents (Check 34).
argument-hint	[prune | memory | executables [<run-dir-or-report-path>] | "external <url-or-path>" | "<recurring task description>"] [--project] [--eager] [--keep "<items>"]
disable-model-invocation	true
allowed-tools	Read, Edit, Bash, Glob, Grep, Write, AskUserQuestion, Agent, WebFetch, TaskCreate, TaskUpdate, TaskList
effort	low

Analyze how Claude Code is used and surface concrete improvements — new agents/skills to reduce repetition, or consolidate lessons into governance files (rules, agent instructions, skill updates) — without duplicating what exists.

NOT for single-file edits or quality checks — use /foundry:audit for config quality checks. NOT for audit-only scan for extraction candidates (use /foundry:audit --efficiency instead of distill executables for detection-only).

• $ARGUMENTS: optional. Modes:
  • Omitted — analyze existing patterns and agents; generate suggestions proactively.
  • prune [--eager] — evaluate project memory file for stale, redundant, or verbose entries. Default: advisory diff + apply prompt. --eager: score every entry (Usage likelihood × Impact → Tier P0/P1/P2), print full scored table with # column, let user select by tier or item numbers, delegate edits to foundry:curator.
  • memory [--eager] — read .notes/lessons.md and memory feedback files, distill recurring patterns into proposed rule files, agent instruction updates, and skill workflow changes. --eager: include Pattern count, Strength, and Tier columns in proposal table; let user select clusters to promote by tier or item numbers; delegate writes to foundry:curator.
  • external <source> [--eager] — analyse external plugin, skill, or agentic resource and produce structured adoption proposal. <source> is URL, file path, or local directory. --eager: lower adoption bar — recommend partial adoption even for single useful components.
  • executables [--eager] [<run-dir-or-report-path>] — perform bin/ extraction from /foundry:audit --efficiency Check 33 candidates. Auto-detects latest run dir under .reports/audit/; pass optional path to target a specific run dir or report file. Runs inline Check 33 scan when no report exists. Default gates on HIGH/MEDIUM verdict. --eager: also surface LOW verdict clusters as extraction candidates. Spawns foundry:sw-engineer per cluster. Skip to Mode: Executables Extraction below.
  • [--eager] <recurring task description> — use description as context when generating suggestions. --eager: lower frequency threshold from 3+ to 2+ occurrences; single high-effort occurrence also qualifies.
  • --project — in prune and memory modes, show an interactive project picker: enumerate all slugs under ~/.claude/projects/*/memory/ with MEMORY.md size in tokens, then let user select which project(s) to operate on. Omit to operate across all projects automatically. Has no effect on other modes.

Key boundary 1: end of Step 2 frequency heuristics (default mode only — prune/memory/external/executables exit early), before Step 3 gap analysis. Preserve at boundary 1: EAGER flag, ARGUMENTS (stripped), no run-dir (default mode is stateless). Terminal paths: end of Step 5 report (default mode); end of Memory Pruning mode (both eager and standard branches).

Task hygiene: load and follow the protocol below.

# loads: compaction-contract.md
# audit-skip: resilience-replication
_FS=$(python "${CLAUDE_PLUGIN_ROOT:-plugins/cc_foundry}/bin/resolve_shared_path.py" foundry skills/_shared 2>/dev/null || echo "plugins/cc_foundry/skills/_shared")  # timeout: 5000
cat "$_FS/task-hygiene.md"

export CSID="${CLAUDE_CODE_SESSION_ID:-$PPID}"
KEEP_ITEMS=""
if [[ "$ARGUMENTS" =~ --keep[[:space:]]\"([^\"]+)\" ]]; then
    KEEP_ITEMS="${BASH_REMATCH[1]}"
fi
ARGUMENTS=$(echo "$ARGUMENTS" | sed 's/--keep "[^"]*"//g')
rm -f .temp/state/skill-contract.md  # clear stale contract (compaction-contract.md §Lifecycle)  # timeout: 5000
mkdir -p "${TMPDIR:-/tmp}/distill-state-${CSID}"
echo "$KEEP_ITEMS" > "${TMPDIR:-/tmp}/distill-state-${CSID}/keep-items"
EAGER=false
[[ "$ARGUMENTS" == *"--eager"* ]] && EAGER=true
ARGUMENTS=$(echo "$ARGUMENTS" | sed 's/--eager//g' | xargs)  # timeout: 3000
echo "EAGER=$EAGER"  # shell vars don't persist across Bash calls — read from stdout
echo "ARGUMENTS_STRIPPED=$ARGUMENTS"

Note: EAGER and stripped ARGUMENTS are set by this Bash block, but shell variable state does not persist across separate Bash() tool calls. After this block runs, read its stdout (EAGER=true/false, ARGUMENTS_STRIPPED=...) and carry those values as model-context references for all subsequent mode dispatch and threshold decisions. Do not rely on $EAGER as a live shell variable in later steps — substitute the literal boolean value read from stdout.

PROJECT_FLAG=false
if echo "$ARGUMENTS" | grep -qE -- "--project"; then
    PROJECT_FLAG=true
    ARGUMENTS=$(echo "$ARGUMENTS" | sed 's/--project//' | xargs)
fi
echo "PROJECT_FLAG=$PROJECT_FLAG"
echo "ARGUMENTS_FINAL=$ARGUMENTS"

Note: PROJECT_FLAG does not persist across Bash calls. Read its value from the stdout line PROJECT_FLAG=true/false and carry as model-context reference. When true, the mode must run the interactive picker before operating.

Step 1: Inventory existing agents and skills

Use Glob tool to enumerate agents and skills across all sources — project-local AND plugin-namespaced — to avoid false-gap findings when candidate already exists in plugin:

• Project-local: pattern agents/*.md, path .claude/; pattern skills/*/SKILL.md, path .claude/
• Plugin source (workspace): pattern */agents/*.md, path plugins/; pattern */skills/*/SKILL.md, path plugins/
• Installed plugin cache (if accessible): resolve cache root — PLUGIN_CACHE="${CLAUDE_PLUGIN_ROOT:-plugins/cc_foundry}" — then use Glob tool on $PLUGIN_CACHE for pattern */agents/*.md and */skills/*/SKILL.md

For each agent/skill found, extract: name, description, tools, purpose. Tag each entry with plugin namespace (e.g. foundry:sw-engineer, oss:resolve) — used in Step 3 gap analysis to prevent recommending duplicates of plugin-namespaced agents/skills.

Step 2: Analyze work patterns

Mode-token normalization — all mode dispatches below compare against the first whitespace-delimited token of the stripped ARGUMENTS (after --eager removal). Use this single rule consistently; do not rely on exact equality of the full $ARGUMENTS string, since trailing flags/spaces from prior parsing may differ.

If first token equals executables (i.e. executables alone or executables <path>, NOT a path or word that merely starts with the string executables): skip Steps 2–5 entirely and go to "Mode: Executables Extraction" below.

If first token equals prune: skip Steps 2–5 entirely and go to "Mode: Memory Pruning" below.

If first token equals memory: skip Steps 2–5 entirely and go to "Mode: Memory Distillation" below.

If first token equals external (i.e. external <source>, NOT a word that merely starts with the string external): skip Steps 2–5 entirely and go to "Mode: External Distillation" below.

Otherwise, look for signals of repetitive or specialist work. First three git commands are independent — run in parallel:

# timeout: 3000
# --- run these three in parallel ---
git log --oneline -50

git log --name-only --pretty="" -30 | sort | uniq -c | sort -rn | head -20

git log --oneline -100 | cut -d' ' -f2 | sort | uniq -c | sort -rn | head -15

Then use Glob tool (pattern todo_*.md, path .plans/active/) to list active task files; read each with Read tool. Also read .notes/lessons.md (if exists) for task history and conversation hints.

If $ARGUMENTS provided, use as additional context for pattern analysis.

Frequency Heuristics

• 3+ occurrences of pattern in recent history → candidate for automation
• 2+ different projects using same manual process → cross-project skill
• significant manual effort per occurrence (subjective — use git history context) → high-value automation target
• Domain-specific knowledge required → candidate for specialist agent (not just skill)

With --eager (lower thresholds):

• 2+ occurrences → candidate for automation
• 1 occurrence with significant manual effort → qualifies as high-value candidate
• Domain-specific threshold unchanged

export CSID="${CLAUDE_CODE_SESSION_ID:-$PPID}"
IFS= read -r _KEEP < "${TMPDIR:-/tmp}/distill-state-${CSID}/keep-items" 2>/dev/null || _KEEP=""
_PRESERVE="run-dir=n/a"
[ -n "$_KEEP" ] && _PRESERVE="$_PRESERVE; user-keep: $_KEEP"
mkdir -p .temp/state  # timeout: 5000
{
    echo "## Active Skill Contract"
    echo "- skill: foundry:distill · phase: gap-analysis (after work-pattern scan)"
    echo "- run-dir: n/a"
    echo "- preserve: $_PRESERVE"
    echo "- next: gap analysis (Step 3) → duplication check (Step 4) → report (Step 5)"
} > .temp/state/skill-contract.md

Step 3: Gap analysis

For each identified pattern, check:

1. Already covered? — search existing agent/skill descriptions for overlap
2. Frequent enough? — recurring ≥ 3 times or clearly domain-specialized (See Step 2 heuristics — combine ≥3 occurrences with effort/frequency signals from Steps 1–2)
3. Would specialist add quality? — does it require deep domain knowledge?
4. Too narrow? — single-use task doesn't warrant persistent agent

Thresholds for recommendation:

• New agent: recurring specialist role, complex decision-making, 5+ distinct capabilities
• New skill: workflow orchestration, multi-step process with fixed structure
• No new file needed: one-off or already covered by existing agent

Step 4: Check for duplication

Before recommending anything, run overlap check and anti-pattern checklist:

For each candidate agent/skill:
- Does any existing agent cover >50% of its scope? → enhance existing instead
  (with --eager: lower to >30%; any shared single named capability → flag as boundary issue)
- Is the name/description confusingly similar to an existing one? → rename existing

Anti-pattern checklist — reject candidate if any apply:

1. Role vs task confusion: agents are roles, not tasks. Do not create agent for every different topic.
2. Near-duplicate: candidate duplicates existing agent with slightly different name. Enhance existing instead.
3. Thin wrapper: candidate skill just calls one agent with fixed args. Not enough value to justify new skill file. Exception: skills that add measure-first/measure-after bookends, multi-mode dispatch across 3+ agents, or safety breaks (retry limits, validation gates) justify wrapper even if only one agent executes for given invocation.

Step 5: Report

## Agent/Skill Suggestions

### Existing Coverage (no gaps found)
- [agent/skill]: covers [pattern] well — no new file needed

### Recommend: New Agent — [name]
**Trigger**: [what recurring pattern or gap justifies this]
**Gap**: [what existing agents don't cover]
**Scope**: [what it would do — 3-5 bullet points]
**Suggested tools**: [Read, Write, Edit, Bash, etc.]
**Draft description**: "[one-line description for frontmatter]"

### Recommend: New Skill — [name]
**Trigger**: [what repetitive workflow justifies this]
**Gap**: [why existing skills don't cover it]
**Scope**: [what workflow steps it would orchestrate]
**Draft description**: "[one-line description for frontmatter]"

### Recommend: Enhance Existing — [agent/skill name]
**Add**: [specific capability missing from current version]
**Why**: [what recurring task would benefit]

### No Action Needed
[pattern]: already handled by [existing agent/skill]

## Confidence
**Score**: [0.N]
**Gaps**: [e.g., git history too shallow, task files not present, descriptions too generic to compare]

**Refinements**: N passes. [Pass 1: <what improved>. Pass 2: <what improved>.] — omit if 0 passes

rm -f .temp/state/skill-contract.md  # clear contract — skill complete (compaction-contract.md §Lifecycle)  # timeout: 5000

Mode: Memory Pruning — only when $ARGUMENTS == "prune"

Locate, evaluate, and trim project memory file.

Find memory file:

# timeout: 5000
FOUND=$(find "$HOME/.claude/projects" -maxdepth 3 -name "MEMORY.md" -path "*/memory/MEMORY.md" 2>/dev/null | sort)
if [ -z "$FOUND" ]; then
    echo "PRUNE_ABORT"
    echo "PRUNE_ABORT_REASON: no memory files found under ~/.claude/projects/"
else
    echo "PRUNE_FOUND"
    echo "$FOUND" | while IFS= read -r f; do
        slug=$(echo "$f" | sed 's|.*/projects/||;s|/memory/MEMORY.md||')
        tokens=$(( $(wc -c < "$f" 2>/dev/null || echo 0) / 4 ))
        echo "PRUNE_ENTRY: $slug | ${tokens}k tokens | $f"
    done
fi

Short-circuit: After the block runs, scan for PRUNE_ABORT (exact-line match). If present, stop prune mode and end with Confidence block. Otherwise, collect all PRUNE_ENTRY: lines — each has format <slug> | <N>k tokens | <path>.

If PROJECT_FLAG == true (interactive picker): call AskUserQuestion with multiSelect: true. Build options from PRUNE_ENTRY lines — label = <slug> (tokens=<N>k), description = prune this project's memory. Max 4 options: if more than 4 projects found, take the 4 largest by token count and note in the question text that remaining projects were omitted (user can re-run). Always add a final option with label Skip and description exit without changes. Checked slugs → extract matching <path> fields as the working set.

If PROJECT_FLAG == false: use all <path> fields from PRUNE_ENTRY lines as the working set.

Parallel analysis across projects — for working sets with 2+ files, spawn one analysis agent per project simultaneously. For single-file working sets, run P1–P2 inline (no spawn).

Spawn one Agent per project with model="sonnet" (mechanical Drop/Trim/Keep classification — no reasoning tier needed; no schema — returns text analysis):

Read MEMORY.md at <absolute-path>.
Also read .claude/CLAUDE.md in the current working directory (limit=40) to identify overlap — anything already there need not live in memory.
Evaluate every section using these criteria:
  Drop: stale/no-longer-accurate, fully duplicated in CLAUDE.md, resolved one-time issues
  Trim: accurate but contains rationale/history no longer needed day-to-day; keep operational facts only
  Keep: rules applied every session, project-specific facts absent from CLAUDE.md
Return structured analysis (no prose):
PROJECT: <slug>
DROP: <section-name> — <one-line reason>
TRIM: <section-name> — <what to keep vs remove>
KEEP: <section-name>
CONFIDENCE: 0.N

Wait for all agents to complete. Merge into consolidated proposal list keyed by slug, labeling each section with its project slug.

If $EAGER == true — skip P1–P3 below; execute P-eager steps:

P-eager-1: Spawn one scoring agent per project in parallel with model="sonnet" (structured two-dimension scoring — no reasoning tier needed); working sets with 2+ files; inline for single file. Each agent scores every section in its assigned MEMORY.md:

Read MEMORY.md at <absolute-path>.
Score every section on two dimensions:
  Usage likelihood: High (every session) | Moderate (occasional) | Low (rare/one-off)
  Impact if missing: High (wrong behavior) | Moderate (degraded output) | Low (no effect)
  Tier: P0=keep (High×High or High×Moderate) | P1=trim (Moderate×Moderate or mixed) | P2=drop (Low on either)
  Action: if content belongs in rules/*.md or agent file → "→ rule"
Return ONLY (one TSV line per section, no prose):
PROJECT:<slug>  #:<n>  Section:<name>  Usage:<tier>  Impact:<tier>  Tier:P<n>  Action:<keep|drop|trim|→ rule>

Collect responses from all projects. Assign sequential # IDs across all projects. Print one consolidated scored table (all projects, # as primary sort):

• Usage likelihood: High = needed every session · Moderate = occasional · Low = rare/one-off
• Impact if missing: High = wrong behavior without it · Moderate = degraded output · Low = no effect
• Tier (derived): P0 = keep · P1 = trim candidate · P2 = drop/convert candidate
  • P0: High×High or High×Moderate
  • P1: any Moderate×Moderate or mixed High/Low signal
  • P2: Low usage OR Low impact (especially both)
• Action: entries whose content could live in rules/*.md or an agent file → mark → rule in Action column

| #  | Project | Section | Usage likelihood | Impact if missing | Tier | Action      |
|----|---------|---------|-----------------|-------------------|------|-------------|
| 1  | slug-a  | ...     | High            | High              | P0   | Keep        |
| 2  | slug-b  | ...     | Low             | Low               | P2   | Drop        |
| 3  | slug-a  | ...     | Moderate        | High              | P1   | Trim        |
| 4  | slug-b  | ...     | Low             | High              | P2   | → rule      |

Legend:
  Usage likelihood — High: every session · Moderate: occasional · Low: rare/one-off
  Impact if missing — High: wrong behavior · Moderate: degraded · Low: no effect
  Tier — P0: keep · P1: trim candidate · P2: drop/convert candidate
  Action — "→ rule" entries can be promoted to rules/*.md then dropped from memory

P-eager-2: Call AskUserQuestion tool — do NOT write question as plain text:

• question: "Which entries to prune? Select tier or type item numbers (e.g. 2, 4, 7)."
• (a) label: All P2 — description: drop all tier-P2 entries; apply → rule conversions as proposals
• (b) label: All P1 + P2 — description: trim P1 entries and drop P2 entries
• (c) label: Specific items — description: enter item numbers in next message; applies only those
• (d) label: Skip — description: leave MEMORY.md untouched; user edits manually

If user picks (c): print "Enter item numbers (e.g. 2, 4, 7):" and wait for next message; resolve item numbers against # column before proceeding.

P-eager-3: Spawn one foundry:curator agent per project in parallel. Group selected # items by project slug; each agent receives only the items for its project. Substitute absolute memory file path inline before issuing each Agent call:

Read MEMORY.md at <absolute-path>.
Apply these prune actions (sections identified by # from scored table — only the items for this project):
  <list: # — section name — action (Drop | Trim | Convert to rule)>
Rules:
- Drop: remove entire section including heading
- Trim: keep operational directive only (1 line max per entry); remove rationale/backstory
- Convert to rule: remove section from MEMORY.md; print proposed rule file content inline in response for user review before writing — do NOT write the rule file
Write MEMORY.md changes using the Edit tool.
Return ONLY: {"status":"done","project":"<slug>","sections_dropped":N,"sections_trimmed":N,"rule_conversions":N,"confidence":0.N}

Wait for all curator agents to complete. Collect results. Print consolidated summary:

Pruned MEMORY.md — <date>
  Projects processed: N
  Dropped: N sections total — [project: names, ...]
  Trimmed: N sections total — [project: names, ...]
  Rule conversions proposed: N — [project: names, ...] (review and write manually or via /manage)
  Kept:    N sections unchanged
  Saved:   ~N lines total

End response with ## Confidence block per CLAUDE.md output standards.

rm -f .temp/state/skill-contract.md  # clear contract — skill complete (compaction-contract.md §Lifecycle)  # timeout: 5000

Otherwise ($EAGER == false) — standard read-only advisory flow:

Evaluate each section against these criteria:

• Drop: content no longer accurate (removed features, resolved one-time issues, superseded decisions), or fully duplicated in CLAUDE.md
• Trim: sections still accurate but containing implementation history or rationale no longer needed day-to-day — keep operational facts (what/where), drop why-it-was-built backstory
• Keep: rules actively applied every session; project-specific facts absent from CLAUDE.md; anything model needs to act correctly

Memory-write gate — project CLAUDE.md Memory Policy prohibits auto-writes to MEMORY.md. Prune mode runs read-only by default and produces advisory diff/report rather than applying edits silently:

P1: Read all memory files (parallel for 2+ files). Analyse each for stale, redundant, and verbose entries.

P2: Print consolidated proposed prune report across all projects:

Prune proposals (apply manually unless explicitly approved below):

[Project: <slug>]
  Drop  — <section name>: <reason>
  Trim  — <section name>: <what to remove vs keep>

[Project: <slug>]
  ...

P3: Call AskUserQuestion — do NOT write question as plain text. Map options directly into tool call:

• question: "Apply prune edits across all N project memory files?"
• (a) label: Apply now — description: apply all proposals to all memory files in parallel
• (b) label: Show diff first — description: print line-by-line preview before applying any change
• (c) label: Skip — description: leave all MEMORY.md files untouched; user will edit manually

Only after user picks (a) (or (b) followed by approval) may Edit be invoked on memory files. Never apply prune edits silently. Apply edits to all projects in parallel using Edit tool (one project per call, concurrent).

Print consolidated summary after applying (or after user declines):

Pruned MEMORY.md — <date>
  Projects: N
  Dropped: N sections total — [project: names, ...]
  Trimmed: N sections total — [project: names, ...]
  Kept:    N sections unchanged
  Saved:   ~N lines total

End response with ## Confidence block per CLAUDE.md output standards.

rm -f .temp/state/skill-contract.md  # clear contract — skill complete (compaction-contract.md §Lifecycle)  # timeout: 5000

Mode: Memory Distillation — only when first token is memory

DISTILL_MODES=$(python "${CLAUDE_PLUGIN_ROOT:-plugins/cc_foundry}/bin/resolve_skill_subdir.py" distill modes 2>/dev/null || echo "plugins/cc_foundry/skills/distill/modes")  # timeout: 5000
cat "$DISTILL_MODES/memory.md"

Execute the mode loaded above.

Mode: External Distillation — only when $ARGUMENTS begins with external

DISTILL_MODES=$(python "${CLAUDE_PLUGIN_ROOT:-plugins/cc_foundry}/bin/resolve_skill_subdir.py" distill modes 2>/dev/null || echo "plugins/cc_foundry/skills/distill/modes")  # timeout: 5000
cat "$DISTILL_MODES/external.md"

Execute the mode loaded above.

Mode: Executables Extraction — only when $ARGUMENTS begins with executables

DISTILL_MODES=$(python "${CLAUDE_PLUGIN_ROOT:-plugins/cc_foundry}/bin/resolve_skill_subdir.py" distill modes 2>/dev/null || echo "plugins/cc_foundry/skills/distill/modes")  # timeout: 5000
cat "$DISTILL_MODES/executables.md"

Execute the mode loaded above.

• Skill is introspective: looks at tooling itself, not just code

• Invoke periodically (e.g., monthly) or after burst of correction/feedback; one-time snapshot, not continuous monitor

• Suggestions are proposals — review before creating new files

• After creating new agent/skill from suggestion, re-run skill once to confirm gap resolved, then stop

• memory mode is primary consolidation path — run after any session with significant corrections to prevent lesson drift into MEMORY.md noise

• Agent Teams signal tracking: when reviewing patterns, also look for:

  • Skills using --team or team-mode heuristics more/less than expected → flag over/under-use relative to decision matrix in CLAUDE.md § Agent Teams
  • Security findings in reviews for non-auth code → foundry:qa-specialist teammate scope too broad; narrow it
  • Model tier mismatches (e.g., heavy analysis assigned to sonnet teammates) → flag for tier adjustment

• external mode calibration: two concrete GT fixture cases defined in calibrate skills mode file — find via find "${CLAUDE_PLUGIN_ROOT:-plugins/cc_foundry}" -maxdepth 5 -path "*/calibrate/modes/skills.md" 2>/dev/null | head -1 with fallback to plugins/cc_foundry/skills/calibrate/modes/skills.md:

  • caveman plugin — narrow, self-contained communication mode, no local structural overlap → GT: install-as-is recommended, Group A empty or thin
  • Karpathy autoresearch — research automation tool, strong overlap with research: plugin structure → GT: Group A candidates map to research plugin, digest recommended, install-as-is not triggered
  • Ground truth = static snapshot of each tool's agent/skill/rule files (no live fetch needed); score adoption-table lane assignments against GT outcomes

• Follow-up chains:

  • Suggestion accepted for new agent/skill → /foundry:manage create to scaffold and register it
  • Suggestion to enhance existing → edit agent/skill directly, then /foundry:setup
  • memory proposals applied → /foundry:setup to propagate; /foundry:audit rules to verify new rule files structurally sound
  • executables extraction complete → /foundry:setup to propagate bin/ scripts; run /foundry:audit --efficiency to confirm clusters == 0