
Skill
shadow-frog-meditate
clean and consolidate shadow knowledge base
Description
Clean and consolidate the shadow knowledge base. Scans all shadow files for duplicate discoveries (same claim, different wording), near-duplicates (one extends another), and conflicting entries (contradicting claims). Merges duplicates, resolves conflicts by investigating the code, and asks the user only when resolution is unclear. Invoke periodically to keep the shadow focused and free of noise.
SKILL.md
ShadowFrog Meditate
Shadow hygiene — deduplicate, merge, and resolve conflicts across the
entire .shadow/ knowledge base. Prerequisite: .shadow/ exists with
discoveries.
Why Meditate?
Over time, shadows accumulate noise:
- Duplicates: the same insight written differently by different sessions
- Near-duplicates: one discovery is a subset of another
- Conflicts: two discoveries contradict each other (code may have changed, or one was wrong)
- Cross-scope duplicates: a per-file discovery and a
_cross/entry saying the same thing
This noise confuses downstream agents and dilutes signal. Meditate cleans it up.
Phase 1: Scan
Use parallel subagents to scan the shadow. Each subagent handles a batch of shadow files.
Scope Optimization
Not every file needs scanning. To reduce cost:
- Skip files with 0-1 discoveries — they can't have internal duplicates
- Focus on files modified since last meditate — check
_meta/state.jsonlast_update_atagainst file modification times - Always scan files with 5+ discoveries — highest duplicate risk
For the first meditate after a large dream run, most files will need scanning. For incremental meditation after small updates, this can reduce scope by 80%+.
Per-File Scan
For each per-file shadow (e.g., src/auth.py.md):
- Read all discoveries under each
## symbolheading - For each pair of discoveries under the same symbol, classify:
- Duplicate: same behavioral claim, different wording
- Near-duplicate: one discovery is a subset/refinement of the other
- Conflict: the two discoveries make contradicting claims
- Distinct: genuinely different insights — no action needed
- Record each finding as a structured action (see below)
Scan Output Format
Subagents must output findings as one JSON object per line so the orchestrator can auto-apply resolutions. This is critical for automation — prose recommendations require manual interpretation.
{"action": "merge", "file": "src/auth.py.md", "symbol": "authenticate_user", "keep": "- silently returns None on expired tokens...", "remove": "- returns None when token expires...", "merged": "- authenticate_user() silently returns None on expired tokens instead of raising. 3 of 7 callers don't check.\n _(verified, source: exploration)_", "reason": "duplicate: same claim, different wording"}
{"action": "merge", "file": "src/db.py.md", "symbol": "connect", "keep": "- connection pool exhaustion...", "remove": "- pool runs out...", "merged": "...", "reason": "near-duplicate: first extends second"}
{"action": "conflict", "file": "src/auth.py.md", "symbol": "validate_token", "entry_a": "- raises ValueError...", "entry_b": "- returns False...", "resolution": "verified_a", "reason": "code inspection: line 42 raises ValueError"}
{"action": "conflict", "file": "src/cache.py.md", "symbol": "invalidate", "entry_a": "...", "entry_b": "...", "resolution": "escalate", "reason": "both claims have evidence, needs user input"}
{"action": "move_to_cross", "file": "src/auth.py.md", "symbol": "validate_token", "entry": "- all validators share...", "cross_slug": "shared-validation-pattern", "reason": "cross-scope: involves 4 files"}
Fields:
action:merge|conflict|move_to_cross|move_from_crossfile: shadow file path relative to.shadow/symbol: the##/###heading the discovery lives underkeep: the discovery text to keep (for merge)remove: the discovery text to delete (for merge)merged: the final merged text (for merge)resolution:verified_a|verified_b|escalate(for conflict)reason: human-readable explanation
The orchestrator collects all lines, applies merge and conflict
actions automatically, and presents escalate items to the user.
Cross-Scope Scan
After per-file scanning:
- Collect all per-file discoveries into a flat list
- For each
_cross/*.mddiscovery, check if any per-file discovery makes the same or overlapping claim - For each
_prefs.mdpreference, check if any per-file discovery or_cross/entry duplicates it - Record cross-scope findings the same way
Scanning Guidelines
- Compare claims semantically, not just textually. "Returns None on expired tokens" and "Silently returns None when token expires" are duplicates.
- Two discoveries about the same function but covering different behaviors are distinct, not duplicates. E.g., "returns None on expired tokens" vs "uses constant-time comparison" — these are unrelated observations about the same function.
- Pay attention to
Also involves:— two discoveries with overlappingAlso involves:refs are more likely related.
Phase 2: Resolve
Process each finding by type.
Duplicates → Merge
Combine into a single discovery:
- Keep the richer wording (more detail, more context)
- Keep the stronger trust:
source: user>source: interaction>source: exploration - Keep the stronger status:
verified>uncertain>refuted - Merge
Also involves:refs (union of both) - Delete the weaker entry
Example:
BEFORE (two entries under same symbol):
- authenticate_user() returns None on expired tokens.
_(verified, source: exploration)_
- When the token is expired, authenticate_user silently returns None
instead of raising. 3 of 7 callers don't check.
_(verified, source: exploration)_
AFTER (merged):
- authenticate_user() silently returns None on expired tokens instead
of raising. 3 of 7 callers don't check the return value.
_(verified, source: exploration)_
Near-Duplicates → Absorb
The broader discovery absorbs the narrower one:
- Expand the broader entry to include any extra detail from the narrower
- Delete the narrower entry
- Preserve the stronger trust/status between the two
Conflicts → Investigate
When two discoveries contradict each other:
- Read the actual source code at the
file::symbollocation - Trace the logic to determine which claim is correct
- If needed, write and run a short test script to verify
- Mark the correct claim
verified, the incorrect onerefuted - If the incorrect claim was once true but code changed, update it
to reflect the current behavior and mark it
verified
If investigation takes more than a few minutes without resolution:
- Keep both discoveries
- Add a note:
(conflict unresolved — needs user input) - Ask the user to clarify at the end of the meditate session
Cross-Scope Duplicates → Place Correctly
When the same discovery exists in both a per-file shadow and _cross/:
- If it involves 3+ files → keep in
_cross/, remove from per-file - If it involves 1-2 files → keep in per-file, remove from
_cross/ - Update cross-references in both directions after moving
When a per-file discovery duplicates a _prefs.md entry:
- If it's truly project-wide (not tied to a specific symbol) → keep
in
_prefs.md, remove from per-file - If it's specific to that symbol but happens to match a pref → keep both (they serve different purposes)
Phase 3: Report
After all resolutions, print a summary:
Meditate Summary
================
Files scanned: 42
Duplicates merged: 7
Near-dupes absorbed: 3
Conflicts resolved: 2
Conflicts escalated: 1
Cross-scope fixed: 2
Total entries removed: 12
Escalated (needs your input):
src/auth.py::validate_token
- "raises ValueError on invalid format" vs "returns False on invalid format"
Both claims have evidence. Which behavior is correct?
Parallelism
For large shadows (20+ files), use parallel subagents:
- Partition shadow files into batches of ~10 files each
- Launch one subagent per batch for Phase 1 (scan)
- Each subagent outputs JSON-per-line findings (see Scan Output Format)
- Orchestrator collects all JSON lines from all subagents
- Auto-apply
mergeactions: useedittool withremoveasold_str, replacekeepwithmerged - Auto-apply
conflictactions whereresolutionisverified_aorverified_b— mark the loserrefuted - Collect
escalateitems for user review - Run Phase 3 (report)
For smaller shadows, run everything in a single pass — the scan output format is still useful for traceability.
Dream Archive Hygiene (_dreams/)
Meditate performs consistency checks and field repair on _dreams/. It
does NOT delete or rewrite report prose (those are historical records),
but it DOES fix missing/wrong metadata in the index.
Structural Checks
- Index consistency — verify every folder in
_dreams/has a row in_dreams/_index.md, and every row in the index has a matching folder. Fix mismatches (add missing rows, remove orphaned rows). - Report completeness — each
_dreams/<id>/should contain at minimum areport.md. Flag any empty directories. - Stale patches — if
base_commitin a report's frontmatter is more than 100 commits behind current HEAD, add a note to the index:⚠️ patch may not apply cleanly. Check with:git rev-list <base_commit>..HEAD --count 2>/dev/null - Cross-reference integrity — if a per-file discovery has a
Dream report: _dreams/<id>/reference, verify that dream folder exists. Remove dangling references.
Index Field Repair (run after Structural Checks)
Dream subagents sometimes write incomplete index rows (e.g., unknown
category/verdict, generic titles). meditate-repair.py (below)
auto-resolves these by reading each experiment's report.md frontmatter,
manifest.json, and verdict-section signals.
Do NOT hand-edit 50+ rows. Use the script. The agent's job is to surface what the script CAN'T auto-fix:
- Corrupted reports — when
report.md'sdream_id(frontmatter or body) doesn't match the folder name, the report was copy-pasted from another experiment. The script prints these to stderr and skips them. Do NOT auto-fix corruption — the content is wrong, not just the ID. Log them in the meditate summary for user review. - Ambiguous parent branches — when no
manifest.jsonexists for an experiment, the script falls back to slug heuristics (-extend,-fix,-deeper,-improve,-integration,-cleanup,-metrics,-remainingstrongly suggest compounding from a sibling). If the heuristic match is not high-confidence, flag for user review rather than guessing.
Applying Index Repairs
SKILL_DIR=""
for DIR in .github/skills/shadow-frog-meditate \
.claude/skills/shadow-frog-meditate; do
[ -d "$DIR" ] && SKILL_DIR="$DIR" && break
done
if [ -n "$SKILL_DIR" ] && [ -x "$SKILL_DIR/meditate-repair.py" ]; then
python3 "$SKILL_DIR/meditate-repair.py"
else
echo "meditate-repair.py not found; falling back to manual scan." >&2
fi
What it does:
- Backs up
.shadow/_dreams/_index.mdto.bakfirst - Detects corrupted reports (frontmatter
dream_id!= folder name) and prints them to stderr — these rows are skipped, you resolve manually - For every other row with
unknown/empty category/verdict/title, resolves the canonical value from the report's frontmatter, manifest, or verdict section signals - Title repair replaces generic forms (raw slug, "Dream Report:
", "Dream t##: ") with the first # H1or## Summaryline
Verdict detection order is manifest > VERDICT_SECTION signals > whole-body signals. Dead-end signals are checked BEFORE useful signals
so not useful doesn't match useful.
Idempotent — safe to rerun until output reports 0 repaired.
Do NOT delete dream reports during meditate — only the user decides what to keep or discard (via Phase 7 review or manual cleanup).
Rules
- Never delete a
source: userdiscovery without asking — user knowledge is the highest trust. If it conflicts withsource: exploration, investigate thoroughly before concluding the user was wrong. - Preserve
Also involves:refs — when merging, take the union. - Update
_index.mdafter removing entries (discovery counts change). - Update
_meta/state.json— setlast_update_type: "meditate". - Don't touch
_prefs.mdplacement unless a pref is clearly duplicated verbatim in a per-file shadow.
Format Compliance
Every merged or rewritten discovery must exactly follow the canonical
format in /shadow-frog (Discovery Format, Cross-Cutting, Preferences).
Re-read both the original entries and the spec before writing — a
malformed discovery is worse than a duplicate; it breaks the viewer
parser and downstream agents.
Meditate-specific rules:
- When merging, take the union of
labels: [...]from both entries. - When merging, preserve every
Also involves: file::symbolfrom both entries (union, not intersection). Dream report: _dreams/<id>/references must survive the merge — re-attach to the merged entry if either original had one.- Status (
verified/uncertain/refuted) is taken from the stronger source:user≻interaction≻verifiedexploration ≻uncertain. - Discovery text stays behavioral, not a code summary — preserve the more behavioral wording when entries differ in style.
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