Review candidate `[LEARN]` entries in `.claude/state/personal-memory.md` (gitignored) and run them through a five-critic council in parallel: generality, staleness, redundancy, evidence, format. Majority vote (3+ of 5) promotes the entry to MEMORY.md. Use when user says "promote memory", "review my learnings", "what should graduate to MEMORY.md", "five-critic council", or as monthly memory maintenance.
npx skills add https://github.com/pedrohcgs/claude-code-my-workflow --skill promote-memory
<!-- Pattern adapted with attribution from Chris Blattman's claudeblattman v2.1
"Five-critic council" (claudeblattman.com, Apr 2026 continuous-improvement
loop). Blattman uses it to decide what enters his MEMORY layer; we adapt
it to the personal-memory → MEMORY.md promotion question codified in
.claude/rules/meta-governance.md. -->
/promote-memory — five-critic council for memory promotionThe template's meta-governance.md rule splits memory into two tiers:
MEMORY.md (committed, ≤ 200 lines) — generic learnings that help all forkers..claude/state/personal-memory.md (gitignored, no size cap) — machine-specific and user-specific learnings.The rule says generic patterns should sync via git; personal patterns stay local. What it doesn't say is *who decides which is which*. /promote-memory operationalizes the call: spawn five critics in parallel, each reviewing the candidate [LEARN] entries on a single dimension, and promote on majority vote (3+ of 5).
/loop task. Wire /loop monthly /promote-memory all if you want automated proposal cadence (still requires user approval for each promotion).[LEARN] after a single correction. Just add it to personal-memory.md; let it sit until the next council runs./learn --revoke or manual edit. /promote-memory only promotes; it doesn't demote.Each critic runs in a forked context (Task with context=fork) — they don't see each other's verdicts or the user's draft. Each casts one YES/NO vote per candidate entry with a one-sentence rationale.
> "Would a non-econ forker benefit from this [LEARN] entry — a biology PhD, a sociology postdoc, a CS instructor? If the lesson is specific to *your* setup (your bibliography path, your machine's TeX install, your discipline's notation), vote NO."
> "Does this entry contradict the current state of the codebase? Run grep -r on the file paths, function names, or settings the entry references. If the referenced thing has been renamed, removed, or significantly changed, vote NO — the entry is stale and would mislead a future session."
> "Is this lesson already encoded in MEMORY.md, CLAUDE.md, or an existing rule? Read the relevant files. If yes (even paraphrased), vote NO — duplication erodes the index's signal."
> "Does the entry cite the incident, file path, or specific case that motivated it? If the entry is [LEARN:foo] always do X with no anchor to *why*, vote NO. Future Claude can't judge edge cases without the rationale."
> "Does the entry follow the schema in .claude/rules/meta-governance.md: [LEARN:category] wrong → right for corrections, structured Why: + How to apply: for feedback/project entries? If it's just a free-form note, vote NO — fix the format first, then re-submit."
Each critic returns YES/NO + rationale. The promotion threshold is majority (3+ YES).
If $ARGUMENTS is all, read every [LEARN:*] entry in .claude/state/personal-memory.md. Otherwise treat $ARGUMENTS as a substring filter (e.g., r-code matches all [LEARN:r-code] entries).
Five Task invocations in parallel, one per critic, each with context: fork:
Read / Grep the codebase. Should explicitly check any file paths / function names / settings the entry references.MEMORY.md + CLAUDE.md + relevant rule files..claude/rules/meta-governance.md for the schema reference.Use the Haiku tier for all five critics (per .claude/rules/model-routing.md: mechanical-ish review work). The user can override via the agent's model: field if they want Sonnet for the harder calls.
Collect verdicts. For each candidate entry, compute the vote count + per-critic verdicts.
For each entry:
## `[LEARN:foo] <summary>`
**Vote:** 4-of-5 YES (promote with note)
| Critic | Vote | Rationale |
|---|:---:|---|
| Generality | YES | ... |
| Staleness | YES | ... |
| Redundancy | YES | ... |
| Evidence | NO | Entry doesn't cite the originating incident. Add a one-line "Incident:" pointer before promoting. |
| Format | YES | ... |
**Recommendation:** Address Evidence critic, then promote.
**Proposed MEMORY.md addition:**
[LEARN:foo] <full proposed text>
The user reviews the report and explicitly approves which entries to promote. The skill writes approved entries to MEMORY.md, removes the same entries from personal-memory.md (or marks them with # promoted YYYY-MM-DD for audit), and surfaces a summary.
Do not auto-promote — even on 5-of-5 YES votes. The user's approval is the final gate.
[LEARN:category] section), personal-memory.md updated (entry marked promoted).quality_reports/memory_promotion_<date>.md audit file recording the full council session for forensics..claude/rules/meta-governance.md — the two-tier memory contract this skill operationalizes..claude/agents/promote-memory-council.md — the five-critic implementation (one agent file with five role specs, dispatched in parallel via Task)..claude/rules/model-routing.md — why critics default to Haiku tier./learn (existing skill) — captures new [LEARN] entries; pairs with /promote-memory (which decides what graduates).Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.
Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances
Generate Excalidraw diagrams from natural language descriptions. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", or "generate an Excalidraw file". Supports flowcharts, relationship diagrams, mind maps, and system architecture diagrams. Outputs .excalidraw JSON files that can be opened directly in Excalidraw.
Build and distribute Expo development clients locally or via TestFlight
Use when you have a written implementation plan to execute in a separate session with review checkpoints
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
Take pedrohcgs/promote-memory from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.