self-improvement-3
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.
What this skill does
# Self-Improvement Skill Log learnings and errors to markdown files for continuous improvement. Coding agents can later process these into fixes, and important learnings get promoted to project memory. ## Quick Reference | Situation | Action | |-----------|--------| | Command/operation fails | Log to `.learnings/ERRORS.md` | | User corrects you | Log to `.learnings/LEARNINGS.md` with category `correction` | | User wants missing feature | Log to `.learnings/FEATURE_REQUESTS.md` | | API/external tool fails | Log to `.learnings/ERRORS.md` with integration details | | Knowledge was outdated | Log to `.learnings/LEARNINGS.md` with category `knowledge_gap` | | Found better approach | Log to `.learnings/LEARNINGS.md` with category `best_practice` | | Similar to existing entry | Link with `**See Also**`, consider priority bump | | Broadly applicable learning | Promote to `CLAUDE.md`, `AGENTS.md`, and/or `.github/copilot-instructions.md` | ## Setup Create `.learnings/` directory in project root if it doesn't exist: ```bash mkdir -p .learnings ``` Copy templates from `assets/` or create files with headers. ## Logging Format ### Learning Entry Append to `.learnings/LEARNINGS.md`: ```markdown ## [LRN-YYYYMMDD-XXX] category **Logged**: ISO-8601 timestamp **Priority**: low | medium | high | critical **Status**: pending **Area**: frontend | backend | infra | tests | docs | config ### Summary One-line description of what was learned ### Details Full context: what happened, what was wrong, what's correct ### Suggested Action Specific fix or improvement to make ### Metadata - Source: conversation | error | user_feedback - Related Files: path/to/file.ext - Tags: tag1, tag2 - See Also: LRN-20250110-001 (if related to existing entry) --- ``` ### Error Entry Append to `.learnings/ERRORS.md`: ```markdown ## [ERR-YYYYMMDD-XXX] skill_or_command_name **Logged**: ISO-8601 timestamp **Priority**: high **Status**: pending **Area**: frontend | backend | infra | tests | docs | config ### Summary Brief description of what failed ### Error ``` Actual error message or output ``` ### Context - Command/operation attempted - Input or parameters used - Environment details if relevant ### Suggested Fix If identifiable, what might resolve this ### Metadata - Reproducible: yes | no | unknown - Related Files: path/to/file.ext - See Also: ERR-20250110-001 (if recurring) --- ``` ### Feature Request Entry Append to `.learnings/FEATURE_REQUESTS.md`: ```markdown ## [FEAT-YYYYMMDD-XXX] capability_name **Logged**: ISO-8601 timestamp **Priority**: medium **Status**: pending **Area**: frontend | backend | infra | tests | docs | config ### Requested Capability What the user wanted to do ### User Context Why they needed it, what problem they're solving ### Complexity Estimate simple | medium | complex ### Suggested Implementation How this could be built, what it might extend ### Metadata - Frequency: first_time | recurring - Related Features: existing_feature_name --- ``` ## ID Generation Format: `TYPE-YYYYMMDD-XXX` - TYPE: `LRN` (learning), `ERR` (error), `FEAT` (feature) - YYYYMMDD: Current date - XXX: Sequential number or random 3 chars (e.g., `001`, `A7B`) Examples: `LRN-20250115-001`, `ERR-20250115-A3F`, `FEAT-20250115-002` ## Resolving Entries When an issue is fixed, update the entry: 1. Change `**Status**: pending` → `**Status**: resolved` 2. Add resolution block after Metadata: ```markdown ### Resolution - **Resolved**: 2025-01-16T09:00:00Z - **Commit/PR**: abc123 or #42 - **Notes**: Brief description of what was done ``` Other status values: - `in_progress` - Actively being worked on - `wont_fix` - Decided not to address (add reason in Resolution notes) - `promoted` - Elevated to CLAUDE.md, AGENTS.md, or .github/copilot-instructions.md ## Promoting to Project Memory When a learning is broadly applicable (not a one-off fix), promote it to permanent project memory. ### When to Promote - Learning applies across multiple files/features - Knowledge any contributor (human or AI) should know - Prevents recurring mistakes - Documents project-specific conventions ### Promotion Targets | Target | What Belongs There | |--------|-------------------| | `CLAUDE.md` | Project facts, conventions, gotchas for all Claude interactions | | `AGENTS.md` | Agent-specific workflows, tool usage patterns, automation rules | | `.github/copilot-instructions.md` | Project context and conventions for GitHub Copilot | ### How to Promote 1. **Distill** the learning into a concise rule or fact 2. **Add** to appropriate section in target file (create file if needed) 3. **Update** original entry: - Change `**Status**: pending` → `**Status**: promoted` - Add `**Promoted**: CLAUDE.md`, `AGENTS.md`, or `.github/copilot-instructions.md` ### Promotion Examples **Learning** (verbose): > Project uses pnpm workspaces. Attempted `npm install` but failed. > Lock file is `pnpm-lock.yaml`. Must use `pnpm install`. **In CLAUDE.md** (concise): ```markdown ## Build & Dependencies - Package manager: pnpm (not npm) - use `pnpm install` ``` **Learning** (verbose): > When modifying API endpoints, must regenerate TypeScript client. > Forgetting this causes type mismatches at runtime. **In AGENTS.md** (actionable): ```markdown ## After API Changes 1. Regenerate client: `pnpm run generate:api` 2. Check for type errors: `pnpm tsc --noEmit` ``` ## Recurring Pattern Detection If logging something similar to an existing entry: 1. **Search first**: `grep -r "keyword" .learnings/` 2. **Link entries**: Add `**See Also**: ERR-20250110-001` in Metadata 3. **Bump priority** if issue keeps recurring 4. **Consider systemic fix**: Recurring issues often indicate: - Missing documentation (→ promote to CLAUDE.md or .github/copilot-instructions.md) - Missing automation (→ add to AGENTS.md) - Architectural problem (→ create tech debt ticket) ## Periodic Review Review `.learnings/` at natural breakpoints: ### When to Review - Before starting a new major task - After completing a feature - When working in an area with past learnings - Weekly during active development ### Quick Status Check ```bash # Count pending items grep -h "Status\*\*: pending" .learnings/*.md | wc -l # List pending high-priority items grep -B5 "Priority\*\*: high" .learnings/*.md | grep "^## \[" # Find learnings for a specific area grep -l "Area\*\*: backend" .learnings/*.md ``` ### Review Actions - Resolve fixed items - Promote applicable learnings - Link related entries - Escalate recurring issues ## Detection Triggers Automatically log when you notice: **Corrections** (→ learning with `correction` category): - "No, that's not right..." - "Actually, it should be..." - "You're wrong about..." - "That's outdated..." **Feature Requests** (→ feature request): - "Can you also..." - "I wish you could..." - "Is there a way to..." - "Why can't you..." **Knowledge Gaps** (→ learning with `knowledge_gap` category): - User provides information you didn't know - Documentation you referenced is outdated - API behavior differs from your understanding **Errors** (→ error entry): - Command returns non-zero exit code - Exception or stack trace - Unexpected output or behavior - Timeout or connection failure ## Priority Guidelines | Priority | When to Use | |----------|-------------| | `critical` | Blocks core functionality, data loss risk, security issue | | `high` | Significant impact, affects common workflows, recurring issue | | `medium` | Moderate impact, workaround exists | | `low` | Minor inconvenience, edge case, nice-to-have | ## Area Tags Use to filter learnings by codebase region: | Area | Scope | |------|-------| | `frontend` | UI, components, client-side code | | `backend` | API, services, server-side code | | `infra` | CI/CD, deployment, Docker, cloud | | `tests` | Test files, testing utilities, coverage | | `docs` | Documentation, comments, READMEs | | `config` | Configuration files, environment, setti
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.