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growth-log

Use after a complex task, failure, or when reviewing what was learned. Teaches how to write growth logs that extract reusable patterns — not diary entries.

DeepseekModel 官方收录技能 质量 优秀 · 90 v1.0.0

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https://deepseekmodel.com/api/download.php?id=affaan-m-ecc-skills-growth-log-skill-md&format=skill
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name growth-log description Use after a complex task, failure, or when reviewing what was learned. Teaches how to write growth logs that extract reusable patterns — not diary entries. metadata {"version":"1.1.0","origin":"ECC"} Growth Log Skill The problem: Most people write "fixed a bug in X" as a learning log. That's a diary entry, not a learning artifact. A real growth log extracts the pattern so you recognize it next time. This skill teaches: How to write learning entries that compound across sessions. Works with any note-taking system — Markdown files, Notion, Obsidian, plain text. Templates are generic; adapt to your setup. When to Activate After completing a complex task (multi-file, new feature, architecture change) After a failure, mistake, or "that was harder than expected" moment When you want to review what you've learned over a period When NOT to activate: Trivial changes (typo fixes, single-line tweaks, config value changes with no debugging). The threshold: did this task involve debugging, redoing, rollback, or a non-obvious decision? If yes → write an entry. If no → skip. The Three Rules Rule 1: Failures > Achievements A failure is nutritionally denser than a success. One bug that took 2 hours to find teaches more than 3 features that worked first try. Bad: "Successfully implemented the login flow." Good (web dev): "Login flow: session token wasn't persisting because the cookie SameSite defaulted to Lax in Chrome 128+. Pattern: always explicitly set SameSite=None; Secure when cross-origin. Signal to recognize: auth breaks after browser upgrade or when crossing origin boundaries." Good (data pipeline): "CSV import failed silently on empty rows because pandas.read_csv(dropna=False) keeps zero-width rows that len() counts as valid. Pattern: always df.dropna(how='all', inplace=True) before row-count validation." Rule 2: The Bole Principle (伯乐原则) Before writing a new entry, ask: "Is this fundamentally the same as something I already recorded?" Same root cause, different symptom → merge , don't duplicate. New root cause → new entry. How to check: Search existing entries for keywords from your root cause before writing. If you find a match, add your new symptom as an additional example under the existing entry rather than creating a duplicate. Example: "Forgot to update the output index after creating a file" and "Forgot to update skill ratings after a task" — same root cause (no automatic capture trigger). Merge into one entry about "post-task capture gaps." Rule 3: Must Be Transferable Every entry must answer: "Next time I face a similar situation, what do I do differently?" If you can't write that sentence, you haven't extracted the pattern yet. How to extract a pattern from a concrete event: State what happened in one sentence Ask "why?" iteratively until you reach root cause (usually 3-5 whys) Generalize: "What class of problem is this?" (not "Chrome 128 bug" but "browser default change breaking existing behavior") Formulate as: "Next time I see [signal], I will [action]." Name the signal: what specific observable tells you this pattern is active? Entry Template Scope: One entry per distinct root cause. Typical length: 4-8 sentences. If it takes >2 minutes to write, you're narrating events. If <30 seconds, you haven't gone deep enough. ## [Title: the pattern, not the event] ### Context - What was I trying to do? - What went wrong / what worked surprisingly well? ### Root Cause / Core Insight - The underlying mechanism, not just the symptom ### The Pattern (transferable) - Next time [similar situation], I will [specific action]. - Signal to recognize: [what observable tells me this pattern is active?] ### Related - [ entry-name ]( ../path/to/related-entry.md ) Entry Types All four types use the template above. The type determines which sections carry the most weight: Type When to Use Emphasis Example Title Failure Something broke, needed debugging, or required rework Root Cause "Config inheritance ≠ behavior inheritance across sessions" Methodology A repeatable process emerged from the work Context / Pattern "PPT → open-book exam study guide: three-layer structure" Pattern Discovery A reusable insight about tools, systems, or thinking Pattern section "PR description template: describe the gap, not the feature" Capability Change A measurable skill improvement Context (before vs after) "Git: from clone/push to independent PR with 12 commits" Quality Checklist Before finalizing a growth log entry: Does the title name the pattern , not the event? Is there a "Next time I will..." sentence? Is the "Signal to recognize" specific enough to trigger the pattern next time? Did I search existing entries for duplicates before writing? (Bole Principle) Is the root cause distinguished from the symptom? Are related memories cross-linked? Is the entry 4-8 sentences? Shorter = too shallow; longer = narrating events. Anti-Patterns Avoid: "Fixed bug in payment module" (event, not pattern) Avoid: Copying the git commit message verbatim (commits describe what changed; logs extract why it matters) Avoid: Writing an entry for every commit (only when a pattern emerges) Avoid: Skipping the transferable sentence (without it, it's just a diary — this is non-negotiable) Avoid: Duplicating the same pattern under different titles (violates Bole Principle — search before writing) Storage Store entries wherever you keep notes. Common patterns: Markdown files in a growth-log/ directory (one file per day: YYYY-MM-DD.md ) A dedicated section in Notion, Obsidian, or your note-taking app Plain text files with a consistent naming convention Pick one convention and stick to it. Searchability matters more than format. If You Use Delivery Gate The delivery-gate Stop hook checks that learning files were modified today via filesystem timestamps. This skill teaches what to write — so the file that delivery-gate checks actually contains useful patterns, not empty timestamps. Task completes → delivery-gate checks: was the learning file touched today? → Stale (no file modified): block — "what did you learn?" → Fresh (file touched): pass — this skill ensures the content is useful Having enforcement without methodology → empty entries. Having methodology without enforcement → forgotten captures. Each is independently useful; together they close the loop.
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.skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用 下载
.skillpro 增强格式,额外含脚本 / 工具 / 依赖 / 钩子占位 下载
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