Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add gongyijie85/dsh-ecc --skill growth-loggit clone --depth 1 https://github.com/gongyijie85/dsh-eccWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/gongyijie85/dsh-ecc/growth-log)<a href="https://agentmods.dev/skills/gongyijie85/dsh-ecc/growth-log"><img src="https://agentmods.dev/badge/skills/gongyijie85/dsh-ecc/growth-log/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gongyijie85/dsh-ecc/growth-log"><img src="https://agentmods.dev/badge/skills/gongyijie85/dsh-ecc/growth-log.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00034 | $0.01517 |
| Opus 5 | $0.00017 | $0.00758 |
| Sonnet 5 | $0.00007 | $0.00303 |
| Haiku 4.5 | $0.00003 | $0.00152 |
Grade A, and why
growth-log scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
94% identical to growth-log — 26 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 129 lines · 34 tokens per session scan A 8f90db7c9e84
growth-log is a skill published in the GitHub repository gongyijie85/dsh-ecc (7 stars, last pushed yesterday), licensed MIT. It adds 34 tokens to every session and 1,517 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to growth-log, differing in 26 lines, and is treated as a copy.
Other skills, from other repositories
manage-taskboard
Manage work in the native DeepSeek Harness Taskboard with exact task ids and optimistic versions. Use when an Agent must inspect project work, claim an eligible todo, record progress or blockers, verify an implementation, submit it for human review, or release its own claim; also use when a human asks how to accept…
teach-zh
A teaching workflow for helping a user learn a subject over multiple sessions. It stores the learner’s goals, reference material, lessons, notes, and learning records in the current workspace.
promentor
A tool that turns a software project into a hands-on course in the style of an MIT course. It can produce lessons, code walkthroughs, practical labs, behavior tests, grading, explanations, and AI reviews.
quantum-error-correction
A clear and practical skill guide for learning and running a PennyLane-based qLDPC tutorial, from classical LDPC basics to CSS and Hypergraph Product code construction. Skill-first for covered code generation, runnable examples, execution, debugging, validation, and fixed workflows.
grilling
Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.
teach
Teach the user a new skill or concept, within this workspace.