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 yeaight7/agent-powerups --skill hard-won-skill-extractorgit clone --depth 1 https://github.com/yeaight7/agent-powerupsWrote 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/yeaight7/agent-powerups/hard-won-skill-extractor)<a href="https://agentmods.dev/skills/yeaight7/agent-powerups/hard-won-skill-extractor"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/hard-won-skill-extractor/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/yeaight7/agent-powerups/hard-won-skill-extractor"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/hard-won-skill-extractor.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 | $0.00042 | $0.01024 |
| Opus 5.5 | $0.00017 | $0.00410 |
| Sonnet 5.5 | $0.00008 | $0.00205 |
| Haiku 4.5 | $0.00004 | $0.00102 |
Grade A, and why
hard-won-skill-extractor 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hard-Won Skill Extractor
This skill serves to document how to manually extract a useful learned skill into a reusable markdown file.
Core Principle
Reusable skills are not code snippets to copy-paste, but principles and decision-making heuristics that teach an agent HOW TO THINK about a class of problems.
The difference:
- BAD (mimicking): "When you see ConnectionResetError, add this try/except block"
- GOOD (reusable skill): "In async network code, any I/O operation can fail independently due to client/server lifecycle mismatches. The principle: wrap each I/O operation separately, because failure between operations is the common case, not the exception."
Quality Gate
Before extracting a skill manually, ALL three must be true:
- "Could someone Google this in 5 minutes?" → NO
- "Is this specific to THIS codebase?" → YES
- "Did this take real debugging effort to discover?" → YES
Recognition Signals
Extract ONLY after:
- Solving a tricky bug that required deep investigation
- Discovering a non-obvious workaround specific to this codebase
- Finding a hidden gotcha that wastes time when forgotten
- Uncovering undocumented behavior that affects this project
What Makes a USEFUL Skill
- Non-Googleable: Something you couldn't easily find via search
- Context-Specific: References actual files, error messages, or patterns from THIS codebase
- Actionable with Precision: Tells you exactly WHAT to do and WHERE
- Hard-Won: Took significant debugging effort to discover
Anti-Patterns (DO NOT EXTRACT)
- Generic programming patterns (use documentation instead)
- Refactoring techniques (these are universal)
- Library usage examples (use library docs)
- Type definitions or boilerplate
- Anything a junior dev could Google in 5 minutes
Manual Extraction Workflow
Step 1: Gather Required Information
- Problem Statement: The SPECIFIC error, symptom, or confusion that occurred
- Solution: The EXACT fix, not general advice
- Triggers: Keywords that would appear when hitting this problem again
- Scope: Almost always Project-level unless it's a truly universal insight
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.
- 1mo ago First seen · 127 lines · 42 tokens per session scan A f99e30a12bb1
hard-won-skill-extractor is a skill published in the GitHub repository yeaight7/agent-powerups (6 stars, last pushed 19d ago), licensed Apache-2.0. It adds 42 tokens to every session and 1,024 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-14.
Other skills, from other repositories
skill-authoring
Author SKILL.md: frontmatter, structure, writing principles.
systematic-debugging
4-phase root cause debugging: understand bugs before fixing.
github-code-review
Review PRs: diffs, inline comments via gh or REST.
requesting-code-review
Pre-commit review: security scan, quality gates, auto-fix.
simplify-code
Sequential 3-lens cleanup of recent code changes.
test-driven-development
TDD: enforce RED-GREEN-REFACTOR, tests before code.