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 agentmods add skills/michaelycjo/specseal/learnnpx skills add MichaelYcJo/SpecSeal --skill learngit clone --depth 1 https://github.com/MichaelYcJo/SpecSealWrote 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/michaelycjo/specseal/learn)<a href="https://agentmods.dev/skills/michaelycjo/specseal/learn"><img src="https://agentmods.dev/badge/skills/michaelycjo/specseal/learn.svg" alt="Measured on agentmods" 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 | $0.00085 | $0.00442 |
| Opus 5 | $0.00043 | $0.00221 |
| Sonnet 5 | $0.00017 | $0.00088 |
| Haiku 4.5 | $0.00009 | $0.00044 |
Grade A, and why
learn 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 4d 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.
What it actually says
learn — write down only what a search cannot return
Most of what a session discovers is recoverable: the code holds it, git holds it, a search answers it in five minutes. What is not recoverable is the thing that cost hours and leaves no trace — the dependency quirk, the reason an abstraction is shaped that way. Record that and nothing else.
Save Criteria (must meet ALL)
- Non-Googleable - Can't find answer in 5-min search
- Project-specific - Tied to this codebase/setup
- Hard-won - Required real debugging effort
- Actionable - Includes specific files, lines, code
What to Save
- Bug patterns unique to this project
- Non-obvious configuration requirements
- Architectural decisions and their reasons
- Dependency quirks and workarounds
- Performance pitfalls discovered through profiling
What NOT to Save
- General programming knowledge
- Standard library usage
- Anything in official docs
- Temporary workarounds (save the real fix instead)
Storage
Save to: ~/.claude/projects/<project>/memory/
- Use semantic filenames:
auth-session-quirk.md,db-connection-pool.md - Keep each file focused on one insight
- Update existing files rather than creating duplicates
Format
# [Short descriptive title]
## Problem
[What went wrong]
## Root Cause
[Why it happened - specific to this project]
## Solution
[What fixed it - with file paths and code]
## Prevention
[How to avoid this in the future]
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.
- 4d ago First seen · 62 lines · 85 tokens per session scan A c41748e66262
learn is a skill published in the GitHub repository MichaelYcJo/SpecSeal (1 stars, last pushed today), licensed MIT. It adds 85 tokens to every session and 442 once invoked, about $0.0004 per session on Opus 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 2026-08-31.
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