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 dannote/dot-pi --skill chat-to-skillgit clone --depth 1 https://github.com/dannote/dot-piWrote 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/dannote/dot-pi/chat-to-skill)<a href="https://agentmods.dev/skills/dannote/dot-pi/chat-to-skill"><img src="https://agentmods.dev/badge/skills/dannote/dot-pi/chat-to-skill/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/dannote/dot-pi/chat-to-skill"><img src="https://agentmods.dev/badge/skills/dannote/dot-pi/chat-to-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Data Exfiltration · line 47 Code or instructions that leak agent conversation context to external services, potentially exposing sensitive user interactions.Fix: Remove any code that sends prompts, responses, or session data externally. Preserve user privacy; never exfiltrate conversation content.
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 | $0.00057 | $0.01112 |
| Opus 5.5 | $0.00023 | $0.00445 |
| Sonnet 5.5 | $0.00011 | $0.00222 |
| Haiku 4.5 | $0.00006 | $0.00111 |
Grade A, and why
chat-to-skill 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 2mo 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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chat to Skill
Transform conversation history into reusable skills — long-term memory for Claude.
Process
1. Analyze Dialog
Scan the entire conversation to identify:
- Primary goal: What was the user trying to achieve?
- Secondary goals: Any related objectives discovered along the way
- Errors encountered: Mistakes, dead ends, wrong approaches
- Successful path: What actually worked
2. Abstract to Reusable Patterns
Critical: Do NOT create skills for specific cases. Abstract to general patterns.
Ask yourself:
- What CATEGORY of problem was solved? (not the specific instance)
- What would this look like with different data/context?
- Would this skill be useful in other projects?
Abstraction levels (from bad to good):
| Too specific (BAD) | Good abstraction |
|---|---|
| "Seed users from client Excel" | "Import spreadsheet data into Rails" |
| "Parse names into fields" | (implementation detail, not a skill) |
| "Fix pytest in project X" | "Configure pytest for monorepos" |
| "Add dark mode to app Y" | "Implement theme switching in React" |
Rules:
- Remove project names, organization names, specific entities
- Focus on the TECHNIQUE, not the specific data
- If something is just an implementation detail (name parsing, date formatting), it's not a separate skill
- One dialog = usually one skill (the main workflow), not multiple micro-skills
3. Extract Context-Specific Details
Depending on the task type, look for:
Development tasks:
- Commands and flags that worked
- Versions and compatibility (what works with what)
- Configuration that was needed
- Code patterns and architectural decisions
- Debugging process (how the root cause was found)
- Tool/library choices and why
Research/analysis tasks:
- Sources that proved useful
- Search strategies that worked
- How to validate findings
Process/workflow tasks:
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.
- 2mo ago First seen · 169 lines · 57 tokens per session scan A c8e900ab38d1
chat-to-skill is a skill published in the GitHub repository dannote/dot-pi (57 stars, last pushed 2d ago), licensed MIT. It adds 57 tokens to every session and 1,112 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 2026-08-30.
chat-to-skill compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| chat-to-skilldannote/dot-pi | Skill | 57 | 2d ago | MIT | A | 57 | Claude Code, Codex |
| a0-developmentagent0ai/agent-zero | Skill | 20k | yesterday | none | A | 24 | Claude Code, Codex |
| temporal-developertemporalio/skill-temporal-developer | Skill | 236 | yesterday | MIT | A | 173 | Claude Code, Codex |
| cafleethimkt/cafleet | Skill | 56 | yesterday | MIT | A | 62 | Claude Code, Codex |
| anycap-human-interactionanycap-ai/anycap | Skill | 50 | 16d ago | MIT | A | 190 | Claude Code |
| anycap-media-productionanycap-ai/anycap | Skill | 50 | 16d ago | MIT | A | 167 | Claude Code |
| anycap-blog-productionanycap-ai/anycap | Skill | 50 | 16d ago | MIT | A | 143 | Claude Code, Codex |