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 vincentkoc/dotskills --skill technical-skill-findergit clone --depth 1 https://github.com/vincentkoc/dotskillsWrote 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/vincentkoc/dotskills/technical-skill-finder)<a href="https://agentmods.dev/skills/vincentkoc/dotskills/technical-skill-finder"><img src="https://agentmods.dev/badge/skills/vincentkoc/dotskills/technical-skill-finder.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00037 | $0.00836 |
| Opus 5 | $0.00018 | $0.00418 |
| Sonnet 5 | $0.00007 | $0.00167 |
| Haiku 4.5 | $0.00004 | $0.00084 |
Grade A, and why
technical-skill-finder 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 8d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Technical Skill Finder
Purpose
Find recurring pain points from local agent logs and convert them into actionable skill candidates, reuse opportunities, or existing skill updates.
When to use
- You want to discover missing technical skills from historical agent activity.
- You want reproducible criteria before creating a new skill.
- You want to validate whether an existing skill already covers the pattern.
- You want to include optional personal-signal sources (when authorized).
Inputs
SCOPE(required): repository paths, workspace, or tool domains to inspect.SOURCES(required): ordered source list to mine.TIMEFRAME(optional): defaultallunless constrained by user.PRIVACY_POLICY(required): explicit user direction for personal logs.TOP_N(optional): number of highest-priority candidates to return.
Workflow
- Initialize source set
~/.codex/history.jsonl~/.codex/archived_sessions/*.jsonl~/.codex/sessions/*.jsonland~/.codex/log/*if present- Repository-specific telemetry in
AGENTS.md/local docs when available Cursor/Codexagent logs detected under known dotfiles directories
- Normalize extraction signals
- Parse stack traces and classify failure type (
auth,type-check,llm-error,git/ci,runtime,refactor-merge,test) - Parse recurring command phrases (
rg,mypy,pytest,gh,git, package-manager failures) - Record frequency, recency, and affected project context
- Parse stack traces and classify failure type (
- Cluster signals
- Group by: domain (python/js/rust/docs/tooling), command lineage, and error signature.
- Deprioritize one-off sessions with low recurrence.
- Map to existing skills
- Compare candidate clusters with available skills by
nameanddescription. - If overlap is high, propose skill update path.
- If no overlap, propose new skill.
- Compare candidate clusters with available skills by
- Emit ranking output
- Provide
impact,frequency,confidence,skill-fit, and first-apply command set.
- Provide
- Produce minimal first-iteration artifacts for high-priority candidates
- Candidate title + scope
- Trigger phrase examples
- Required inputs
- Suggested workflow summary
- Evidence snippets (line/file-level)
- Suggested dependencies/tools (e.g.,
jq,rg, shell utilities, MCP resources)
- Optional extension to personal-signal sources
- Only after explicit approval to read personal channels.
- If MCP is available and user has granted access, run MCP resource discovery and include message-signal-derived patterns.
- Keep this opt-in and isolated from coding-signal output unless user requests a merged plan.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 81 lines · 37 tokens per session scan A 9ae29793c294
technical-skill-finder is a skill published in the GitHub repository vincentkoc/dotskills (102 stars, last pushed today), licensed MIT. It adds 37 tokens to every session and 836 once invoked, about $0.0002 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 2026-08-30.
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