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Agent skill

Technical Skill Finder

by vincentkoc in vincentkoc/dotskills

Mine coding agent logs (Codex/Cursor/session histories and similar telemetry) to discover high-value candidate skills, then draft structured skill creation/reuse recommendations.

MITAuto-check passed

Install Technical Skill Finder

skills CLI
$ npx skills add vincentkoc/dotskills --skill technical-skill-finder -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install vincentkoc/dotskills technical-skill-finder --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/vincentkoc/dotskills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/technical-skill-finder .claude/skills/technical-skill-finder && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
technical-skill-finder
GitHub stars
107
Token cost
~1.2k tokens
SKILL.md length
460 words
Files
5 (incl. references, assets)
Skills in repo
19
Repo updated
First seen
Licence
MIT

At a glance

Mine coding agent logs (Codex/Cursor/session histories and similar telemetry) to discover high-value candidate skills, then draft structured skill creation/reuse recommendations.

  • Works in 7 steps: Initialize source set → Normalize extraction signals → Cluster signals → …
  • SKILL.md covers Purpose, When to use, Inputs and Workflow, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Technical Skill Finder is an agent skill from vincentkoc/dotskills. Mine coding agent logs (Codex/Cursor/session histories and similar telemetry) to discover high-value candidate skills, then draft structured skill creation/reuse recommendations.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including reference files and assets (for example `agents/openai.yaml`, `references/scorecard.md` and `references/sources.md`).

The repository describes itself as: 🐙 A curated set of Codex and OpenClaw skills for workflow automation, technical debugging, and agent-assisted development patterns. The licence is MIT.

Example prompts

  • “/technical-skill-finder”

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Initialize source set
  2. Normalize extraction signals
  3. Cluster signals
  4. Map to existing skills
  5. Emit ranking output
  6. Produce minimal first-iteration output for high-priority candidates
  7. Optional extension to personal-signal sources

What it can do on your machine

Read from SKILL.md and the folder at commit b83ca13. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are mermaid).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Technical Skill Finder loads about 1.2k tokens when it runs, and up to ~1.5k if it reads all its reference files. Until then it costs about 50 tokens; SKILL.md has 460 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~50
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from vincentkoc/dotskills at commit b83ca13, republished under its MIT licence (© vincentkoc). 460 words, ~1,215 tokens.

Download SKILL.mdSave it as .claude/skills/technical-skill-finder/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
technical-skill-finder
description
Mine coding agent logs (Codex/Cursor/session histories and similar telemetry) to discover high-value candidate skills, then draft structured skill creation/reuse recommendations.
license
MIT
metadata.source
https://github.com/vincentkoc/dotskills

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): default all unless constrained by user.
  • PRIVACY_POLICY (required): explicit user direction for personal logs.
  • TOP_N (optional): number of highest-priority candidates to return.

Workflow

  1. Initialize source set
    • ~/.codex/history.jsonl
    • ~/.codex/archived_sessions/*.jsonl
    • ~/.codex/sessions/*.jsonl and ~/.codex/log/* if present
    • Repository-specific telemetry in AGENTS.md/local docs when available
    • Cursor / Codex agent logs detected under known dotfiles directories
  2. 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
  3. Cluster signals
    • Group by: domain (python/js/rust/docs/tooling), command lineage, and error signature.
    • Deprioritize one-off sessions with low recurrence.
  4. Map to existing skills
    • Compare candidate clusters with available skills by name and description.
    • If overlap is high, propose skill update path.
    • If no overlap, propose new skill.
  5. Emit ranking output
    • Provide impact, frequency, confidence, skill-fit, and first-apply command set.
  6. Produce minimal first-iteration output 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)
    • Return this through chat/stdout by default. Create a persistent artifact root only when the user selects one or another required workflow declares it, with file/byte budgets and source/input identity.
  7. 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.
Show full SKILL.md (120 more words)Show less

Guardrails

  • Never infer or emit private content from message logs unless explicitly permitted.
  • Skip binary/corrupt files and summarize only parseable text sources.
  • Prefer deterministic commands and small scripts over ad-hoc manual parsing.
  • Always avoid proposing skills with unresolved operational context (credentials, environment, private URLs).
  • If evidence is ambiguous, return confidence: low and request one more session sample.
  • Reuse one canonical identity-matched inventory instead of materializing duplicate large extracts. Apply the $operations-worktree task artifact contract when retention or resumable phase state is required.

Outputs

  • skill_candidates.md-style report in chat:
    • reuse candidates (existing skill can be extended)
    • new skill candidates (not yet covered)
    • top source anchors with references
    • recommended next action (create/update)

Read references/sources.md for source precedence. Read references/scorecard.md for prioritization rules.

Flow

mermaid
stateDiagram-v2
    [*] --> SelectAuthorizedSources
    SelectAuthorizedSources --> NormalizeAndCluster
    NormalizeAndCluster --> CompareExistingSkills
    CompareExistingSkills --> ProposeUpdate: substantial overlap
    CompareExistingSkills --> ProposeNewSkill: no existing coverage
    CompareExistingSkills --> ReportUncertainty: insufficient evidence
    ProposeUpdate --> RankAndReport
    ProposeNewSkill --> RankAndReport
    RankAndReport --> [*]
    ReportUncertainty --> [*]
    note right of SelectAuthorizedSources
        Personal channels require explicit approval.
        Return findings inline unless retention is selected.
    end note

© vincentkoc, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (references, assets) in skills/technical-skill-finder of vincentkoc/dotskills.

  • SKILL.md
  • agents/openai.yaml
  • assets/icon.jpg
  • references/scorecard.md
  • references/sources.md

Open the folder on GitHubat commit b83ca13

Compare with similar skills

Technical Skill Finder next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Technical Skill Finder compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Technical Skill Finder this skillvincentkoc/dotskills107—~1.2kAutomated safety check: PassMIT
Growth Logaffaan-m/ECC276k1 repos~1.7kAutomated safety check: PassMIT
Clickhouse Logs Queriessupabase/supabase111k—~2.4kAutomated safety check: PassApache-2.0
Error Log Mininguphiago/recon-skills1.3k—~3.3kAutomated safety check: PassMIT
OmniRoute Usage Logsdiegosouzapw/OmniRoute74k—~2kAutomated safety check: PassMIT
Investigating LogsPostHog/posthog40k—~2.1kAutomated safety check: PassCustom licence

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Questions about Technical Skill Finder

What does Technical Skill Finder do?

Mine coding agent logs (Codex/Cursor/session histories and similar telemetry) to discover high-value candidate skills, then draft structured skill creation/reuse recommendations. Technical Skill Finder is an agent skill from vincentkoc/dotskills. Mine coding agent logs (Codex/Cursor/session histories and similar telemetry) to discover high-value candidate skills, then draft structured skill creation/reuse recommendations.

How do I install Technical Skill Finder in Claude Code?

Run `npx skills add vincentkoc/dotskills --skill technical-skill-finder -a claude-code`. Or copy the skill folder (skills/technical-skill-finder in vincentkoc/dotskills) into .claude/skills/technical-skill-finder in your project. Claude Code loads it when a task matches its description.

How do I install Technical Skill Finder in Codex?

Run `npx skills add vincentkoc/dotskills --skill technical-skill-finder -a codex`. Or copy the skill folder (skills/technical-skill-finder in vincentkoc/dotskills) into .agents/skills/technical-skill-finder in your project. Codex loads it when a task matches its description.

Can I use Technical Skill Finder in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add vincentkoc/dotskills --skill technical-skill-finder -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/technical-skill-finder, .gemini/skills/technical-skill-finder, .github/skills/technical-skill-finder and .opencode/skills/technical-skill-finder in your project.

What does Technical Skill Finder need to run?

SKILL.md names no scripts, command-line tools or credentials: Technical Skill Finder is instructions for the agent only.

Does Technical Skill Finder access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Technical Skill Finder safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Technical Skill Finder use?

Technical Skill Finder is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Technical Skill Finder use?

About 1.2k tokens (SKILL.md is roughly 4.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 261 tokens, read only when the agent opens those files.

What are the alternatives to Technical Skill Finder?

Skills that share tags, products or a category with Technical Skill Finder: Growth Log (affaan-m/ECC, 276k stars), Clickhouse Logs Queries (supabase/supabase, 111k stars), Error Log Mining (uphiago/recon-skills, 1.3k stars) and OmniRoute Usage Logs (diegosouzapw/OmniRoute, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Technical Skill Finder?

vincentkoc (a GitHub user) maintains it in vincentkoc/dotskills, which has 107 GitHub stars. The repository holds 19 skills in this directory. The repository was last updated on October 8, 2026.

Source: vincentkoc/dotskills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.