New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Project Skill Audit

by sickn33 in sickn33/agentic-awesome-skills

Audit a project and recommend the highest-value skills to add or update.

MITAuto-check passed

Install Project Skill Audit

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill project-skill-audit -a claude-code

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

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills project-skill-audit --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/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/project-skill-audit .claude/skills/project-skill-audit && 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
project-skill-audit
GitHub stars
47k
Used in
3 other repos
Token cost
~2.1k tokens
SKILL.md length
1,108 words
Files
2
Skills in repo
1,493
Repo updated
First seen
Licence
MIT

At a glance

Audit a project and recommend the highest-value skills to add or update.

  • Works in 4 steps: Search memory index first → Open targeted rollout summaries → Use raw sessions only as a fallback → …
  • SKILL.md covers Overview, When to Use, Workflow and Session Analysis, plus 8 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Project Skill Audit is an agent skill from sickn33/agentic-awesome-skills. Audit a project and recommend the highest-value skills to add or update.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).

The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

Example prompts

  • “/project-skill-audit”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Search memory index first
  2. Open targeted rollout summaries
  3. Use raw sessions only as a fallback
  4. Turn session evidence into skill candidates

What it can do on your machine

Read from SKILL.md and the folder at commit 680176d. 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.

    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

Project Skill Audit loads about 2.1k tokens when it runs. Until then it costs about 23 tokens; SKILL.md has 1,108 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~23
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k

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 sickn33/agentic-awesome-skills at commit 680176d, republished under its MIT licence (© sickn33). 1,108 words, ~2,130 tokens.

Download SKILL.mdSave it as .claude/skills/project-skill-audit/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
project-skill-audit
description
Audit a project and recommend the highest-value skills to add or update.
risk
safe
source
Dimillian/Skills (MIT)
date_added
2026-03-25

Project Skill Audit

Overview

Audit the project's real recurring workflows before recommending skills. Prefer evidence from memory, rollout summaries, existing skill folders, and current repo conventions over generic brainstorming.

Recommend updates before new skills when an existing project skill is already close to the needed behavior.

When to Use

  • When the user asks what skills a project needs or which existing skills should be updated.
  • When recommendations should be grounded in project history, memory files, and local conventions.

Workflow

  1. Map the current project surface. Identify the repo root and read the most relevant project guidance first, such as AGENTS.md, README.md, roadmap/ledger files, and local docs that define workflows or validation expectations.

  2. Build the memory/session path first. Resolve the memory base as $CODEX_HOME when set, otherwise default to ~/.codex. Use these locations:

    • memory index: $CODEX_HOME/memories/MEMORY.md or ~/.codex/memories/MEMORY.md
    • rollout summaries: $CODEX_HOME/memories/rollout_summaries/
    • raw sessions: $CODEX_HOME/sessions/ or ~/.codex/sessions/

  3. Read project past sessions in this order. If the runtime prompt already includes a memory summary, start there. Then search MEMORY.md for:

    • repo name
    • repo basename
    • current cwd
    • important module or file names Open only the 1-3 most relevant rollout summaries first. Fall back to raw session JSONL only when the summaries are missing the exact evidence you need.

  4. Scan existing project-local skills before suggesting anything new. Check these locations relative to the current repo root:

    • .agents/skills
    • .codex/skills
    • skills Read both SKILL.md and agents/openai.yaml when present.

  5. Compare project-local skills against recurring work. Look for repeated patterns in past sessions:

    • repeated validation sequences
    • repeated failure shields
    • recurring ownership boundaries
    • repeated root-cause categories
    • workflows that repeatedly require the same repo-specific context If the pattern appears repeatedly and is not already well captured, it is a candidate skill.

  6. Separate new skill from update existing skill. Recommend an update when an existing skill is already the right bucket but has stale triggers, missing guardrails, outdated paths, weak validation instructions, or incomplete scope. Recommend a new skill only when the workflow is distinct enough that stretching an existing skill would make it vague or confusing.

  7. Check for overlap with global skills only after reviewing project-local skills. Use $CODEX_HOME/skills and $CODEX_HOME/skills/public to avoid proposing project-local skills for workflows already solved well by a generic shared skill. Do not reject a project-local skill just because a global skill exists; project-specific guardrails can still justify a local specialization.

Session Analysis

1. Search memory index first
  • Search MEMORY.md with rg using the repo name, basename, and cwd.
  • Prefer entries that already cite rollout summaries with the same repo path.
  • Capture:
    • repeated workflows
    • validation commands
    • failure shields
    • ownership boundaries
    • milestone or roadmap coupling
2. Open targeted rollout summaries
  • Open the most relevant summary files under memories/rollout_summaries/.
  • Prefer summaries whose filenames, cwd, or keywords match the current project.
  • Extract:
    • what the user asked for repeatedly
    • what steps kept recurring
    • what broke repeatedly
    • what commands proved correctness
    • what project-specific context had to be rediscovered
3. Use raw sessions only as a fallback
  • Only search sessions/ JSONL files if rollout summaries are missing a concrete detail.
  • Search by:
    • exact cwd
    • repo basename
    • thread ID from a rollout summary
    • specific file paths or commands
  • Use raw sessions to recover exact prompts, command sequences, diffs, or failure text, not to replace the summary pass.
4. Turn session evidence into skill candidates
  • A candidate new skill should correspond to a repeated workflow, not just a repeated topic.
  • A candidate skill update should correspond to a workflow already covered by a local skill whose triggers, guardrails, or validation instructions no longer match the recorded sessions.
  • Prefer concrete evidence such as:
    • "this validation sequence appeared in 4 sessions"
    • "this ownership confusion repeated across extractor and runtime fixes"
    • "the same local script and telemetry probes had to be rediscovered repeatedly"
Show full SKILL.md (486 more words)Show less

Recommendation Rules

  • Recommend a new skill when:

    • the same repo-specific workflow or failure mode appears multiple times across sessions
    • success depends on project-specific paths, scripts, ownership rules, or validation steps
    • the workflow benefits from strong defaults or failure shields

  • Recommend an update when:

    • an existing project-local skill already covers most of the need
    • SKILL.md and agents/openai.yaml drift from each other
    • paths, scripts, validation commands, or milestone references are stale
    • the skill body is too generic to reflect how the project is actually worked on

  • Do not recommend a skill when:

    • the pattern is a one-off bug rather than a reusable workflow
    • a generic global skill already fits with no meaningful project-specific additions
    • the workflow has not recurred enough to justify the maintenance cost

What To Scan

  • Past sessions and memory:

    • memory summary already in context, if any
    • $CODEX_HOME/memories/MEMORY.md or ~/.codex/memories/MEMORY.md
    • the 1-3 most relevant rollout summaries for the current repo
    • raw $CODEX_HOME/sessions or ~/.codex/sessions JSONL files only if summaries are insufficient

  • Project-local skill surface:

    • ./.agents/skills/*/SKILL.md
    • ./.agents/skills/*/agents/openai.yaml
    • ./.codex/skills/*/SKILL.md
    • ./skills/*/SKILL.md

  • Project conventions:

    • AGENTS.md
    • README.md
    • roadmap, ledger, architecture, or validation docs
    • current worktree or recent touched areas if needed for context

Output Expectations

Return a compact audit with:

  1. Existing skills List the project-local skills found and the main workflow each one covers.

  2. Suggested updates For each update candidate, include:

    • skill name
    • why it is incomplete or stale
    • the highest-value change to make

  3. Suggested new skills For each new skill, include:

    • recommended skill name
    • why it should exist
    • what would trigger it
    • the core workflow it should encode

  4. Priority order Rank the top recommendations by expected value.

Naming Guidance

  • Prefer short hyphen-case names.
  • Use project prefixes for project-local skills when that improves clarity.
  • Prefer verb-led or action-oriented names over vague nouns.

Failure Shields

  • Do not invent recurring patterns without session or repo evidence.
  • Do not recommend duplicate skills when an update to an existing skill would suffice.
  • Do not rely on a single memory note if the current repo clearly evolved since then.
  • Do not bulk-load all rollout summaries; stay targeted.
  • Do not skip rollout summaries and jump straight to raw sessions unless the summaries are insufficient.
  • Do not recommend skills from themes alone; recommendations should come from repeated procedures, repeated validation flows, or repeated failure modes.
  • Do not confuse a project's current implementation tasks with its reusable skill needs.

Follow-up

If the user asks to actually create or update one of the recommended skills, switch to $skill-creator and implement the chosen skill rather than continuing the audit.

Example

User request:

Audit this repository to identify which skills it needs, which existing skills are stale, and the exact changes to make.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

© sickn33, 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 1 other file in skills/project-skill-audit of sickn33/agentic-awesome-skills.

  • SKILL.md
  • agents/openai.yaml

Open the folder on GitHubat commit 680176d

Used in 3 other repositories

We found 7 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Project Skill Audit 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.

Project Skill Audit compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Project Skill Audit this skillsickn33/agentic-awesome-skills47k3 repos~2.1kAutomated safety check: PassMIT
Recommendagenticnotetaking/arscontexta3.5k—~5.1kAutomated safety check: PassMIT
Value Propositionphuryn/pm-skills27k—~1.5kAutomated safety check: PassMIT
Value Prop Statementsphuryn/pm-skills27k—~758Automated safety check: PassMIT
Restaurant Recommendationsasgeirtj/system_prompts_leaks69k—~755Automated safety check: PassCC0-1.0
Aria Valid Attr Valuethedaviddias/Front-End-Checklist74k—~456Automated safety check: PassMIT

Similar skills

  • Recommend

    agenticnotetaking/arscontexta

    Get research-backed architecture advice for your knowledge system.

    3.5k GitHub stars~5.1k tokensUpdated 7 mo ago
    Auto-check passed

  • Value Proposition

    phuryn/pm-skills

    Design a detailed value proposition using a 6-part JTBD template — Who, Why, What before, How, What after, Alternatives.

    27k GitHub stars~1.5k tokensUpdated 24 days ago
    Marketing & SEOAuto-check passed

  • Value Prop Statements

    phuryn/pm-skills

    Generate value proposition statements for marketing, sales, and onboarding from existing value propositions.

    27k GitHub stars~758 tokensUpdated 24 days ago
    Marketing & SEOAuto-check passed

  • Restaurant Recommendations

    asgeirtj/system_prompts_leaks

    Find restaurants that fit the people, occasion, location and budget.

    69k GitHub stars~755 tokensUpdated yesterday
    Auto-check passed

  • Aria Valid Attr Value

    thedaviddias/Front-End-Checklist

    A skill your agent uses when reviewing rendered HTML, interactive components, or design-system patterns related to Use valid values for ARIA attributes.

    74k GitHub stars~456 tokensUpdated 3 days ago
    Frontend & DesignAuto-check passed

  • Recommendations

    amd/gaia

    Recommend films, books, games, restaurants, or gear using what the user has already said they like and dislike, plus a web search for current options.

    1.6k GitHub stars~622 tokensUpdated today
    Productivity & AutomationAuto-check passed

More from sickn33/agentic-awesome-skills

All 1,493 skills in this repo

  • Liuguang Banlan UI

    sickn33/agentic-awesome-skills

    Implements an interface in one of two named color modes, iridescent white or colorful black, from a parameterized starter that reports measured color intensity.

    47k GitHub starsUsed in 1 repo~2.5k tokens
    Auto-check passed

  • User Thoughts Memory

    sickn33/agentic-awesome-skills

    Saves a user's project decisions, rules and preferences into a project-local mdbase so later sessions and other agents can recover the intent.

    47k GitHub starsUsed in 1 repo~2.5k tokens
    Auto-check passed

  • Using LWC Memory and Graphs

    sickn33/agentic-awesome-skills

    Keeps project decisions, research and verified results available across coding-agent sessions through LWC memory, a document Wiki graph and a CodeGraph code index.

    47k GitHub starsUsed in 1 repo~2k tokens
    Auto-check passed

  • Find Complementary Founders

    sickn33/agentic-awesome-skills

    Guides an agent through assessing its own owner for cofounder fit, publishing an approved profile, and ranking complementary profiles other agents published for their owners.

    47k GitHub starsUsed in 1 repo~4.8k tokens
    Auto-check passed

  • Whatsapp Cloud API

    sickn33/agentic-awesome-skills

    Integracao com WhatsApp Business Cloud API (Meta). An agent skill from sickn33/agentic-awesome-skills.

    47k GitHub starsUsed in 2 repos~4.5k tokens
    Auto-check passed

  • Cline Pilot

    sickn33/agentic-awesome-skills

    Acts as a proxy for the Cline CLI, dispatching coding tasks one at a time, monitoring runs by hard evidence, relaying decisions to you and learning per-project preferences.

    47k GitHub starsUsed in 1 repo~4.6k tokens
    Auto-check passed

Questions about Project Skill Audit

What does Project Skill Audit do?

Audit a project and recommend the highest-value skills to add or update. Project Skill Audit is an agent skill from sickn33/agentic-awesome-skills. Audit a project and recommend the highest-value skills to add or update.

How do I install Project Skill Audit in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill project-skill-audit -a claude-code`. Or copy the skill folder (skills/project-skill-audit in sickn33/agentic-awesome-skills) into .claude/skills/project-skill-audit in your project. Claude Code loads it when a task matches its description.

How do I install Project Skill Audit in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill project-skill-audit -a codex`. Or copy the skill folder (skills/project-skill-audit in sickn33/agentic-awesome-skills) into .agents/skills/project-skill-audit in your project. Codex loads it when a task matches its description.

Can I use Project Skill Audit 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 sickn33/agentic-awesome-skills --skill project-skill-audit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/project-skill-audit, .gemini/skills/project-skill-audit, .github/skills/project-skill-audit and .opencode/skills/project-skill-audit in your project.

What does Project Skill Audit need to run?

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

Does Project Skill Audit 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 Project Skill Audit 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 Project Skill Audit use?

Project Skill Audit is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Project Skill Audit use?

About 2.1k tokens (SKILL.md is roughly 8.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Project Skill Audit?

Skills that share tags, products or a category with Project Skill Audit: Recommend (agenticnotetaking/arscontexta, 3.5k stars), Value Proposition (phuryn/pm-skills, 27k stars), Value Prop Statements (phuryn/pm-skills, 27k stars) and Restaurant Recommendations (asgeirtj/system_prompts_leaks, 69k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Project Skill Audit?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,379 GitHub stars. The repository holds 1,493 skills in this directory. The repository was last updated on October 9, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.