Discovers your full tool environment and amplifies prompts with capability awareness.

Apache-2.0Auto-check: notes

Install Tool Advisor

skills CLI
$ npx skills add hashgraph-online/awesome-codex-plugins --skill tool-advisor -a claude-code

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

GitHub CLI
$ gh skill install hashgraph-online/awesome-codex-plugins tool-advisor --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/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/dragon1086/claude-skills/skills/tool-advisor .claude/skills/tool-advisor && 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
tool-advisor
GitHub stars
1.2k
Token cost
~2.8k tokens
SKILL.md length
704 words
Files
2
Skills in repo
686
Repo updated
First seen
Licence
Apache-2.0

At a glance

Discovers your full tool environment and amplifies prompts with capability awareness.

  • Works in 6 steps: Discover Environment → Analyze Task + Define Completion → Capability Matching → …
  • The user asks what tools should I use
  • SKILL.md covers Iron Rules, Phase 1: Discover Environment, Phase 2: Analyze Task + Define… and Phase 3: Capability Matching, plus 6 more sections
  • Calls python3

What it does

Tool Advisor is an agent skill from hashgraph-online/awesome-codex-plugins. Discovers your full tool environment and amplifies prompts with capability awareness. Suggests optimal tool compositions as non-binding options. Use when the user asks "what tools should I use", "best approach for this task", "how should I tackle", or explicitly mentions tool-advisor / $tool-advisor / ta. Do NOT trigger for direct coding requests, explanations, or reviews without tool-selection intent.

Its SKILL.md is about 2.8k 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: A curated list of awesome OpenAI Codex / ChatGPT plugins, skills, and resources. The 1 Codex Marketplace. See live plugins at: https://hol.org/plugins/best-codex-plugins. The licence is Apache-2.0.

When your agent uses it

  • The user asks what tools should I use
  • Best approach for this task
  • How should I tackle
  • Explicitly mentions tool-advisor / $tool-advisor / ta

Example prompts

  • “what tools should I use”
  • “best approach for this task”
  • “how should I tackle”
  • “/tool-advisor”

Requirements

  • Python 3
  • Docker

Workflow steps

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

  1. Discover Environment
  2. Analyze Task + Define Completion
  3. Capability Matching
  4. Suggest Options
  5. Capability Gap
  6. Performance Tips

What it can do on your machine

Read from SKILL.md and the folder at commit 78497e5. 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

    Shell commands in SKILL.md call:

    • python3

    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

Tool Advisor loads about 2.8k tokens when it runs. Until then it costs about 105 tokens; SKILL.md has 704 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~105
When it runs · the whole SKILL.md, loaded when a task matches
~2.8k

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:137
    [ -f .env ] && echo "  .env exists ($(wc -l < .env) lines)" ;

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 hashgraph-online/awesome-codex-plugins at commit 78497e5, republished under its Apache-2.0 licence (© hashgraph-online). 704 words, ~2,777 tokens.

Download SKILL.mdSave it as .claude/skills/tool-advisor/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
tool-advisor
description
Discovers your full tool environment and amplifies prompts with capability awareness. Suggests optimal tool compositions as non-binding options. Use when the user asks "what tools should I use", "best approach for this task", "how should I tackle", or explicitly mentions tool-advisor / $tool-advisor / ta. Do NOT trigger for direct coding requests, explanations, or reviews without tool-selection intent.
argument-hint
<prompt or task description>
metadata.author
aerok
metadata.version
3.5.1
aliases
ta

Tool Advisor v3.5 — Cross-Agent Amplifier + Optional Composer

You are a Tool Amplifier: DISCOVER what the user has, DELIVER enriched context, SUGGEST tool compositions as options. You arm the model with knowledge — you never replace its judgment.


Iron Rules

  1. NEVER execute mutating actions. No edits, commits, installs, or task executors. Read-only scans (Phase 1) are permitted. You scan and advise.
  2. Complete ALL 6 phases. Each produces visible output or "N/A — [reason]". No skipping.
  3. MUST end with Quick Action table. Copy-paste first steps. No exceptions.
  4. No internal deliberation in output. Reason internally, present conclusions only.
  5. Follow the output template literally. Small=collapsed (<10 lines). Medium+=full. Every section appears or gets "N/A".
  6. STOP after template output. The template IS your deliverable. Do not execute any approach.
  7. Scale output to task complexity. Don't over-engineer a typo fix.
  8. Max 3 questions, one message. If unknowns exist, ask once then proceed with sensible defaults.
  9. Human-in-the-loop for installs. Never auto-install anything.

Phase 1: Discover Environment

Layer 1 — Native Tools (enumerate, don't scan)
  • File/Search: Read, Write, Edit, Glob, Grep (or equivalent agent-native tools)
  • Execution: shell/terminal execution tools
  • Web: web search/fetch tools (if available)
  • Agent: subagent/delegation tools (if available)
  • Planning: plan/user-question tools (if available)
  • Task Tracking: task CRUD tools (if available)
Layer 2–4 — Dynamic Discovery (single Bash call)
bash
echo "=== MCP Servers ===" ;
for f in ~/.claude/settings.json .claude/settings.json .mcp.json; do
  [ -f "$f" ] && echo "-- $f --" && python3 -c "
import sys,json
try:
  d=json.load(open('$f')); servers=d.get('mcpServers',{})
  for k in servers: print(f'  {k}')
  if not servers: print('  (none)')
except: print('  (none)')
" 2>/dev/null
done ;
for f in ~/.codex/config.json ~/.codex/settings.json .codex/config.json .codex/settings.json; do
  [ -f "$f" ] && echo "-- $f --" && python3 -c "
import sys,json
try:
  d=json.load(open('$f')); servers=d.get('mcpServers',{}) or d.get('mcp_servers',{})
  for k in servers: print(f'  {k}')
  if not servers: print('  (none)')
except: print('  (none)')
" 2>/dev/null
done ;
if [ -f ~/.codex/config.toml ]; then
  echo "-- ~/.codex/config.toml --" ;
  python3 -c "
import re, pathlib
p=pathlib.Path('~/.codex/config.toml').expanduser()
txt=p.read_text(errors='ignore')
found=False
for m in re.finditer(r'^\\s*\\[mcp_servers\\.([^\\]]+)\\]', txt, re.M):
  print(f'  {m.group(1)}'); found=True
if not found: print('  (none)')
" 2>/dev/null || echo "  (none)" ;
fi ;
echo "=== Skills ===" ;
SKILLS_FOUND=0 ;
for root in ~/.claude/skills ~/.agents/skills ~/.codex/skills; do
  [ -d "$root" ] || continue ;
  for d in "$root"/*/; do
    [ -d "$d" ] || continue ;
    desc=$(head -25 "$d/SKILL.md" 2>/dev/null | python3 -c "
import sys
lines = sys.stdin.readlines()
in_desc = False; parts = []
for line in lines:
    if line.startswith('description:'):
        in_desc = True
        v = line.split('description:',1)[1].strip().lstrip('>')
        if v: parts.append(v)
    elif in_desc:
        if line.startswith(' ') or line.startswith('\t'): parts.append(line.strip())
        else: break
print(' '.join(parts)[:150])
" 2>/dev/null) ;
    echo "  $(basename "$d"): $desc" ;
    SKILLS_FOUND=1 ;
  done ;
done ;
[ "$SKILLS_FOUND" -eq 0 ] && echo "  (none)" ;
echo "=== Plugins ===" ;
cat ~/.claude/plugins/installed_plugins.json 2>/dev/null | python3 -c "
import sys,json
try:
  d=json.load(sys.stdin)
  for k in d: print(f'  {k}')
  if not d: print('  (none)')
except: print('  (none)')
" 2>/dev/null || echo "  (none)" ;
echo "=== Agents ===" ;
AGENTS_FOUND=0 ;
for d in ~/.claude/agents ~/.agents/agents ~/.codex/agents; do
  [ -d "$d" ] || continue ;
  echo "-- $d --" ;
  for f in "$d"/*.md "$d"/*.yaml "$d"/*.yml "$d"/*.txt; do
    [ -f "$f" ] || continue ;
    name=$(basename "$f" | sed 's/\.[^.]*$//') ;
    desc=$(head -20 "$f" 2>/dev/null | grep -E '^description:|^role:|^# ' | head -1 | sed 's/^[^:]*:[[:space:]]*//' | sed 's/^# //' | cut -c1-120) ;
    echo "  $name: ${desc:-(no description)}" ;
    AGENTS_FOUND=1 ;
  done ;
done ;
[ "$AGENTS_FOUND" -eq 0 ] && echo "  (none)" ;
echo "=== Dev Tools ===" ;
for cmd in git node python3 docker pytest npm pnpm bun cargo go java ruby codex claude gemini aider cursor; do
  command -v $cmd >/dev/null 2>&1 && echo "  $cmd: $(command -v $cmd)"
done ;
echo "=== Environment Hints ===" ;
[ -f .env ] && echo "  .env exists ($(wc -l < .env) lines)" ;
[ -f .env.example ] && echo "  .env.example exists" ;
[ -f docker-compose.yml ] || [ -f docker-compose.yaml ] && echo "  docker-compose found" ;
[ -f Makefile ] && echo "  Makefile found" ;
[ -f Taskfile.yml ] && echo "  Taskfile found" ;
[ -f justfile ] && echo "  justfile found"

→ Output: "Your Environment" table (full) or inline env summary (collapsed).


Phase 2: Analyze Task + Define Completion

Classify the task in one line. Extract a "Done when" sentence. This phase is intentionally minimal — the model already reasons about tasks well; the skill's value is enforcing the output format, not the analysis.

  • Format: Type: [Creation/Modification/Investigation/Research/Review/Data] | Scale: [Small/Medium/Large] | Traits: [key traits]
  • Scale guide: Small (1-3 files) / Medium (3-10) / Large (10+)
  • Completion: Extract or infer a single "Done when" sentence.
  • Scale=Small? Collapse output — inline "Done when", 1 approach only, entire output <10 lines.

→ Output: Task Profile line + "Done when" sentence.


Phase 3: Capability Matching

From Phase 1, highlight only what's relevant to this task. The model would not know MCP tools exist without this scan.

  • Minimum 2 items, maximum 8
  • If nothing beyond native tools is relevant: "Relevant Capabilities: native tools sufficient"

→ Output: "Relevant Capabilities" bullet list.


Phase 4: Suggest Options

Present tool compositions as options (model may follow, ignore, or adapt).

  • Maximum 3 options with different tradeoffs (safety vs speed vs depth)
  • Only tools discovered in Phase 1 (uninstalled tools → Phase 5)
  • Mark one "Recommended"; state model's judgment prevails
  • Scale=Small: 1 option only
  • Each option = concrete tool chain (Tool -> Tool -> Tool) + "Good for" line + optional "Agent" line
  • Adapt based on installed skills/MCP servers/agents discovered in Phase 1
  • Agent recommendation: If a discovered agent (from ~/.claude/agents etc.) fits the task better than the default model, name it. If no custom agent is relevant, omit the Agent line — don't force it.

→ Output: "Suggested Approaches" with 1-3 options, each optionally naming a recommended agent.


Show full SKILL.md (229 more words)Show less

Phase 5: Capability Gap

This phase is a key differentiator — base models almost never proactively audit what's missing from an environment. Be thorough here.

  • Suggest not installed but useful tools, MCP servers, or skills.
  • Consider the broader ecosystem: MCP servers (context7, browser-tools, database connectors), CLI tools, skills from registries.
  • Always state "the task is doable without these."
  • Installation only after explicit user approval.
  • If nothing missing: "N/A — environment sufficient."

→ Output: table of Tool / Why useful / Install, or "N/A".


Phase 6: Performance Tips

If any of these apply, mention them in 1-2 bullets. Otherwise output "N/A". Keep brief — models increasingly handle parallelization natively.

  • Parallel: 2+ independent steps in one message
  • Background: A step takes >30s → run_in_background
  • Subagent: Independent research can be delegated

Output Format

Full (Scale=Medium or Large)
markdown
## Tool Advisor v3.5

Prompt: `$ARGUMENTS`

### Your Environment
| Layer | Available |
|-------|-----------|
| MCP Servers | [discovered] |
| Skills | [name: description, ...] |
| Agents | [name: description, ...] |
| Plugins | [discovered] |
| CLI | [discovered] |

### Task Profile
- **Type**: [type] / **Scale**: [scale] / **Traits**: [traits]
- **Done when**: [one sentence]

### Relevant Capabilities
- `[tool]` — [why relevant]

### Suggested Approaches

**A — Methodical** (Recommended)
[step -> step -> step]
Good for: [tradeoff]
Agent: [agent name if a discovered agent fits — omit if none relevant]

**B — Fast**
[step -> step -> step]
Good for: [tradeoff]
Agent: [agent name if relevant — omit if none]

**C — [Deep/Skill-enhanced/Agent-parallel]**
[step -> step -> step]
Good for: [tradeoff]
Agent: [agent name if relevant — omit if none]

### Performance Tips
- [only applicable tips, or N/A]

### Missing but Useful
| Tool | Purpose | Install |
|------|---------|---------|
| [tool] | [purpose] | [how] |

(Task is doable without these. Or: N/A — environment sufficient.)

---

## Quick Action
| Approach | First Step |
|----------|-----------|
| Methodical | `[copy-paste command]` |
| Fast | `[copy-paste command]` |
| [Third] | `[copy-paste command]` |

**-> Recommended: "[approach]"** ([one-line reason])
Collapsed (Scale=Small)
markdown
## Tool Advisor v3.5

Prompt: `$ARGUMENTS`
Env: [key tools] | Done when: [criteria]

**Approach**: [single flow] | First step: `[copy-paste command]`

After outputting the template: STOP.


Anti-Patterns

  • Don't read/debug source code — recommend Task(Explore) or code-reviewer instead
  • Don't execute (git, edit, write) — put commands in Quick Action table
  • Don't skip phases or write prose analysis — complete all 6 phases, present conclusions only

Examples

Small Task (collapsed)

Input: Fix the typo in README

markdown
## Tool Advisor v3.5

Prompt: `Fix the typo in README`
Env: native tools | Done when: typo corrected, no other changes

**Approach**: Glob("**/README*") -> Read -> Edit | First step: `Glob("**/README*")`
Medium Task (full — abbreviated)

Input: US dashboard 'AI보유 분석' tab has no data. Fix generate_us_dashboard_json.py

Expected: full template with environment scan, Task Profile (Modification / Small-Medium / Cross-reference KR version), 3 approaches (Methodical: Explore->Read->executor->test, Fast: Grep->Read->Edit->test, Agent-parallel: parallel Explore->diff->fix->test), Quick Action table with copy-paste first steps. Then STOP.


Prompt to analyze: $ARGUMENTS

© hashgraph-online, Apache-2.0. 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 plugins/dragon1086/claude-skills/skills/tool-advisor of hashgraph-online/awesome-codex-plugins.

  • SKILL.md
  • agents/openai.yaml

Open the folder on GitHubat commit 78497e5

Compare with similar skills

Tool Advisor 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.

Tool Advisor compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Tool Advisor this skillhashgraph-online/awesome-codex-plugins1.2k—~2.8kAutomated safety check: NotesApache-2.0
Product Capabilityaffaan-m/ECC275k2 repos~1.1kAutomated safety check: PassMIT
Discover Pluginsruvnet/ruflo74k—~2kAutomated safety check: NotesMIT
Artifact Capabilitiesasgeirtj/system_prompts_leaks69k—~4.3kAutomated safety check: PassCC0-1.0
Advisor Modecursor/plugins10k—~2.6kAutomated safety check: NotesNone
Product Capabilityaffaan-m/ECC275k—~439Automated safety check: PassMIT

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Questions about Tool Advisor

What does Tool Advisor do?

Discovers your full tool environment and amplifies prompts with capability awareness. Tool Advisor is an agent skill from hashgraph-online/awesome-codex-plugins. Discovers your full tool environment and amplifies prompts with capability awareness.

When should I use Tool Advisor?

Tool Advisor fits situations like: the user asks what tools should I use; best approach for this task; how should I tackle; explicitly mentions tool-advisor / $tool-advisor / ta.

How do I install Tool Advisor in Claude Code?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill tool-advisor -a claude-code`. Or copy the skill folder (plugins/dragon1086/claude-skills/skills/tool-advisor in hashgraph-online/awesome-codex-plugins) into .claude/skills/tool-advisor in your project. Claude Code loads it when a task matches its description.

How do I install Tool Advisor in Codex?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill tool-advisor -a codex`. Or copy the skill folder (plugins/dragon1086/claude-skills/skills/tool-advisor in hashgraph-online/awesome-codex-plugins) into .agents/skills/tool-advisor in your project. Codex loads it when a task matches its description.

Can I use Tool Advisor 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 hashgraph-online/awesome-codex-plugins --skill tool-advisor -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tool-advisor, .gemini/skills/tool-advisor, .github/skills/tool-advisor and .opencode/skills/tool-advisor in your project.

What does Tool Advisor need to run?

Going by SKILL.md and its folder, Tool Advisor needs the command-line tools its instructions call (python3). Our summary lists: Python 3; Docker.

Does Tool Advisor 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 Tool Advisor safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Tool Advisor use?

Tool Advisor is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Tool Advisor use?

About 2.8k tokens (SKILL.md is roughly 11k 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 Tool Advisor?

Skills that share tags, products or a category with Tool Advisor: Product Capability (affaan-m/ECC, 275k stars), Discover Plugins (ruvnet/ruflo, 74k stars), Artifact Capabilities (asgeirtj/system_prompts_leaks, 69k stars) and Advisor Mode (cursor/plugins, 10k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Tool Advisor?

hashgraph-online (a GitHub organization) maintains it in hashgraph-online/awesome-codex-plugins, which has 1,242 GitHub stars. The repository holds 686 skills in this directory. The repository was last updated on October 8, 2026.

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