Agent Builder
shareAI-lab/learn-claude-code
Design and build AI agents for any domain. An agent skill from shareAI-lab/learn-claude-code.
Executes agent-enhanced council queries as one Workflow of parallel Claude analyst agents that each query a provider, evaluate response quality, ask follow-up questions, and return a schema-enforced…
$ npx skills add hex/claude-council --skill deep-execution -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install hex/claude-council deep-execution --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/hex/claude-council.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/deep-execution .claude/skills/deep-execution && rm -rf skills-srcUse ~/.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/
Install the "deep-execution" agent skill from https://github.com/hex/claude-council/tree/main/skills/deep-execution into .claude/skills/deep-execution/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-execution", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/hex/claude-council/tree/main/skills/deep-executionType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add hex/claude-council --skill deep-execution -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install hex/claude-council deep-execution --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/hex/claude-council.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/deep-execution .agents/skills/deep-execution && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "deep-execution" agent skill from https://github.com/hex/claude-council/tree/main/skills/deep-execution into .agents/skills/deep-execution/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-execution", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add hex/claude-council --skill deep-execution -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install hex/claude-council deep-execution --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/hex/claude-council.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/deep-execution .cursor/skills/deep-execution && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "deep-execution" agent skill from https://github.com/hex/claude-council/tree/main/skills/deep-execution into .cursor/skills/deep-execution/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-execution", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/hex/claude-council.git --path skills/deep-execution--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add hex/claude-council --skill deep-execution -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install hex/claude-council deep-execution --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/hex/claude-council.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/deep-execution .gemini/skills/deep-execution && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "deep-execution" agent skill from https://github.com/hex/claude-council/tree/main/skills/deep-execution into .gemini/skills/deep-execution/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-execution", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install hex/claude-council deep-executionInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add hex/claude-council --skill deep-execution -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/hex/claude-council.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/deep-execution .github/skills/deep-execution && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "deep-execution" agent skill from https://github.com/hex/claude-council/tree/main/skills/deep-execution into .github/skills/deep-execution/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-execution", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add hex/claude-council --skill deep-execution -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install hex/claude-council deep-execution --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/hex/claude-council.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/deep-execution .opencode/skills/deep-execution && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "deep-execution" agent skill from https://github.com/hex/claude-council/tree/main/skills/deep-execution into .opencode/skills/deep-execution/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-execution", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
deep-executionExecutes agent-enhanced council queries as one Workflow of parallel Claude analyst agents that each query a provider, evaluate response quality, ask follow-up questions, and return a schema-enforced…
Deep Execution is an agent skill from hex/claude-council. Executes agent-enhanced council queries as one Workflow of parallel Claude analyst agents that each query a provider, evaluate response quality, ask follow-up questions, and return a schema-enforced analysis with confidence ratings and blind spot analysis. Invoked when the --agents flag is used or when complex architectural decisions are detected.
Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `agent-prompt-template.md`).
It sits in AI & LLM Engineering. The repository describes itself as: Claude Code plugin that asks several AI coding agents the same question and shows their answers side by side. Gemini, OpenAI, Grok, Perplexity, Kimi and any model OpenRouter… The licence is MIT.
6 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 23867e1. It shows what the files ask for, not the result of running them.
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.
No scripts in the folder and no shell commands in SKILL.md (its code samples are bash, javascript and json).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Deep Execution loads about 2.5k tokens when it runs. Until then it costs about 91 tokens; SKILL.md has 756 words of instructions outside code blocks.
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.
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.
The full file from hex/claude-council at commit 23867e1, republished under its MIT licence (© hex). 756 words, ~2,535 tokens.
.claude/skills/deep-execution/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.Run one Workflow of parallel Claude analyst agents for deeper analysis. Each analyst queries its provider, evaluates response quality, can ask follow-up questions, and returns a structured analysis that the workflow enforces against the schema.
One shell call resolves what the workflow needs — each provider's model, its
role, and the question file it will send — with the same helpers the standard
flow uses. Paste the final question into the heredoc verbatim: the quoted
marker means the shell does NOT interpret quotes, backticks or $() in it.
The question is emitted once; each provider's role-injected variant is built
in shell, not pasted again.
source "${CLAUDE_PLUGIN_ROOT}/scripts/lib/providers.sh"
source "${CLAUDE_PLUGIN_ROOT}/scripts/lib/roles.sh"
PROVIDERS=(<the selected providers, space separated>)
ROLES="<the --roles value, or empty>"
RUN=$(date +%s) # names this run's files; Step 6 reuses it
Q="$PWD/.claude/council-cache/.agents-$RUN"
mkdir -p "$PWD/.claude/council-cache"
# Self-ignoring, as cache.sh and run-council.sh keep it: these files carry the
# question and any --file contents, and must never land in a commit.
[[ -f "$PWD/.claude/council-cache/.gitignore" ]] || printf '*\n' > "$PWD/.claude/council-cache/.gitignore"
cat > "$Q.txt" <<'COUNCIL_Q_EOF'
<the question, verbatim>
COUNCIL_Q_EOF
# --file / auto-context: append the context to the question file here, e.g.
# { printf '\n\nHere is the content of %s:\n\n```\n' "<path>"; cat "<path>"; printf '```\n'; } >> "$Q.txt"
# An unknown role is refused, as the standard flow refuses it; assigning it
# would hand that provider the bare question under a role heading.
[[ -z "$ROLES" ]] || validate_roles "$ROLES" || exit 1
# The model the ANALYST agents run on — distinct from the provider models below,
# which are what actually answer the question. Pinned rather than inherited: an
# omitted model makes every analyst run on whatever model the user's session
# happens to use, so the cost of a mode that already fans out one agent per
# provider varies by a factor of several for no stated reason. Sonnet handles
# the analyst's work — run a query, judge the answer, emit a structured object.
ANALYST_MODEL="${COUNCIL_AGENT_MODEL:-sonnet}"
case "$ANALYST_MODEL" in
sonnet|opus|haiku|fable) ;;
*) echo "COUNCIL_AGENT_MODEL must be one of: sonnet, opus, haiku, fable (got '$ANALYST_MODEL')" >&2; exit 1 ;;
esac
echo "analyst model $ANALYST_MODEL"
ASSIGNMENTS=""
[[ -n "$ROLES" ]] && ASSIGNMENTS=$(assign_roles_to_providers "$ROLES" "${PROVIDERS[@]}")
echo "run $RUN"
for p in "${PROVIDERS[@]}"; do
role=""
[[ -n "$ASSIGNMENTS" ]] && role=$(get_provider_role "$p" "$ASSIGNMENTS")
qf="$Q.txt"
if [[ -n "$role" ]]; then
qf="$Q-$p.txt"
build_prompt_with_role "$(cat "$Q.txt")" "$role" > "$qf"
fi
printf '%s\t%s\t%s\n' "$p" "$(get_model "$p")" "$qf"
doneIt prints the run id, then the analyst model, then one line per provider: name,
model (shown in the Step 4 header), and the absolute path of that provider's
question file. Pass the analyst model to the workflow as analystModel; the
workflow script cannot read the environment itself.
Agent mode is one Workflow: one analyst agent per provider, in parallel, each
returning its analysis through schema-enforced structured output. The user
asked for agent mode (--agents, or yes to the prompt in ask.md Step 1.5),
which is the opt-in the Workflow tool requires.
If the Workflow tool is not available in this session, stop here: tell the
user that --agents needs a Claude Code with the Workflow tool, and offer to
run the same question in standard mode instead. Do not fall back to another
way of spawning agents.
Read ${CLAUDE_PLUGIN_ROOT}/schemas/agent-analysis.schema.json with the Read
tool, then call the Workflow tool with this script, verbatim, via script:
export const meta = {
name: 'council-agents',
description: 'One analyst per council provider: query it, judge the answer, follow up, return a structured analysis',
phases: [{ title: 'Analyze', detail: 'one analyst agent per provider, in parallel' }],
}
// The tool's schema validator does not know the draft-2020-12 dialect the
// file declares; without the declaration it validates the same keywords fine.
const schema = { ...args.schema }
delete schema.$schema
const template = `${args.pluginRoot}/skills/deep-execution/agent-prompt-template.md`
// Single stage: there is no later step for a finished analyst to move on to,
// so the barrier costs nothing.
const results = await parallel(args.providers.map(p => () =>
agent(
`You are the council analyst for the provider "${p.name}".\n` +
`Read ${template} and carry out every step in it, with these values:\n` +
`- {PROVIDER} = ${p.name}\n` +
`- {PLUGIN_ROOT} = ${args.pluginRoot}\n` +
`- {QUESTION_FILE} = ${p.questionFile}\n` +
`Your final answer is the Round 3 analysis object, returned through the structured output tool.`,
{ label: p.name, phase: 'Analyze', schema, agentType: 'general-purpose',
model: args.analystModel })))
// parallel() keeps a dead or skipped analyst's slot as null, index-aligned with args.providers.
const failed = args.providers.filter((_, i) => !results[i]).map(p => p.name)
if (failed.length) log(`no analysis from: ${failed.join(', ')}`)
return {
// The label goes last so an analyst that emits its own "provider" key cannot rename itself.
analyses: results.map((a, i) => a && { ...a, provider: args.providers[i].name }).filter(Boolean),
failed,
}and these args (real JSON values, not a string; pluginRoot is the real
path of ${CLAUDE_PLUGIN_ROOT}, questionFile the path Step 1 printed):
{
"pluginRoot": "<CLAUDE_PLUGIN_ROOT>",
"analystModel": "<the model Step 1 printed>",
"schema": { "...the parsed schema file..." },
"providers": [
{ "name": "gemini", "questionFile": "<absolute path from Step 1>" }
]
}The workflow returns { analyses, failed }. Every object in analyses
satisfies the schema; failed names the providers whose analyst died or was
skipped. A provider that returned an error is not in failed: its analyst
reports it as a quality: poor, confidence: low analysis whose
full_response is the error text (the template says so). Step 5 treats both
as the same thing — a provider that did not answer.
Nothing to validate: use each analysis's fields directly in Steps 4-5, and
carry failed into Step 5's provider failures.
For each analysis, display it using this format. {MODEL} is the Step 1
model: a model-fallback re-run inside an analyst cannot change this header,
and the displacement is visible only in the analysis text.
## {EMOJI} {PROVIDER} ({MODEL}) — Agent Analysis
**Quality**: {quality} | **Confidence**: {confidence} | **Retried**: {retried}
### Key Recommendations
{recommendations}
### Unique Perspective
{unique_perspective}
### Blind Spots
{blind_spots}
---
<details>
<summary>Full {PROVIDER} Response</summary>
{full_response}
</details>Provider emojis (ALWAYS use emoji + space):
With pre-analyzed responses, generate a richer synthesis than the standard mode.
The calibration rules in ${CLAUDE_PLUGIN_ROOT}/prompts/synthesis.md apply here
too. Its "returned an error" case covers the providers in failed AND every
quality: poor analysis whose full_response is a provider error: name them
under failures and keep them out of consensus, whatever weight their
confidence field would give them.
Weight agreement by each provider's confidence level. High-confidence agreement is stronger signal than low-confidence agreement — but only about the reasoning, never about the premises. Every provider read the same description of a system none of them can inspect, so confident unanimity can equally mean the question asserted something false and each provider reasoned from it correctly. When agreement is both broad and confident, say so and then name the premise the whole answer rests on, and whether anyone was in a position to check it.
Cross-reference each provider's blind spots against other providers' recommendations. Flag risks that NO provider considered.
Where providers disagree, explain WHY they likely diverge (different assumptions, different optimization targets, different risk tolerance).
Synthesize the strongest approach, noting which providers support it and at what confidence level.
Save the complete output (all provider analyses + synthesis) to
.claude/council-cache/council-agents-{RUN}.md, where RUN is the run id
Step 1 printed (the directory exists since Step 1). Then remove the run's
question files, which nothing prunes:
rm -f .claude/council-cache/.agents-{RUN}*Tell the user:
Full agent analysis saved to
.claude/council-cache/council-agents-{RUN}.md
failed), say so and continue with the othersscriptPath and resumeFromRunId from the tool result; the finished analysts return from cache© hex, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 1 other file in skills/deep-execution of hex/claude-council.
Open the folder on GitHubat commit 23867e1
Deep Execution 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Deep Execution this skillhex/claude-council | 848 | — | ~2.5k | Automated safety check: Pass | MIT | |
| Agent BuildershareAI-lab/learn-claude-code | 78k | 6 repos | ~1.2k | Automated safety check: Pass | MIT | |
| Add Uint Supportpytorch/pytorch | 104k | 2 repos | ~2.3k | Automated safety check: Pass | Custom licence | |
| Peft Fine TuningOrchestra-Research/AI-Research-SKILLs | 13k | 9 repos | ~3.1k | Automated safety check: Pass | MIT | |
| Segment Anything Model GuideOrchestra-Research/AI-Research-SKILLs | 13k | 9 repos | ~3.3k | Automated safety check: Pass | MIT | |
| 1passwordtrpc-group/trpc-agent-go | 1.8k | 15 repos | ~656 | Automated safety check: Pass | Apache-2.0 |
shareAI-lab/learn-claude-code
Design and build AI agents for any domain. An agent skill from shareAI-lab/learn-claude-code.
pytorch/pytorch
Add unsigned integer (uint) type support to PyTorch operators by updating ATDISPATCH macros.
Orchestra-Research/AI-Research-SKILLs
Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods.
Orchestra-Research/AI-Research-SKILLs
Guide to using Meta's Segment Anything Model for zero-shot image segmentation with point, box or mask prompts, or automatic mask generation.
trpc-group/trpc-agent-go
Set up and use 1Password CLI (op). An agent skill from trpc-group/trpc-agent-go.
Orchestra-Research/AI-Research-SKILLs
Shows how to store documents and embeddings in Chroma, query them by similarity with metadata filters, and persist them to disk for RAG and semantic search projects.
hex/claude-council
Runs a local council when no external AI providers are configured.
hex/claude-council
Executes council queries by running the query pipeline across selected AI providers (Gemini, OpenAI, Grok, Perplexity), displaying formatted responses verbatim, and generating a synthesis of…
hex/claude-council
Adds new AI providers to claude-council, configures provider API settings, troubleshoots provider connections, and documents the provider script interface.
hex/claude-council
A bug reproduces, the expected behaviour is clear, and the fix stands apart from the current work.
hex/claude-council
A CI job fails on a named revision and the fix belongs in code, config or tests, not in the CI provider.
hex/claude-council
Docs must be updated or restructured to match code that already changed: README, reference pages, CLI help or config docs.
Categories
Executes agent-enhanced council queries as one Workflow of parallel Claude analyst agents that each query a provider, evaluate response quality, ask follow-up questions, and return a schema-enforced…. Deep Execution is an agent skill from hex/claude-council. Executes agent-enhanced council queries as one Workflow of parallel Claude analyst agents that each query a provider, evaluate response quality, ask follow-up questions, and return a schema-enforced analysis with confidence ratings and blind spot analysis.
Deep Execution fits situations like: AI & LLM Engineering work in your project.
Run `npx skills add hex/claude-council --skill deep-execution -a claude-code`. Or copy the skill folder (skills/deep-execution in hex/claude-council) into .claude/skills/deep-execution in your project. Claude Code loads it when a task matches its description.
Run `npx skills add hex/claude-council --skill deep-execution -a codex`. Or copy the skill folder (skills/deep-execution in hex/claude-council) into .agents/skills/deep-execution in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add hex/claude-council --skill deep-execution -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/deep-execution, .gemini/skills/deep-execution, .github/skills/deep-execution and .opencode/skills/deep-execution in your project.
SKILL.md names no scripts, command-line tools or credentials: Deep Execution is instructions for the agent only.
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.
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.
Deep Execution is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.5k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Deep Execution: Agent Builder (shareAI-lab/learn-claude-code, 78k stars), Add Uint Support (pytorch/pytorch, 104k stars), Peft Fine Tuning (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Segment Anything Model Guide (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
hex (a GitHub user) maintains it in hex/claude-council, which has 848 GitHub stars. The repository holds 10 skills in this directory. The repository was last updated on October 6, 2026.
Source: hex/claude-council on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.