MCP Server Builder
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
Entry point for new or resumed work. An agent skill from hashgraph-online/awesome-codex-plugins.
$ npx skills add hashgraph-online/awesome-codex-plugins --skill ingest -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install hashgraph-online/awesome-codex-plugins ingest --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/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/ariaxhan/kernel-claude/skills/ingest .claude/skills/ingest && 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 "ingest" agent skill from https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/ariaxhan/kernel-claude/skills/ingest into .claude/skills/ingest/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ingest", 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/hashgraph-online/awesome-codex-plugins/tree/main/plugins/ariaxhan/kernel-claude/skills/ingestType 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 hashgraph-online/awesome-codex-plugins --skill ingest -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install hashgraph-online/awesome-codex-plugins ingest --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/ariaxhan/kernel-claude/skills/ingest .agents/skills/ingest && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ingest" agent skill from https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/ariaxhan/kernel-claude/skills/ingest into .agents/skills/ingest/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ingest", 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 hashgraph-online/awesome-codex-plugins --skill ingest -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install hashgraph-online/awesome-codex-plugins ingest --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/ariaxhan/kernel-claude/skills/ingest .cursor/skills/ingest && 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 "ingest" agent skill from https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/ariaxhan/kernel-claude/skills/ingest into .cursor/skills/ingest/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ingest", 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/hashgraph-online/awesome-codex-plugins.git --path plugins/ariaxhan/kernel-claude/skills/ingest--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 hashgraph-online/awesome-codex-plugins --skill ingest -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install hashgraph-online/awesome-codex-plugins ingest --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/ariaxhan/kernel-claude/skills/ingest .gemini/skills/ingest && 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 "ingest" agent skill from https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/ariaxhan/kernel-claude/skills/ingest into .gemini/skills/ingest/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ingest", 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 hashgraph-online/awesome-codex-plugins ingestInstalls 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 hashgraph-online/awesome-codex-plugins --skill ingest -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/ariaxhan/kernel-claude/skills/ingest .github/skills/ingest && 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 "ingest" agent skill from https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/ariaxhan/kernel-claude/skills/ingest into .github/skills/ingest/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ingest", 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 hashgraph-online/awesome-codex-plugins --skill ingest -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install hashgraph-online/awesome-codex-plugins ingest --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/ariaxhan/kernel-claude/skills/ingest .opencode/skills/ingest && 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 "ingest" agent skill from https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/ariaxhan/kernel-claude/skills/ingest into .opencode/skills/ingest/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ingest", 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.
ingestEntry point for new or resumed work. An agent skill from hashgraph-online/awesome-codex-plugins.
Ingest is an agent skill from hashgraph-online/awesome-codex-plugins. Entry point for new or resumed work. Researches and scopes new tasks; validates and resumes handoff/checkpoint manifests. Triggers: start, begin, do, implement, build, fix, create, resume, continue.
Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Agent Workflows. 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.
5 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 78497e5. It shows what the files ask for, not the result of running them.
Pre-approves these tools, so the agent can use them without asking each time:
ReadBashGrepGlobTaskWebSearchWebFetchFrom allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are bash).
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.
Ingest loads about 3k tokens when it runs. Until then it costs about 51 tokens; SKILL.md has 1,363 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 noted patterns worth knowing about, such as sudo or a known installer.
allowed-tools: Read, Bash, Grep, Glob, Task, WebSearch, WebFetchAutomated 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 hashgraph-online/awesome-codex-plugins at commit 78497e5, republished under its Apache-2.0 licence (© hashgraph-online). 1,363 words, ~3,026 tokens.
.claude/skills/ingest/SKILL.md (or your agent's skills folder).<skill id="ingest">
<purpose>
Unified entry for new and resumed work.
New task: READ → CLASSIFY → RESEARCH → SCOPE → TESTS → EXECUTE → LEARN (human confirms each phase).
Resume: DISCOVER → VALIDATE → DIVERGENCE → COMPILE (bounded context + receipt) → RESUME AT PHASE.
For autonomous loop: /kernel:forge
Authority order (highest wins) — a manifest is a map, not the territory:
</purpose>
<skill_load> always: skills/debug/SKILL.md on_classify: bug: skills/debug/SKILL.md feature: skills/build/SKILL.md, skills/architecture/SKILL.md refactor: skills/architecture/SKILL.md review: skills/review/SKILL.md on_domain: frontend: skills/frontend/SKILL.md app: skills/app-dev/SKILL.md on_tier: 2+: skills/orchestration/SKILL.md reference: skills/build/reference/build-research.md </skill_load>
<on_start>
agentdb read-start
ls _meta/research/ # check prior workLoad /kernel:quality, /kernel:testing, /kernel:git immediately. After classify: load task-specific skills above. Do NOT proceed without loading them. After scope: if tier 2+, load /kernel:orchestration. If any domain detected (API, auth, frontend, backend): load domain skills. </on_start>
<step id="1_classify">
task: what user wants (one sentence)
type: bug|feature|refactor|question|verify|resume|review
familiar: yes|no
Search before asking: Glob, Grep, common paths.
<ask_user> Use AskUserQuestion when: classification is ambiguous (could be bug or feature, refactor or rewrite) Ask: "This looks like {type_A} but could be {type_B}. Which framing fits your intent?" Options: type_A, type_B, or clarify </ask_user>
After classify: load matching workflow from workflows/{type}.md if it exists.
Workflow steps guide the phase sequence. Human confirms at each step (ingest mode).
</step>
<branch after="classify">
IF type == resume (or a manifest path was supplied) → go to MANIFEST RESUME (below)
IF familiar AND tier_likely_1 → skip to step 3 (scope), mark research="skipped (familiar)"
IF unfamiliar OR complex → proceed to step 2 (research)
ALWAYS: check _meta/research/ cache regardless (cache != full research)
</branch>
<step id="1b_manifest_resume" trigger="classify.type == resume">
Resume from a kernel.handoff/v1 or kernel.checkpoint/v1 manifest. The runtime CLI:
`KM="${CLAUDE_PLUGIN_ROOT:-.}/orchestration/manifest/kernel-manifest"`
Discover: explicit path if the user gave one, else:
"$KM" latest # newest across _meta/checkpoints/ + _meta/handoffs/Legacy markdown handoffs (_meta/handoffs/*.md) remain readable this release: parse goal/decisions/next-steps from prose, note "legacy handoff (deprecated, no validation/divergence/budget)" and suggest regenerating as JSON. Removal path: docs/MIGRATION-8.md.
Validate — a manifest that does not validate is not resumed:
"$KM" validate <manifest> # exit 2 (no parser) on a sealed manifest = STOPDivergence — live state wins over manifest claims:
"$KM" divergence <manifest> --jsonTyped divergence events apply workflow.invalidation_rules[].when and return
recalculated phase statuses. Never trust an inherited phase whose inputs changed.
Preflight: run "$KM" preflight <manifest>. Canonical state permits only typed
current-branch, path-exists, and allowlisted argv checks; raw shell is invalid.
Compile bounded context — read the bundle, not the raw tree:
"$KM" compile <manifest> --bundle-out /tmp/resume-bundle.md --receipt-out _meta/reports/receipt-{date}.jsonThe receipt (kernel.context-receipt/v1) reports estimated tokens per layer and status: within_budget → proceed · target_exceeded → drop optional selectors, proceed with a note · maximum_exceeded (exit 3) → STOP, report the receipt, ask before loading anything.
Activate the policy (arms the guard-context hook for sealed/bounded):
"$KM" activate <manifest>sealed: forbidden globs are hook-BLOCKED; do not fight the hook — amend the manifest if access is genuinely needed. bounded: extra loads are allowed but ledgered; justify each in the receipt's loads_beyond_manifest.
Resume at the declared position:
"$KM" resume <manifest> # entry_phase / entrypoint / next_operationSkip inherited phases (already verified by divergence), execute required ones. Honor execution.stop_conditions and emit checkpoints at execution.checkpoints.
Complete: when outputs.required are verified,
"$KM" deactivate --receipt _meta/reports/receipt-{date}.jsonDeactivate projects the receipt into AgentDB's observational context graph (shadow
telemetry). Then outputs.completion (usually agentdb write-end), which records session
outcome on that graph row when did/blocked are present.
Optional advisory (never auto-loads):
bash agentdb graph-suggest {task_type}
Output: "Resuming {manifest}: {goal}. Entry: {entry_phase}. Receipt: {total_estimated_tokens} tokens ({status})."
</step>
<step id="2_research" mandatory="true">
**RULE: Research without verification is theory fiction.** Every research finding must be verified
with a minimal test, prototype, or proof before it drives implementation. 8 research agents and
6 docs mean nothing if nobody built a test to prove the approach works. (LRN-F11)
<substeps>
1. Check existing: ls _meta/research/, agentdb query
2. anti_patterns FIRST: "{tech} not working", "{tech} gotchas"
3. Solutions: official docs, GitHub issues, Stack Overflow
4. Built-in check: framework > stdlib > npm package
5. **Verify**: build minimal proof (test screen, script, unit test) before committing to approach
6. Write to: _meta/research/{topic}.md (include verification result)
</substeps>
<format>
# {Topic} Research
## Anti-Patterns
1. {pattern}: {why} → {fix}
## Proven Solution
- package: {name}@{version}
## Sources
- {urls}
</format>
tier 2+: spawn kernel:researcher
<ask_user>
Use AskUserQuestion when: research reveals multiple viable approaches or unknown risks
Ask: "Research found {N} approaches. Proceed with {recommended}, or explore alternatives?"
Options: proceed, explore alternatives, skip research
</ask_user>
</step>
<step id="3_scope">
files:
1: {path} - {what changes}
count: N
tier: 1|2|3
<tiers>
1: reversible + loud if wrong → execute inline
2: persistent or moderately quiet → plan, execute inline; delegate a surgeon only for heavy file-disjoint work; verify
3: hard to undo, quiet if wrong, or wide blast radius → contract + surgeon + adversary
ambiguous: assume higher. File count is only a weak hint, never the trigger.
</tiers>
<ask_user> Use AskUserQuestion when: tier classification is borderline (e.g., 2-3 files but complex coupling) Ask: "Scoped to {N} files — tier {X}. Confirm tier, or should I treat as tier {X+1}?" Options: confirm tier {X}, bump to tier {X+1}
When the request itself is underspecified (any GOAL/CONSTRAINTS/INPUTS/OUTPUTS/DONE-WHEN
field unknowable), run the structured interview from skills/build/SKILL.md "The
interview" BEFORE scoping: batched AskUserQuestion rounds over intent, implementation
forks, veto-risk UX, edge behavior, and tradeoffs. Bounded choices only; open-ended
direction stays prose. Answers are quoted into the spec/commission so each decision
carries its authority.
</ask_user>
</step>
<branch after="scope">
IF scope reveals unknowns not covered by research → loop to step 2 with narrowed query
IF scope is clear → proceed to step 4
</branch>
<step id="4_tests" mandatory="true">
<rule>Define success before coding. Tests first.</rule>
skill_ref: skills/build/reference/testing.md
done_when:
evals: code_grader: PASS/FAIL command regression: existing tests pass
<principles>
mock_boundaries_only: external APIs, DBs
edge_cases_first: null, empty, boundary, timeout
strong_assertions: specific values
</principles>
</step>
<step id="4b_spec_completeness" mandatory="true">
<rule>Spec framing > contract framing. Execution-ready, not goal-shaped.</rule>
Specification prompts with exact code achieve 100% success across all scopes (modelmind H002/H003, 0.95 confidence). Contract framing ("achieve X under constraint Y") leaves interpretation gaps that agents fill incorrectly.
Before handing to surgeon (tier 2+) or starting execution (tier 1), the spec must answer:
Litmus test: could a fresh agent in a new session execute this spec with zero follow-up questions? If no, the spec is incomplete. Return to step 3 (scope) or step 4 (tests) and fill the gap before proceeding.
Anti-pattern: shipping a contract that says "the surgeon will figure out X." The surgeon will figure out X by guessing, and the guess will be wrong.
<ask_user>
Use AskUserQuestion when: the spec has a known gap and you need the user to decide which
exact path to take (rather than letting the surgeon guess).
Ask: "Spec gap at {location}: option A = {exact}, option B = {exact}. Which?"
Options: option A, option B, other
</ask_user>
</step>
<step id="5_execute">
<tier_1>
1. Reference research doc
2. Write failing tests (edge cases!)
3. Implement proven pattern
4. Check Big 5: skills/quality/SKILL.md
5. Run evals → /kernel:validate before commit
6. Commit when done_when satisfied
</tier_1>
<tier_2_plus> rule: you do NOT write code
</step>
<branch after="execute">
IF adversary rejects (tier 3) → return to execute with adversary feedback, max 3 retries
IF tests fail → /kernel:debug, fix, re-execute
IF blocked → checkpoint and STOP, ask human
</branch>
<step id="6_learn" mandatory="true">
<rule>Every task teaches. Capture or lose.</rule>
agentdb learn pattern "{what worked}" "{evidence}" agentdb learn failure "{what broke}" "{evidence}" Update _meta/research/ if new findings. Suggest /kernel:retrospective if 5+ learnings accumulated since last synthesis. Long task still running? Emit /kernel:checkpoint at natural boundaries instead of letting context accumulate (EXP-L21).
<checkpoint>
agentdb write-end '{"task":"X","tier":N,"learned":["Z"]}'
MUST run before session ends.
</checkpoint>
</step>
<output_format> task: one sentence | type: bug|feature|refactor | tier: 1|2|3 | status: researching|scoping|testing|executing|complete </output_format>
<hard_stops> ask_file_location→search | code_without_research→step2 | code_without_tests→step4 | code_tier2+→surgeon | skip_agentdb→go_back </hard_stops>
</skill>
© 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
Just SKILL.md in plugins/ariaxhan/kernel-claude/skills/ingest of hashgraph-online/awesome-codex-plugins.
Open the folder on GitHubat commit 78497e5
Ingest 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 |
|---|---|---|---|---|---|---|
| Ingest this skillhashgraph-online/awesome-codex-plugins | 1.2k | — | ~3k | Automated safety check: Notes | Apache-2.0 | |
| MCP Server Builderanthropics/skills | 180k | 64 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| Hook Development for Claude Code Pluginsanthropics/claude-plugins-official | 38k | 11 repos | ~4.1k | Automated safety check: Notes | Apache-2.0 | |
| Using Superpowersfarm-fe/farm | 5.6k | 35 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Executing Plans Inlineobra/superpowers | 296k | 2 repos | ~5.1k | Automated safety check: Pass | MIT | |
| Claude Code Agent Developmentanthropics/claude-plugins-official | 38k | 8 repos | ~2.8k | Automated safety check: Pass | Apache-2.0 |
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
anthropics/claude-plugins-official
Explains how to write Claude Code plugin hooks, both prompt-based checks and bash commands, for events such as PreToolUse, Stop and SessionStart.
farm-fe/farm
A skill your agent uses when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions
obra/superpowers
Has the agent carry out an implementation plan itself, task by task in the current session, keeping a ledger, proving each step with a test and ending with one whole-branch review.
anthropics/claude-plugins-official
Explains how to write agents for Claude Code plugins: the markdown file with YAML frontmatter, trigger descriptions, model and color settings, and system prompt design.
Azure/azqr
Create new skills, modify and improve existing skills, and measure skill performance.
hashgraph-online/awesome-codex-plugins
Create original anime-style reaction stickers as looping GIFs and MP4 previews, using generated character pose sheets and timed key poses.
hashgraph-online/awesome-codex-plugins
Manage and query Calibre libraries with the calibredb CLI (local paths or Calibre Content server URLs).
hashgraph-online/awesome-codex-plugins
A skill your agent uses when adding, changing, testing, or debugging Rust HTTP APIs and services, especially when Codex needs black-box integration tests, random-port app startup, real database test…
hashgraph-online/awesome-codex-plugins
Make a studio's game look like something at build time — a cover from a real frame of the game (free), painted covers, backdrops, textures and character plates from image models through the…
hashgraph-online/awesome-codex-plugins
Balance game difficulty, resources, rewards, probability, progression, economies, and dominant strategies.
hashgraph-online/awesome-codex-plugins
Analyze nonfiction manuscripts for reader engagement signals, including heading-level word counts, slow starts, long slogs, weak takeaway titles, value pacing, beta-reader comment dropoff, and…
Categories
Entry point for new or resumed work. An agent skill from hashgraph-online/awesome-codex-plugins. Ingest is an agent skill from hashgraph-online/awesome-codex-plugins. Entry point for new or resumed work.
Ingest fits situations like: agent Workflows work in your project.
Run `npx skills add hashgraph-online/awesome-codex-plugins --skill ingest -a claude-code`. Or copy the skill folder (plugins/ariaxhan/kernel-claude/skills/ingest in hashgraph-online/awesome-codex-plugins) into .claude/skills/ingest in your project. Claude Code loads it when a task matches its description.
Run `npx skills add hashgraph-online/awesome-codex-plugins --skill ingest -a codex`. Or copy the skill folder (plugins/ariaxhan/kernel-claude/skills/ingest in hashgraph-online/awesome-codex-plugins) into .agents/skills/ingest 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 hashgraph-online/awesome-codex-plugins --skill ingest -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ingest, .gemini/skills/ingest, .github/skills/ingest and .opencode/skills/ingest in your project.
SKILL.md names no scripts, command-line tools or credentials: Ingest is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Bash, Grep, Glob, Task, WebSearch, WebFetch.
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 notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
Ingest 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.
About 3k tokens (SKILL.md is roughly 12k 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 Ingest: MCP Server Builder (anthropics/skills, 180k stars), Hook Development for Claude Code Plugins (anthropics/claude-plugins-official, 38k stars), Using Superpowers (farm-fe/farm, 5.6k stars) and Executing Plans Inline (obra/superpowers, 296k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
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.