Entry point for new or resumed work. An agent skill from hashgraph-online/awesome-codex-plugins.

Apache-2.0Auto-check: notesAgent Workflows

Install Ingest

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

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

GitHub CLI
$ gh skill install hashgraph-online/awesome-codex-plugins ingest --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/ariaxhan/kernel-claude/skills/ingest .claude/skills/ingest && 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
ingest
GitHub stars
1.2k
Token cost
~3k tokens
SKILL.md length
1,363 words
Files
1
Skills in repo
686
Repo updated
First seen
Licence
Apache-2.0

At a glance

Entry point for new or resumed work. An agent skill from hashgraph-online/awesome-codex-plugins.

  • Works in 5 steps: live verified repository state → explicit current user instruction → handoff or checkpoint manifest → …
  • Agent Workflows work in your project
  • SKILL.md covers Anti-Patterns, Proven Solution and Sources
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

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.

When your agent uses it

  • Agent Workflows work in your project

Example prompts

  • “/ingest”

Requirements

  • Pre-approved tools (allowed-tools): Read, Bash, Grep, Glob, Task, WebSearch, WebFetch

Workflow steps

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

  1. live verified repository state
  2. explicit current user instruction
  3. handoff or checkpoint manifest
  4. chronicle
  5. inferred conversation history

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 these tools, so the agent can use them without asking each time:

    • Read
    • Bash
    • Grep
    • Glob
    • Task
    • WebSearch
    • WebFetch

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

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

    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

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.

Always · name and description, kept in context so the agent knows when to use it
~51
When it runs · the whole SKILL.md, loaded when a task matches
~3k

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.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Bash, Grep, Glob, Task, WebSearch, WebFetch

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). 1,363 words, ~3,026 tokens.

Download SKILL.mdSave it as .claude/skills/ingest/SKILL.md (or your agent's skills folder).
name
ingest
description
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.
allowed-tools
Read, Bash, Grep, Glob, Task, WebSearch, WebFetch
user-invocable
true
kernel.kind
workflow
kernel.version
1
kernel.side_effects
writes_repo
kernel.confirmation
none
kernel.consumes
kernel.handoff/v1, kernel.checkpoint/v1
<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:

  1. live verified repository state
  2. explicit current user instruction
  3. handoff or checkpoint manifest
  4. chronicle
  5. inferred conversation history
    </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>

bash
agentdb read-start
ls _meta/research/  # check prior work

Load /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"`
  1. Discover: explicit path if the user gave one, else:

    bash
    "$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.

  2. Validate — a manifest that does not validate is not resumed:

    bash
    "$KM" validate <manifest>     # exit 2 (no parser) on a sealed manifest = STOP
  3. Divergence — live state wins over manifest claims:

    bash
    "$KM" divergence <manifest> --json

    Typed divergence events apply workflow.invalidation_rules[].when and return recalculated phase statuses. Never trust an inherited phase whose inputs changed.

  4. Preflight: run "$KM" preflight <manifest>. Canonical state permits only typed current-branch, path-exists, and allowlisted argv checks; raw shell is invalid.

  5. Compile bounded context — read the bundle, not the raw tree:

    bash
    "$KM" compile <manifest> --bundle-out /tmp/resume-bundle.md --receipt-out _meta/reports/receipt-{date}.json

    The 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.

  6. Activate the policy (arms the guard-context hook for sealed/bounded):

    bash
    "$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.

  7. Resume at the declared position:

    bash
    "$KM" resume <manifest>    # entry_phase / entrypoint / next_operation

    Skip inherited phases (already verified by divergence), execute required ones. Honor execution.stop_conditions and emit checkpoints at execution.checkpoints.

  8. Complete: when outputs.required are verified,

    bash
    "$KM" deactivate --receipt _meta/reports/receipt-{date}.json

    Deactivate 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}

Show full SKILL.md (538 more words)Show less

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:

  • observable outcome 1
  • edge case handled

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:

  • Exact file paths: every file that will change, by absolute path
  • Exact symbols: every function/class/type to add/modify/remove, by name
  • Exact code snippets for non-trivial logic (not pseudocode, not "implement X")
  • Exact configs/SQL/schemas: if the change touches them, paste the literal block
  • Exact verification commands: how a fresh agent confirms success without asking

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

  1. /kernel:tearitapart — review plan before implementation
  2. agentdb contract '{"goal":"X","files":["Y"],"tier":N}' 2b. If non-local profile: _gh_create_issue with contract goal + tier label
  3. git checkout -b {type}/{name}
  4. Spawn surgeon
  5. Wait for checkpoint
  6. (tier 3) spawn adversary
  7. /kernel:validate → verify evals
  8. /kernel:review — self-review before PR </tier_2_plus>
    </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

Files

Just SKILL.md in plugins/ariaxhan/kernel-claude/skills/ingest of hashgraph-online/awesome-codex-plugins.

Open the folder on GitHubat commit 78497e5

Compare with similar skills

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.

Ingest compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Ingest this skillhashgraph-online/awesome-codex-plugins1.2k—~3kAutomated safety check: NotesApache-2.0
MCP Server Builderanthropics/skills180k64 repos~2.3kAutomated safety check: PassApache-2.0
Hook Development for Claude Code Pluginsanthropics/claude-plugins-official38k11 repos~4.1kAutomated safety check: NotesApache-2.0
Using Superpowersfarm-fe/farm5.6k35 repos~1.4kAutomated safety check: PassMIT
Executing Plans Inlineobra/superpowers296k2 repos~5.1kAutomated safety check: PassMIT
Claude Code Agent Developmentanthropics/claude-plugins-official38k8 repos~2.8kAutomated safety check: PassApache-2.0

Similar skills

  • MCP Server Builder

    anthropics/skills

    Official

    Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.

    180k GitHub starsUsed in 64 repos~2.3k tokens
    Agent WorkflowsAuto-check passed
  • Hook Development for Claude Code Plugins

    anthropics/claude-plugins-official

    Official

    Explains how to write Claude Code plugin hooks, both prompt-based checks and bash commands, for events such as PreToolUse, Stop and SessionStart.

    38k GitHub starsUsed in 11 repos~4.1k tokens
    Agent WorkflowsAuto-check: notes
  • Using Superpowers

    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

    5.6k GitHub starsUsed in 35 repos~1.4k tokens
    Agent WorkflowsAuto-check passed
  • Executing Plans Inline

    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.

    296k GitHub starsUsed in 2 repos~5.1k tokens
    Agent WorkflowsAuto-check passed
  • Claude Code Agent Development

    anthropics/claude-plugins-official

    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.

    38k GitHub starsUsed in 8 repos~2.8k tokens
    Agent WorkflowsAuto-check passed
  • Skill Creator

    Azure/azqr

    Official

    Create new skills, modify and improve existing skills, and measure skill performance.

    795 GitHub starsUsed in 89 repos~8.2k tokens
    Agent WorkflowsAuto-check passed

More from hashgraph-online/awesome-codex-plugins

All 686 skills in this repo
  • Anime Reaction Gif

    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.

    1.2k GitHub stars~922 tokensUpdated today
    Auto-check passed
  • Calibredb

    hashgraph-online/awesome-codex-plugins

    Manage and query Calibre libraries with the calibredb CLI (local paths or Calibre Content server URLs).

    1.2k GitHub stars~1k tokensUpdated today
    Auto-check passed
  • Rust API Test Harness

    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…

    1.2k GitHub stars~1.7k tokensUpdated today
    Auto-check passed
  • Art

    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…

    1.2k GitHub stars~2.4k tokensUpdated today
    Auto-check passed
  • Game Balance Economy

    hashgraph-online/awesome-codex-plugins

    Balance game difficulty, resources, rewards, probability, progression, economies, and dominant strategies.

    1.2k GitHub stars~618 tokensUpdated today
    Auto-check passed
  • Manuscript Engagement Analytics

    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…

    1.2k GitHub stars~875 tokensUpdated today
    Auto-check passed

Categories

Questions about Ingest

What does Ingest do?

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.

When should I use Ingest?

Ingest fits situations like: agent Workflows work in your project.

How do I install Ingest in Claude Code?

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.

How do I install Ingest in Codex?

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.

Can I use Ingest 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 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.

What does Ingest need to run?

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.

Does Ingest 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 Ingest safe to install?

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.

What licence does Ingest use?

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.

How many tokens does Ingest use?

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.

What are the alternatives to Ingest?

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.

Who maintains Ingest?

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.