Queue processing with fresh context per phase. An agent skill from agenticnotetaking/arscontexta.

MITAuto-check: notesAgent Workflows

Install Ralph

skills CLI
$ npx skills add agenticnotetaking/arscontexta --skill ralph -a claude-code

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

GitHub CLI
$ gh skill install agenticnotetaking/arscontexta ralph --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/agenticnotetaking/arscontexta.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skill-sources/ralph .claude/skills/ralph && 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
ralph
GitHub stars
3.5k
Used in
1 other repo
Token cost
~5.3k tokens
SKILL.md length
1,780 words
Files
2
Skills in repo
25
Repo updated
First seen
Licence
MIT

At a glance

Queue processing with fresh context per phase. An agent skill from agenticnotetaking/arscontexta.

  • Run pipeline tasks
  • SKILL.md covers EXECUTE NOW, MANDATORY CONSTRAINT: SUBAGENT…, Phase Configuration and Step 1: Read Queue State, plus 8 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Tasks that involve Subagents

What it does

Ralph is an agent skill from agenticnotetaking/arscontexta. Queue processing with fresh context per phase. Processes N tasks from the queue, spawning isolated subagents to prevent context contamination. Supports serial, parallel, batch filter, and dry run modes. Triggers on "/ralph", "/ralph N", "process queue", "run pipeline tasks".

Its SKILL.md is about 5.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `skill.json`).

It sits in Agent Workflows, covering Subagents. The repository describes itself as: Claude Code plugin that generates individualized knowledge systems from conversation. You describe how you think and work, have a conversation and get a complete second brain as… The licence is MIT.

When your agent uses it

  • Run pipeline tasks
  • Tasks that involve Subagents

Example prompts

  • “/ralph”
  • “/ralph N”
  • “process queue”
  • “/ralph”

Requirements

  • Pre-approved tools (allowed-tools): Read, Write, Edit, Grep, Glob, Bash, Task

What it can do on your machine

Read from SKILL.md and the folder at commit 2acfd5c. 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
    • Write
    • Edit
    • Grep
    • Glob
    • Bash
    • Task

    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 yaml).

    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

Ralph loads about 5.3k tokens when it runs. Until then it costs about 70 tokens; SKILL.md has 1,780 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~70
When it runs · the whole SKILL.md, loaded when a task matches
~5.3k

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, Write, Edit, Grep, Glob, Bash, Task

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 agenticnotetaking/arscontexta at commit 2acfd5c, republished under its MIT licence (© agenticnotetaking). 1,780 words, ~5,277 tokens.

Download SKILL.mdSave it as .claude/skills/ralph/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
ralph
description
Queue processing with fresh context per phase. Processes N tasks from the queue, spawning isolated subagents to prevent context contamination. Supports serial, parallel, batch filter, and dry run modes. Triggers on "/ralph", "/ralph N", "process queue", "run pipeline tasks".
allowed-tools
Read, Write, Edit, Grep, Glob, Bash, Task
version
1.0
generated_from
arscontexta-v1.6
user-invocable
true
context
fork
argument-hint
N [--parallel] [--batch id] [--type extract] [--dry-run] — N = number of tasks to process

EXECUTE NOW

Target: $ARGUMENTS

Parse arguments:

  • N (required unless --dry-run): number of tasks to process
  • --parallel: concurrent claim workers (max 5) + cross-connect validation
  • --batch [id]: process only tasks from specific batch
  • --type [type]: process only tasks at a specific phase (extract, create, reflect, reweave, verify, enrich)
  • --dry-run: show what would execute without running
  • --handoff: output structured RALPH HANDOFF block at end (for pipeline chaining)
Step 0: Read Vocabulary

Read ops/derivation-manifest.md (or fall back to ops/derivation.md) for domain vocabulary mapping. All output must use domain-native terms. If neither file exists, use universal terms.

START NOW. Process queue tasks.


MANDATORY CONSTRAINT: SUBAGENT SPAWNING IS NOT OPTIONAL

You MUST use the Task tool to spawn a subagent for EVERY task. No exceptions.

This is not a suggestion. This is not an optimization you can skip for "simple" tasks. The entire architecture depends on fresh context isolation per phase. Executing tasks inline in the lead session:

  • Contaminates context (later tasks run on degraded attention)
  • Skips the handoff protocol (learnings are not captured)
  • Violates the ralph pattern (one phase per context window)

If you catch yourself about to execute a task directly instead of spawning a subagent, STOP. Call the Task tool. Every time. For every task. Including create tasks. Including "simple" tasks.

The lead session's ONLY job is: read queue, spawn subagent, evaluate return, update queue, repeat.


Phase Configuration

Each phase maps to specific Task tool parameters. Use these EXACTLY when spawning subagents.

PhaseSkill InvokedPurpose
extract/reduceExtract claims from source material
create(inline note creation)Write the {DOMAIN:note} file
enrich/enrichAdd content to existing {DOMAIN:note}
reflect/reflectFind connections, update {DOMAIN:topic map}s
reweave/reweaveUpdate older {DOMAIN:note_plural} with new connections
verify/verifyDescription quality + schema + health checks

All phases use the same subagent configuration:

  • subagent_type: knowledge-worker (if available) or default
  • mode: dontAsk

Subagents inherit the session model. Users running opus get opus quality on processing phases. Users running sonnet get sonnet everywhere. Fresh context per phase already ensures efficiency — every phase gets full capability in the smart zone.


Step 1: Read Queue State

Read the queue file. Check these locations in order:

  1. ops/queue.yaml
  2. ops/queue/queue.yaml
  3. ops/queue/queue.json

Parse the queue. Identify ALL pending tasks.

Queue structure (v2 schema):

The queue uses current_phase and completed_phases per task entry:

yaml
phase_order:
  claim: [create, reflect, reweave, verify]
  enrichment: [enrich, reflect, reweave, verify]

tasks:
  - id: source-name
    type: extract
    status: pending
    source: ops/queue/archive/2026-01-30-source/source.md
    file: source-name.md
    created: "2026-01-30T10:00:00Z"

  - id: claim-010
    type: claim
    status: pending
    target: "claim title here"
    batch: source-name
    file: source-name-010.md
    current_phase: reflect
    completed_phases: [create]

If the queue file does not exist or is empty, report: "Queue is empty. Use /seed or /pipeline to add sources."

Step 2: Filter Tasks

Build a list of actionable tasks — tasks where status == "pending". Order by position in the tasks array (first = highest priority).

Apply filters:

  • If --batch specified: keep only tasks where batch matches
  • If --type specified: keep only tasks where current_phase matches (e.g., --type reflect finds tasks whose current_phase is "reflect")

The phase_order header defines the phase sequence:

  • claim: create -> reflect -> reweave -> verify
  • enrichment: enrich -> reflect -> reweave -> verify

Step 3: If --dry-run, Report and Stop

Show this and STOP (do not process):

--=={ ralph dry-run }==--

Queue: X total tasks (Y pending, Z done)

Phase distribution:
  Claims:       {create: N, reflect: N, reweave: N, verify: N}
  Enrichments:  {enrich: N, reflect: N, reweave: N, verify: N}

Next tasks to process:
1. {id} — phase: {current_phase} — {target}
2. {id} — phase: {current_phase} — {target}
...

Estimated: ~{N} subagent spawns

Step 4: Process Loop (SERIAL MODE)

If --parallel is set, skip to Step 6 instead.

Process up to N tasks (default 1). For each iteration:

4a. Select Next Task

Pick the first pending task from the filtered list. Read its metadata: id, type, file, target, batch, current_phase, completed_phases.

The current_phase determines which skill to invoke.

Report:

=== Processing task {i}/{N}: {id} — phase: {current_phase} ===
Target: {target}
File: {file}
4b. Build Subagent Prompt

Construct a prompt based on current_phase. Every prompt MUST include:

  • Reference to the task file path (from queue's file field)
  • The task identity (id, current_phase, target)
  • The skill to invoke with --handoff
  • ONE PHASE ONLY constraint
  • Instruction to output RALPH HANDOFF block

Phase-specific prompts:

For extract phase (type=extract tasks only):

Read the task file at ops/queue/{FILE} for context.

You are processing task {ID} from the work queue.
Phase: extract | Target: {TARGET}

Run /reduce --handoff on the source file referenced in the task file.
After extraction: create per-claim task files, update the queue with new entries
(1 entry per claim with current_phase/completed_phases), output RALPH HANDOFF.
ONE PHASE ONLY. Do NOT run reflect or other phases.

For create phase:

Read the task file at ops/queue/{FILE} for context.

You are processing task {ID} from the work queue.
Phase: create | Target claim: {TARGET}

Create a {DOMAIN:note} for this claim in {DOMAIN:notes}/[claim as sentence].md
Follow note design patterns:
- YAML frontmatter with description (adds info beyond title), topics
- Body: 150-400 words showing reasoning with connective words
- Footer: Source (wiki link), Relevant Notes (with context), Topics
Update the task file's ## Create section.
ONE PHASE ONLY. Do NOT run reflect.

For enrich phase:

Read the task file at ops/queue/{FILE} for context.

You are processing task {ID} from the work queue.
Phase: enrich | Target: {TARGET}

Run /enrich --handoff using the task file for context.
The task file specifies which existing {DOMAIN:note} to enrich and what to add.
ONE PHASE ONLY. Do NOT run reflect.

For reflect phase:

Build sibling list: Query the queue for other claims in the same batch where completed_phases includes "create" (note already exists). Format as wiki links.

Read the task file at ops/queue/{FILE} for context.

You are processing task {ID} from the work queue.
Phase: reflect | Target: {TARGET}

OTHER CLAIMS FROM THIS BATCH (check connections to these alongside regular discovery):
{for each sibling in batch where completed_phases includes "create":}
- [[{SIBLING_TARGET}]]
{end for, or "None yet" if this is the first claim}

Run /reflect --handoff on: {TARGET}
Use dual discovery: {DOMAIN:topic map} exploration AND semantic search.
Add inline links where genuine connections exist — including sibling claims listed above.
Update relevant {DOMAIN:topic map} with this {DOMAIN:note}.
ONE PHASE ONLY. Do NOT run reweave.

For reweave phase:

Same sibling list as reflect (re-query queue for freshest state):

Read the task file at ops/queue/{FILE} for context.

You are processing task {ID} from the work queue.
Phase: reweave | Target: {TARGET}

OTHER CLAIMS FROM THIS BATCH:
{for each sibling in batch where completed_phases includes "create":}
- [[{SIBLING_TARGET}]]
{end for}

Run /reweave --handoff for: {TARGET}
This is the BACKWARD pass. Find OLDER {DOMAIN:note_plural} AND sibling claims
that should reference this {DOMAIN:note} but don't.
Add inline links FROM older {DOMAIN:note_plural} TO this {DOMAIN:note}.
ONE PHASE ONLY. Do NOT run verify.

For verify phase:

Read the task file at ops/queue/{FILE} for context.

You are processing task {ID} from the work queue.
Phase: verify | Target: {TARGET}

Run /verify --handoff on: {TARGET}
Combined verification: recite (cold-read prediction test), validate (schema check),
review (per-note health).
IMPORTANT: Recite runs FIRST — read only title+description, predict content,
THEN read full {DOMAIN:note}.
Final phase for this claim. ONE PHASE ONLY.
4c. Spawn Subagent (MANDATORY — NEVER SKIP)

Call the Task tool with the constructed prompt:

Task(
  prompt = {the constructed prompt from 4b},
  description = "{current_phase}: {short target}" (5 words max)
)

REPEAT: You MUST call the Task tool here. Do NOT execute the prompt yourself. Do NOT "optimize" by running the task inline. The Task tool call is the ONLY acceptable action at this step.

Wait for the subagent to complete and capture its return value.

4d. Evaluate Return

When the subagent returns:

  1. Look for RALPH HANDOFF block — search for === RALPH HANDOFF and === END HANDOFF === markers
  2. If handoff found: Parse the Work Done, Learnings, and Queue Updates sections
  3. If handoff missing: Log a warning but continue — the work was still completed
  4. Capture learnings: If Learnings section has non-NONE entries, note them for the final report
4e. Update Queue (Phase Progression)

After evaluating the return, advance the task to the next phase.

Phase progression logic:

Look up phase_order from the queue header to determine the next phase. Find current_phase in the array. If there is a next phase, advance. If it is the last phase, mark done.

If NOT the last phase — advance to next:

  • Set current_phase to the next phase in the sequence
  • Append the completed phase to completed_phases

If the last phase (verify) — mark task done:

  • Set status: done
  • Set completed to current UTC timestamp
  • Set current_phase to null
  • Append the completed phase to completed_phases

For extract tasks ONLY: Re-read the queue after marking done. The reduce skill writes new task entries (1 entry per claim/enrichment with current_phase/completed_phases) to the queue during execution. The lead must pick these up for subsequent iterations.

4f. Report Progress
=== Task {id} complete ({i}/{N}) ===
Phase: {current_phase} -> {next_phase or "done"}

If learnings were captured, show a brief summary. If more unblocked tasks exist, show the next one.

4g. Re-filter Tasks

Before the next iteration, re-read the queue and re-filter tasks. Phase advancement may have changed eligibility (e.g., after completing a create phase, the task is now at reflect — if filtering by --type reflect, it becomes eligible).


Step 5: Post-Batch Cross-Connect (Serial Mode)

After advancing a task to "done" (Step 4e), check if ALL tasks in that batch now have status: "done". If yes and the batch has 2 or more completed claims:

  1. Collect all note paths from completed batch tasks. For each claim task with status: "done", read the task file's ## Create section to find the created note path.

  2. Spawn ONE subagent for cross-connect validation:

Task(
  prompt = "You are running post-batch cross-connect validation for batch '{BATCH}'.

Notes created in this batch:
{list of ALL note titles + paths from completed batch tasks}

Verify sibling connections exist between batch notes. Add any that were missed
because sibling notes did not exist yet when the earlier claim's reflect ran.
Check backward link gaps. Output RALPH HANDOFF block when done.",
  description = "cross-connect: batch {BATCH}"
)
  1. Parse handoff block, capture learnings. Include cross-connect results in the final report.

Skip if: batch has only 1 claim (no siblings) or tasks from the batch are still pending.


Show full SKILL.md (718 more words)Show less

Step 6: Parallel Mode (--parallel)

When --parallel flag is present, SKIP Step 4 entirely and use this section instead.

Incompatible flags: --parallel cannot be combined with --type. Parallel mode processes claims end-to-end (all phases). If --type is also set, report an error:

ERROR: --parallel and --type are incompatible. Parallel processes full claim pipelines, not individual phases.
Use serial mode for per-phase filtering: /ralph N --type reflect
Parallel Architecture

Two-phase design: Workers receive sibling claim info upfront so they can link proactively. Phase B validates and catches any gaps.

Ralph Lead (you) — orchestration only
|
+-- PHASE A: PARALLEL CLAIM PROCESSING (concurrent)
|   +-- worker-001: all 4 phases for claim 001 (with sibling awareness)
|   +-- worker-002: all 4 phases for claim 002 (with sibling awareness)
|   +-- worker-003: all 4 phases for claim 003 (with sibling awareness)
|   +-- ...up to 5 concurrent workers
|
+-- [semantic search index sync]
|
+-- PHASE B: CROSS-CONNECT VALIDATION (one subagent, one pass)
|   +-- validates sibling links, adds any that workers missed
|
+-- CLEANUP + FINAL REPORT

Why two phases? Workers have sibling awareness (claim titles in spawn prompt) and link proactively during reflect/reweave. But timing means some sibling notes may not exist yet during a worker's reflect phase. Phase B runs a single cross-connect pass after all notes exist.

6a. Identify Parallelizable Claims

From the filtered queue, find pending claims. A claim is parallelizable when its status == "pending". Cap at 5 concurrent workers (or N, whichever is smaller).

Report:

=== Parallel Mode ===
Parallelizable claims: {count}
Max concurrent workers: {min(count, N, 5)}
6b. Spawn Claim Workers

For each parallelizable claim (up to N requested, max 5 concurrent):

Build the worker prompt with sibling awareness:

You are a claim worker processing claim "{TARGET}" from batch "{BATCH}".

Claim ID: {CLAIM_ID}
Task file: ops/queue/{FILE}
Current phase: {CURRENT_PHASE}
Completed phases: {COMPLETED_PHASES}

SIBLING CLAIMS IN THIS BATCH (link to these where genuine connections exist):
{for each other claim in the batch:}
- "{SIBLING_TARGET}" (task file: ops/queue/{SIBLING_FILE})
{end for}

During REFLECT and REWEAVE, check if your claim genuinely connects to any sibling.
If a sibling {DOMAIN:note} exists in {DOMAIN:notes}/, link to it inline where the
connection is real. If it does not exist yet (still being created), skip —
cross-connect will catch it after.

Read the task file for full context. Execute phases from current_phase onwards.
If completed_phases is not empty, skip those phases (resumption mode).

When complete, update the queue entry to status "done" and report the created
{DOMAIN:note} title, path, and claim ID. The lead needs this for cross-connect.

Spawn via Task tool:

Task(
  prompt = {the constructed prompt},
  description = "claim: {short target}" (5 words max)
)

Spawn workers in PARALLEL — launch all Task tool calls in a single message, not sequentially.

6c. Monitor Workers (Phase A)

Wait for worker completions. As workers complete:

  1. Parse completion message — extract the created note title and path (needed for Phase B)
  2. Log any learnings from the worker's report
  3. Check for issues — failures, skipped phases, resource conflicts

Collect all created notes — maintain a list of {note_title, note_path} from worker completion messages. You need this for the cross-connect validation phase.

Completion gate: Phase B CANNOT start until ALL spawned workers have reported back (either success or error). Track completions:

Workers spawned: {total_spawned}
Workers completed: {completion_count}
Workers with errors: {error_count}

Phase B ready: {completion_count + error_count == total_spawned}

Do NOT proceed to Phase B while any worker is still running.

6d. Cross-Connect Validation (Phase B)

Light validation pass. Workers had sibling awareness during Phase A and linked proactively. This phase validates their work and catches gaps.

Skip if only 1 claim was processed (no siblings to cross-connect).

Spawn ONE subagent for cross-connect validation:

Task(
  prompt = "You are running post-batch cross-connect validation for batch '{BATCH}'.

Notes created in this batch:
{list of ALL newly created note titles with paths from Phase A}

Verify sibling connections exist between these notes. Add any connections that
workers missed because sibling notes did not exist yet when a worker's reflect ran.
Check backward link gaps. Output RALPH HANDOFF block when done.",
  description = "cross-connect: batch {BATCH}"
)

Parse the handoff block, capture learnings.

Report after Phase B:

=== Cross-Connect Validation Complete ===
Sibling connections validated: {count}
Missing connections added: {count}
6e. Cleanup

After Phase B completes (or after Phase A if cross-connect was skipped):

  1. Clean any lock files if created
  2. Skip to Step 7 for the final report, noting parallel mode in the output

Step 7: Final Report

After all iterations (or when no unblocked tasks remain):

--=={ ralph }==--

Processed: {count} tasks
  {breakdown by phase type}

Subagents spawned: {count} (MUST equal tasks processed)

Learnings captured:
  {list any friction, surprises, methodology insights, or "None"}

Queue state:
  Pending: {count}
  Done: {count}
  Phase distribution: {create: N, reflect: N, reweave: N, verify: N}

Next steps:
  {if more pending tasks}: Run /ralph {remaining} to continue
  {if batch complete}: Run /archive-batch {batch-id}
  {if queue empty}: All tasks processed

Verification: The "Subagents spawned" count MUST equal "Tasks processed." If it does not, the lead executed tasks inline — this is a process violation. Report it as an error.

If --handoff flag was set, also output:

=== RALPH HANDOFF: orchestration ===
Target: queue processing

Work Done:
- Processed {count} tasks: {list of task IDs}
- Types: {breakdown by type}

Learnings:
- [Friction]: {description} | NONE
- [Surprise]: {description} | NONE
- [Methodology]: {description} | NONE
- [Process gap]: {description} | NONE

Queue Updates:
- Marked done: {list of completed task IDs}
=== END HANDOFF ===

Error Recovery

Subagent crash mid-phase: The queue still shows current_phase at the failed phase. The task file confirms the corresponding section is empty. Re-running /ralph picks it up automatically — the task is still pending at that phase.

Queue corruption: If the queue file is malformed, report the error and stop. Do NOT attempt to fix it automatically.

All tasks blocked: Report which tasks are blocked and why. Suggest remediation.

Empty queue: Report "Queue is empty. Use /seed or /pipeline to add sources."


Quality Gates

Gate 1: Subagent Spawned

Every task MUST be processed via Task tool. If the lead detects it executed a task inline, log this as an error and flag it in the final report.

Gate 2: Handoff Present

Every subagent SHOULD return a RALPH HANDOFF block. If missing: log warning, mark task done, continue.

Gate 3: Extract Yield

For extract tasks: if zero claims extracted, log as an observation. Do NOT retry automatically.

Gate 4: Task File Updated

After each phase, the task file's corresponding section (Create, Reflect, Reweave, Verify) should be filled. If empty after subagent completes, log warning.


Critical Constraints

Never:

  • Execute tasks inline in the lead session (USE THE TASK TOOL)
  • Process more than one phase per subagent (context contamination)
  • Retry failed tasks automatically without human input
  • Skip queue phase advancement (breaks pipeline state)
  • Process tasks that are not in pending status
  • Run if queue file does not exist or is malformed
  • In parallel mode: combine with --type (incompatible)

Always:

  • Spawn a subagent via Task tool for EVERY task (the lead ONLY orchestrates)
  • Include sibling claim titles in reflect and reweave prompts
  • Re-read queue after extract tasks (subagent adds new entries)
  • Re-filter tasks between iterations (phase advancement creates new eligibility)
  • Log learnings from handoff blocks
  • Report failures clearly for human review
  • Verify subagent count equals task count in final report

© agenticnotetaking, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skill-sources/ralph of agenticnotetaking/arscontexta.

  • SKILL.md
  • skill.json

Open the folder on GitHubat commit 2acfd5c

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in agenticnotetaking/arscontexta, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Ralph 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.

Ralph compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Ralph this skillagenticnotetaking/arscontexta3.5k1 repos~5.3kAutomated safety check: NotesMIT
Claude Code Agent Developmentanthropics/claude-plugins-official37k8 repos~2.8kAutomated safety check: PassApache-2.0
Subagent Driven DevelopmentAsvarox/allkaraoke26137 repos~1.2kAutomated safety check: PassNone
Dispatching Parallel Agentsultralisp/ultralisp25840 repos~1.5kAutomated safety check: PassNone
Paseo Advisor Second Opiniongetpaseo/paseo20k1 repos~756Automated safety check: PassCustom licence
Task Observerrebelytics/one-skill-to-rule-them-all3.2k1 repos~12kAutomated safety check: PassCC-BY-4.0

Similar skills

  • 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.

    37k GitHub starsUsed in 8 repos~2.8k tokens
    Agent WorkflowsAuto-check passed
  • Subagent Driven Development

    Asvarox/allkaraoke

    A skill your agent uses when executing implementation plans with independent tasks in the current session

    261 GitHub starsUsed in 37 repos~1.2k tokens
    Agent WorkflowsAuto-check passed
  • Dispatching Parallel Agents

    ultralisp/ultralisp

    A skill your agent uses when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies

    258 GitHub starsUsed in 40 repos~1.5k tokens
    Agent WorkflowsAuto-check passed
  • Launches one separate agent through Paseo to give a second opinion on the current task, with a self-contained briefing and no permission to edit files.

    20k GitHub starsUsed in 1 repo~756 tokens
    Agent WorkflowsAuto-check passed
  • Task Observer

    rebelytics/one-skill-to-rule-them-all

    Monitors task execution for skill improvement opportunities.

    3.2k GitHub starsUsed in 1 repo~12k tokens
    Agent WorkflowsAuto-check passed
  • O2 Review Loop

    openobserve/openobserve

    Splits a change into planner, coder and independent reviewer roles: you confirm a spec, a subagent implements it, and a separate reviewer checks each round's local WIP commit.

    22k GitHub stars~3.7k tokensUpdated yesterday
    Agent WorkflowsAuto-check passed

More from agenticnotetaking/arscontexta

All 25 skills in this repo
  • Graph

    agenticnotetaking/arscontexta

    Interactive knowledge graph analysis. An agent skill from agenticnotetaking/arscontexta.

    3.5k GitHub starsUsed in 1 repo~4.9k tokens
    Auto-check: notes
  • Learn

    agenticnotetaking/arscontexta

    Research a topic and grow your knowledge graph. An agent skill from agenticnotetaking/arscontexta.

    3.5k GitHub starsUsed in 1 repo~1.9k tokens
    Auto-check: notes
  • Recommend

    agenticnotetaking/arscontexta

    Get research-backed architecture advice for your knowledge system.

    3.5k GitHub starsUsed in 1 repo~5.1k tokens
    Auto-check passed
  • Stats

    agenticnotetaking/arscontexta

    Show vault statistics and knowledge graph metrics. An agent skill from agenticnotetaking/arscontexta.

    3.5k GitHub starsUsed in 1 repo~3.1k tokens
    Auto-check: notes
  • Help

    agenticnotetaking/arscontexta

    Contextual guidance and command discovery. An agent skill from agenticnotetaking/arscontexta.

    3.5k GitHub stars~3.3k tokensUpdated 7 mo ago
    Auto-check: notes
  • Next

    agenticnotetaking/arscontexta

    Surface the most valuable next action by combining task stack, queue state, inbox pressure, health, and goals.

    3.5k GitHub stars~4.9k tokensUpdated 7 mo ago
    Auto-check: notes

Categories

Questions about Ralph

What does Ralph do?

Queue processing with fresh context per phase. An agent skill from agenticnotetaking/arscontexta. Ralph is an agent skill from agenticnotetaking/arscontexta. Queue processing with fresh context per phase.

When should I use Ralph?

Ralph fits situations like: run pipeline tasks; tasks that involve Subagents.

How do I install Ralph in Claude Code?

Run `npx skills add agenticnotetaking/arscontexta --skill ralph -a claude-code`. Or copy the skill folder (skill-sources/ralph in agenticnotetaking/arscontexta) into .claude/skills/ralph in your project. Claude Code loads it when a task matches its description.

How do I install Ralph in Codex?

Run `npx skills add agenticnotetaking/arscontexta --skill ralph -a codex`. Or copy the skill folder (skill-sources/ralph in agenticnotetaking/arscontexta) into .agents/skills/ralph in your project. Codex loads it when a task matches its description.

Can I use Ralph 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 agenticnotetaking/arscontexta --skill ralph -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ralph, .gemini/skills/ralph, .github/skills/ralph and .opencode/skills/ralph in your project.

What does Ralph need to run?

SKILL.md names no scripts, command-line tools or credentials: Ralph is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Write, Edit, Grep, Glob, Bash, Task.

Does Ralph 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 Ralph 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 Ralph use?

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

How many tokens does Ralph use?

About 5.3k tokens (SKILL.md is roughly 21k 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 Ralph?

Skills that share tags, products or a category with Ralph: Claude Code Agent Development (anthropics/claude-plugins-official, 37k stars), Subagent Driven Development (Asvarox/allkaraoke, 261 stars), Dispatching Parallel Agents (ultralisp/ultralisp, 258 stars) and Paseo Advisor Second Opinion (getpaseo/paseo, 20k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Ralph?

agenticnotetaking (a GitHub organization) maintains it in agenticnotetaking/arscontexta, which has 3,492 GitHub stars. The repository holds 25 skills in this directory. The repository was last updated on February 24, 2026.

Source: agenticnotetaking/arscontexta on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.