Trigger: vague/unfocused request or solution-without-problem.

Apache-2.0Auto-check passed

Install Discover

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

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

GitHub CLI
$ gh skill install hashgraph-online/awesome-codex-plugins discover --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/epicsagas/epic-harness/skills/discover .claude/skills/discover && 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
discover
GitHub stars
1.2k
Token cost
~2.5k tokens
SKILL.md length
1,125 words
Files
1
Skills in repo
686
Repo updated
First seen
Licence
Apache-2.0

At a glance

Trigger: vague/unfocused request or solution-without-problem.

  • Works in 6 steps: Prerequisites → Listen → Probe → …
  • SKILL.md covers Iron Law, Process, When to Trigger and Anti-Rationalization, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Discover is an agent skill from hashgraph-online/awesome-codex-plugins. Trigger: vague/unfocused request or solution-without-problem. Also invoked via /discover command. Reframes goal before acting.

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

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.

Example prompts

  • “/discover”

Workflow steps

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

  1. Prerequisites
  2. Listen
  3. Probe
  4. Frame
  5. Save
  6. Transition

What it can do on your machine

Read from SKILL.md and the folder at commit 78497e5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

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

    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

Discover loads about 2.5k tokens when it runs. Until then it costs about 34 tokens; SKILL.md has 1,125 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~34
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k

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 passed

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.

SKILL.md

The full file from hashgraph-online/awesome-codex-plugins at commit 78497e5, republished under its Apache-2.0 licence (© hashgraph-online). 1,125 words, ~2,499 tokens.

Download SKILL.mdSave it as .claude/skills/discover/SKILL.md (or your agent's skills folder).
name
discover
description
Trigger: vague/unfocused request or solution-without-problem. Also invoked via /discover command. Reframes goal before acting.
modes
auto, /discover

Discover — Problem Discovery

You are starting the Discover phase. Your job is to help the user articulate what problem they are actually trying to solve, before jumping to solutions or specs.

CRITICAL: Run HARNESS_DIR=$(epic-harness path) first. NEVER use .harness/ in the project directory.

Iron Law

NO SPEC WITHOUT A PROBLEM STATEMENT. Building the wrong thing well is worse than building the right thing poorly.

Process

Step 0: Prerequisites
  • Resolve harness directory: HARNESS_DIR=$(epic-harness path)
  • Read any existing context (CLAUDE.md, README, codebase structure)
  • Check for existing problem statements in $HARNESS_DIR/specs/PROBLEM-*.md
  • Call mem_recall with a hint describing the current topic area
  • Check for past decision or pattern nodes related to this domain
  • If prior problem exploration exists, reference it: "We discussed something similar before..."
  • If existing PROBLEM files are found, review them — the user may be continuing prior work

Why: Past context prevents re-exploring ground already covered. The knowledge graph connects today's vague request to yesterday's decisions.

Step 1: Listen

Read the user's request carefully. Repeat it back in your own words and ask:

  • "Is that the core of it, or is there more?"

Categorize the request:

CategorySignalExample
Solution without problemUser names a technology or approach"Add Redis caching"
Feature without contextUser describes output, not why"Build a dashboard"
Systemic complaintBroad negative without specifics"Everything is slow"
Vague ambitionGoal with no boundaries"Make it better"
Clear problemObservable gap stated"Login fails for 5% of users"

Why: Categorization determines the probing technique. Misreading the category leads to wrong questions.

Step 2: Probe

Select the technique based on the category identified in Step 1. Ask max 3 questions per round, run max 3 rounds. If the user can't answer or says "I'm not sure", proceed to Frame with what you have.

Technique Selection
User signalTechniqueCore question
Names a solution ("Add Redis")5 Whys"What's happening that makes you need this?" → repeat
Describes a feature without whyJTBD"What situation makes you need this? What would 'done' look like?"
"Everything is broken"Fishbone"Which area: People / Process / Technology / Data / Environment?"
Vague or contradictorySocratic"What specifically do you mean by 'X'?"
Has a vision but no pathDone looks like"When this works perfectly, what do you see?"
Uncertain assumptionsAssumption map"What must be true for this to work?"
Detailed Technique Reference

5 Whys — when the user presents a solution without a problem:

User: "Add Redis caching to the API."
→ "What's happening that makes you want caching?"
→ "Why is that slow?"
→ "Why does that query take long?"

Stop when you reach an actionable root cause. Max 5 levels.

JTBD (Jobs To Be Done) — when the user describes a feature without context:

User: "Build a dashboard."
→ "What situation makes you need this?"
→ "What would you do with the information right now?"
→ "What would have to be true for you to say 'this solved it'?"

Extract job stories: "When [situation], I want [motivation], so I can [outcome]."

Fishbone — when the user has a systemic complaint:

User: "Everything is broken."
→ Walk through categories: People, Process, Technology, Data, Environment
→ "Which of these areas is the biggest contributor?"
→ "Is this on all branches or specific ones?"

Map causes across categories, then narrow to the top 1-2.

Socratic Questioning — when the request is vague or contradictory:

User: "Make it more secure." / "I want it to be faster."
→ Clarification: "What specifically do you mean by 'secure' / 'fast'?"
→ Probing assumptions: "What makes you believe this is the issue?"
→ Implications: "If we change X, what happens to Y?"
→ Alternatives: "What other approaches did you consider?"

"What Does Done Look Like?" — when the user has a vision but no path:

→ "When this is shipped and working perfectly, what specifically do you see?"
→ "What would I click, what output would appear, what would the logs show?"

Work backwards from the concrete end state.

Assumption Mapping — when the request depends on uncertain premises:

→ "Let me check what we're assuming must be true for this to work."
→ List 4-5 assumptions, ask which are uncertain.

Shaky assumptions become prerequisites.

Each round should narrow the space. If after 2 rounds you have enough to frame, don't force a third.

Why: The right technique extracts the real problem in 2-3 rounds instead of 10 random questions. Technique mismatch wastes rounds and frustrates the user.

Step 3: Frame

Synthesize everything into a structured problem statement:

[Who] experiences [observable problem] when [trigger condition], resulting in [quantified impact]. The desired state is [measurable outcome].

Capture supporting context:

  • Root cause (from 5 Whys) or Job story (from JTBD)
  • Constraints: timeline, technology, scale, compatibility
  • Assumptions: what must be true, which are uncertain
  • Out of scope: what this problem explicitly does NOT cover

Show the frame to the user and ask: "Does this capture the problem accurately?"

Why: A written problem statement is testable. If you can't write one, you haven't discovered the problem yet.

Step 4: Save

Once confirmed, save the problem statement:

bash
mkdir -p "$HARNESS_DIR/specs"

Write to $HARNESS_DIR/specs/PROBLEM-{timestamp}.md (see Output Format below).

If the user realizes there are multiple problems during probing, address them one at a time. Each problem gets its own file.

Show full SKILL.md (455 more words)Show less
Step 5: Transition

Tell the user: "Problem defined. Run /spec to turn this into a buildable specification."

If the user wants to explore further (e.g., they realize there are actually 3 problems), loop back to Step 2 with the new angle.

Why: The transition from problem to spec is natural but explicit. The user owns the decision to move forward.

When to Trigger

Auto-trigger (no command needed):

  • User's request lacks a clear problem or goal
  • User names a technology/solution without explaining why
  • User describes symptoms without identifying the underlying issue
  • User says "I don't know where to start" or equivalent
  • Request is so broad it could mean 3+ different things

Explicit invocation (/discover):

  • User wants to formally start the discover phase for a new project or feature
  • User wants to re-explore a problem before re-specifying
  • Continuation from a previous discovery session

Anti-Rationalization

ExcuseRebuttalWhat to do instead
"Let's just start building and figure it out"Building without a problem is expensive guessing.Spend 5 minutes framing the problem. It saves hours of rework.
"The problem is obvious"If it were obvious, you'd have a spec, not a vague request.Write the problem statement. If it's obvious, it takes 30 seconds.
"I don't have time for questions"You don't have time to build the wrong thing.Run 2 focused rounds, not 10 open-ended ones.
"I already told you the problem"You told me a solution. The problem is why you need that solution.Ask one "why" question. If the answer reveals the problem, proceed.
"Can't you just figure it out from the code?"Code shows what exists, not what's missing or why it hurts.Combine code exploration with user context for the full picture.
"Let me just show you the bug"A bug is a symptom, not a problem. Fixing symptoms is whack-a-mole.Trace the bug to its root cause before jumping to a fix.

Evidence Required

Before claiming the problem is defined, show ALL of these:

  • Problem statement written in the structured format (Who / Problem / When / Impact / Desired state)
  • Root cause or job story identified
  • At least one measurable/observable criterion for success
  • User confirmed the frame is accurate
  • Scope boundaries stated (what's in and out)

"I think I understand" without a written problem statement = guessing.

Red Flags

  • Jumping to solutions or code during the discovery conversation
  • Asking more than 3 questions per round (overwhelming the user)
  • Running more than 3 rounds without attempting a frame (analysis paralysis)
  • Ignoring when the user says "I'm not sure" — that IS information
  • Producing a problem statement the user didn't confirm
  • Confusing symptoms with root causes
  • Framing the problem so broadly it could mean anything ("improve the system")
  • Skipping discovery for non-trivial, ambiguous work

Output Format

Save to $HARNESS_DIR/specs/PROBLEM-{timestamp}.md:

markdown
---
status: framed
created: {ISO-8601 timestamp}
context: {one-line summary}
---

# Problem: {title}

## Problem Statement
{Who} experiences {observable problem} when {trigger condition}, resulting in {quantified impact}. The desired state is {measurable outcome}.

## Root Cause / Job Story
{5 Whys chain or JTBD job story}

## Constraints
- Timeline: {when is this needed}
- Technology: {stack constraints}
- Scale: {expected load/users}
- Compatibility: {backward-compat requirements}

## Assumptions
- {assumption} — {certain / uncertain}
- {assumption} — {certain / uncertain}

## Out of Scope
- {what this problem explicitly does NOT cover}

© 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/epicsagas/epic-harness/skills/discover of hashgraph-online/awesome-codex-plugins.

Open the folder on GitHubat commit 78497e5

Compare with similar skills

Discover 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.

Discover compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Discover this skillhashgraph-online/awesome-codex-plugins1.2k—~2.5kAutomated safety check: PassApache-2.0
Is This A Problemanthropics/claude-for-legal9.6k2 repos~2.3kAutomated safety check: PassApache-2.0
Discover Pluginsruvnet/ruflo74k—~2kAutomated safety check: NotesMIT
Aas Discoversickn33/agentic-awesome-skills47k1 repos~523Automated safety check: PassMIT
Opportunity Solution Treephuryn/pm-skills27k—~1.1kAutomated safety check: PassMIT
Google Cloud Solution Architecturegoogle/skills21k—~3.5kAutomated safety check: PassApache-2.0

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Questions about Discover

What does Discover do?

Trigger: vague/unfocused request or solution-without-problem. Discover is an agent skill from hashgraph-online/awesome-codex-plugins. Trigger: vague/unfocused request or solution-without-problem.

How do I install Discover in Claude Code?

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

How do I install Discover in Codex?

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

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

What does Discover need to run?

SKILL.md names no scripts, command-line tools or credentials: Discover is instructions for the agent only.

Does Discover 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 Discover safe to install?

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.

What licence does Discover use?

Discover 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 Discover use?

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.

What are the alternatives to Discover?

Skills that share tags, products or a category with Discover: Is This A Problem (anthropics/claude-for-legal, 9.6k stars), Discover Plugins (ruvnet/ruflo, 74k stars), Aas Discover (sickn33/agentic-awesome-skills, 47k stars) and Opportunity Solution Tree (phuryn/pm-skills, 27k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Discover?

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.