agentleFS
KshitijpalsinghtomarKshitijpalsinghtomar/depth-skills/skills/ds-clarify/SKILL.md

clarify

Skill5 starsChanged 43 days agoMITView on GitHub

Decides whether to ask clarifying questions or proceed with an answer, optimizing for information value vs. delay cost. Now with mathematical depth metrics, question value calculus, answer readiness , and verification gates.

80 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use quieter, arrange and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • HK-hubSkill

    quieter

    Tones down visually aggressive or overstimulating designs, reducing intensity while preserving quality. Use when the user mentions too bold, too loud, overwhelming, aggressive, garish, or wants a calmer, more refined aesthetic.

    Also in 29 projects that use this

  • HK-hubSkill

    arrange

    Improve layout, spacing, and visual rhythm. Fixes monotonous grids, inconsistent spacing, and weak visual hierarchy. Use when the user mentions layout feeling off, spacing issues, visual hierarchy, crowded UI, alignment problems, or wanting better composition.

    Also in 14 projects that use this

  • zwy123zwySkill

    normalize

    将用户随意输入的提示词规范化为结构清晰、便于大模型执行的标准化提示词。适用于需要整理、统一提示词格式时调用。

    Also in 19 projects that use this

  • owshq-mecSkill

    layout

    Improve layout, spacing, and visual rhythm. Fixes monotonous grids, inconsistent spacing, and weak visual hierarchy. Use when the user mentions layout feeling off, spacing issues, visual hierarchy, crowded UI, alignment problems, or wanting better composition.

    Also in 8 projects that use this

  • +8 more from HK-hub

The file

We split this skill into the rules it gives an agent

11 rules. 0 are common, 10 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 77 edited versions we read keep these.

  • 10

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Every prompt is either ready for an answer or missing something that would dramatically improve it.

    line 36
  2. Distinctive:

    Ask a question the user already answered (implicitly or in prior context)

    line 49
  3. Distinctive:

    Write your assessment of the incoming prompt:

    line 73
  4. Distinctive:

    Write your final decision and reasoning:

    line 148
  5. Distinctive:

    Artifact: Ask/Answer verdict.

    line 161
  6. Very specific:

    If recursion_depth > 1, apply this entire protocol to your own output from Steps 1-4.

    line 298
  7. Distinctive:

    Treat your previous output as the "answer under review"

    line 301
  8. Distinctive:

    Run Steps 1-4 on it

    line 302
  9. Distinctive:

    If RSM > 0.95 AND SI_d ≥ SI_{d-1} for 2 consecutive depths → CONVERGED, stop

    line 308
  10. Distinctive:

    If RSM < 0.80 OR SI_d < SI_{d-1} for 2 consecutive depths → DIVERGED, stop, use best depth

    line 309
  11. Distinctive:

    If d = max_depth → stop, use current

    line 310

“Common” is measured: kept by at least 6 in 10 of the 77 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

CLARIFY v2.0 — Ask/Answer Decision Engine (Premium)

Mathematical Compliance: This skill implements the DEPTH-MATHEMATICS specification. It computes and outputs ADS phase activation, CEI components, SI components, cognitive traces (clarification calculus, readiness ), and verifies Gates V1, V3. It formalizes the ask/answer decision as a value calculus.

Every prompt is either ready for an answer or missing something that would dramatically improve it. You must decide: ask now, or answer now and refine later?

The wrong choice costs:

  • Asking when you could answer: Wastes user's time, breaks flow, signals incompetence
  • Answering when you should ask: Delivers wrong thing, requires revision cycles, erodes trust

This skill decides. Now it quantifies the decision.


The Failure Mode You Must Recognize

You are about to either:

  • Ask a question the user already answered (implicitly or in prior context)
  • Answer a question that will require 3 follow-up messages to converge

Both signal the same underlying failure: you assessed the prompt's information state incorrectly.

Cargo cult clarification asks generic questions without scoring their value. This skill rejects it.


The Protocol

0 — PARAMETERIZE: Read Thinking Parameters

Read thinking_parameters:

  • min_questions (default 1) — minimum questions to generate if asking
  • value_threshold (default 3) — minimum total value score to ask
  • require_calculus (default true) — compute clarification calculus
  • require_readiness_ (default true) — issue answer readiness
  • recursion_depth (default 1) — recursive self-audit rounds

1 — ASSESS INTENT READINESS

Write your assessment of the incoming prompt:

INTENT ASSESSMENT
────────────────────────────────────────
Explicit request:    [what user literally asked]
Inferred intent:    [what they probably need - write one sentence]
Missing pieces:     [what you don't know that would change the answer]
Confidence:         [0-100% that you understand what they need]
Urgency signal:     [does prompt contain "urgent", "asap", "right now"?]
Prior context:      [relevant conversation history - yes/no]
Information State:  [COMPLETE / PARTIAL / FRAGMENTED / UNKNOWN]
────────────────────────────────────────

Artifact: Intent assessment.

CEI Component: Intent Assessment (weight 1.0)


2 — CLARIFICATION CALCULUS (if require_calculus = true)

NEW IN v2.0 — Formal value calculus for each potential question.

For each potential question, calculate its value:

QUESTION VALUE CALCULUS
────────────────────────────────────────
Question: [write the question]

Information Impact (II):
  If I knew the answer, how much would my response change?
    - Substantially (different approach): II = 2.0
    - Moderately (refinement): II = 1.0
    - Minimally (same answer either way): II = 0.0

User Cooperation Likelihood (UCL):
  How likely will the user answer this?
    - High (obvious gap, easy to answer): UCL = 1.0
    - Medium (reasonable to ask): UCL = 0.5
    - Low (intrusive, unclear): UCL = 0.0

Delay Cost (DC):
  What is the delay cost?
    - Low (quick answer): DC = 1.0
    - Medium (some back-and-forth): DC = 0.5
    - High (derails conversation): DC = 0.0

Prior Answer Probability (PAP):
  How likely is the user to have already answered this implicitly?
    - Low (genuinely unknown): PAP = 1.0
    - Medium (might be in context): PAP = 0.5
    - High (likely answered): PAP = 0.0

TOTAL VALUE = II × UCL × PAP + DC
  [0.0 - 3.0]

Decision Threshold: [value_threshold, default 3.0 scaled]
  If TOTAL VALUE ≥ threshold → ASK
  If TOTAL VALUE < threshold → ANSWER (with caveats if 1-2)
────────────────────────────────────────

Minimum min_questions questions evaluated.

Artifact: clarification_calculus — formal value calculus for each question.

CEI Component: Clarification Calculus (weight 3.0)


3 — DELIVER VERDICT

Write your final decision and reasoning:

ASK/ANSWER VERDICT
────────────────────────────────────────
Decision:          [ASK / ANSWER / ANSWER-WITH-CAVEATS]
Primary question:  [if asking - write it]
Reasoning:         [2-3 sentences why this is the right call, referencing calculus]
What happens next: [if asking - wait for response]
                     [if answering - deliver and note what I'd ask if I could]
────────────────────────────────────────

Artifact: Ask/Answer verdict.

CEI Component: Verdict (weight 1.0)


4 — ANSWER READINESS (if require_readiness_ = true)

NEW IN v2.0 — Formal of answer readiness.

ANSWER READINESS 
────────────────────────────────────────
 ID: arc_<timestamp>_<hash>
Prompt: [description]
Information State: [COMPLETE / PARTIAL / FRAGMENTED / UNKNOWN]

Questions Evaluated: [N]
Questions to Ask: [N]
Total Value Score: [0.0-3.0]
Threshold: [value_threshold]

Readiness: [READY / NOT_READY / CONDITIONAL]

If NOT_READY:
  Missing Information: [list]
  Questions to Ask: [list]
  Expected Value Gain: [value]

If CONDITIONAL:
  Caveats: [list]
  Questions to Ask if Time Permits: [list]

Validity: [date or condition]
────────────────────────────────────────

Artifact: readiness_ — formal verification of answer readiness.

CEI Component: Readiness (weight 2.0)


5 — METRICS: Compute and Output Depth Metrics

5.1 Phase Activation (for ADS)

{
  "phase": 1,
  "skill": "clarify",
  "artifact_completeness": 0.XX,
  "gate_pass_rate": 0.XX,
  "external_validity": 0.XX,
  "human_eval": 0.XX,
  "phase_activation": 0.XX
}

5.2 CEI Components

{
  "cei_components": {
    "intent_assessment": 1.0,
    "clarification_calculus": 3.0,
    "verdict": 1.0,
    "readiness_": 2.0,
    "total_complexity_weight": 7.0,
    "output_tokens": N,
    "wall_time_seconds": T,
    "cei": 0.XX
  }
}

5.3 SI Components

{
  "si_components": {
    "c2_c3_count": 0,
    "contrarian_viable": false,
    "fatal_attacks": 0,
    "boundary_violations": 0,
    "assumption_reversals": 0,
    "si_total": 0
  }
}

5.4 Cognitive Traces

Clarification Calculus:

{
  "type": "clarification_calculus",
  "questions": [
    {"question": "...", "II": 2.0, "UCL": 1.0, "DC": 1.0, "PAP": 1.0, "total": 3.0, "decision": "ASK"},
    {"question": "...", "II": 1.0, "UCL": 0.5, "DC": 0.5, "PAP": 0.5, "total": 0.625, "decision": "ANSWER"}
  ],
  "threshold": 3.0,
  "questions_to_ask": N
}

Readiness : (as defined in Step 4)

5.5 Depth Contribution

{
  "skill": "clarify",
  "version": "2.0",
  "metrics": {"ads_contribution": 0.XX, "cei": 0.XX, "si": 0, "ec": 0.XX},
  "gates_verified": {"V1": true, "V3": true},
  "limitations": ["..."]
}

6 — GATES: Verify Before Delivery

MANDATORY — Verify these gates PASS:

GateCheckPass Condition
V1Assumption CoverageEvery missing piece in intent assessment traces to a question or caveat
V3Opposition AuthenticityIf ANSWER: calculus shows why asking would be lower value

If any gate FAILS: Return to relevant step and fix. Do not deliver.


7 — RECURSIVE SELF-AUDIT (if recursion_depth > 1)

If recursion_depth > 1, apply this entire protocol to your own output from Steps 1-4.

For each recursion level d = 2 to recursion_depth:

  1. Treat your previous output as the "answer under review"
  2. Run Steps 1-4 on it
  3. Compute RSM (Recursive Stability Metric):
    RSM = 1 - Semantic_Distance(output_d, output_{d-1})
  4. Convergence Check:
    • If RSM > 0.95 AND SI_d ≥ SI_{d-1} for 2 consecutive depths → CONVERGED, stop
    • If RSM < 0.80 OR SI_d < SI_{d-1} for 2 consecutive depths → DIVERGED, stop, use best depth
    • If d = max_depth → stop, use current

Artifact: recursion_log with RSM and SI at each depth.

CEI Component: Recursive Audit (weight 5.0 per level)


8 — INTERFERENCE: Receive and Provide

Receive from prior skills:

From SkillInterferenceUse
BOUNDARY-DETECTORKnowledge gapsAdd as missing pieces in intent assessment
DEEP-THINKAssumptions from restatementInform missing pieces
EXCAVATEC2/C3 assumptionsAdd as missing pieces

Provide to later skills:

To SkillInterferenceMinimum ADS Gain
DEEP-THINKMissing pieces → assumption flags+0.15
EXCAVATEMissing pieces → C2/C3 assumptions+0.20
CONDUCTORReadiness → skill selection+0.15
SHALLOWReadiness → depth budget+0.10

Artifact: interference_log (received and provided).


The Deeper Purpose

The model defaults to answering — it's what it's built to do. But sometimes the highest-value action is to slow down and ask. This skill makes that decision explicit and scored, rather than relying on intuition. The scoring system captures: (1) information impact, (2) user cooperation likelihood, (3) delay cost, (4) prior answer probability. When in doubt, the framework defaults to answering with caveats over asking unnecessarily. Now it's a formal calculus with s.


MATHEMATICS COMPLIANCE

Phase Activation Output

This skill contributes to Phase 1. It outputs:

{
  "phase": 1,
  "skill": "clarify",
  "artifact_completeness": 0.XX,
  "gate_pass_rate": 0.XX,
  "external_validity": 0.XX,
  "human_eval": 0.XX,
  "phase_activation": 0.XX
}

CEI Components

  • Artifacts produced: intent_assessment (1.0), clarification_calculus (3.0), verdict (1.0), readiness_ (2.0)
  • Total complexity weight: 7.0

SI Components

  • C2/C3 assumptions: 0
  • Contrarian viable: false
  • Fatal attacks: 0
  • Boundary violations: 0
  • Assumption reversals: 0

Cognitive Traces Produced

  • Clarification Calculus
  • Readiness
  • Decision Landscape

Gates Verified

  • V1 [ ] V2 [x] V3 [ ] V4 [ ] V5 [ ] V6
  • Human Eval [ ] Held-Out [ ] Ensemble

Required Interferences

  • From BOUNDARY-DETECTOR: Knowledge gaps → missing pieces
  • From DEEP-THINK: Assumptions → missing pieces
  • From EXCAVATE: C2/C3 assumptions → missing pieces

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.