New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Call Politeness Strategy Auditor

by CALLE-AI in CALLE-AI/awesome-phone-call-agents

Offline experimental CALL-E helper that grades agent request phrasing against Stanford-politeness strategy markers, flagging bald imperatives ("Give me your date of birth.") and condescension…

MITAuto-check passed

Install Call Politeness Strategy Auditor

skills CLI
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill call-politeness-strategy-auditor -a claude-code

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

GitHub CLI
$ gh skill install CALLE-AI/awesome-phone-call-agents call-politeness-strategy-auditor --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/CALLE-AI/awesome-phone-call-agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/call-politeness-strategy-auditor .claude/skills/call-politeness-strategy-auditor && 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
call-politeness-strategy-auditor
GitHub stars
107
Token cost
~2k tokens
SKILL.md length
982 words
Files
10 (incl. scripts, references)
Skills in repo
101
Repo updated
First seen
Licence
MIT

At a glance

Offline experimental CALL-E helper that grades agent request phrasing against Stanford-politeness strategy markers, flagging bald imperatives ("Give me your date of birth.") and condescension…

  • Works in 5 steps: Mask first. Any 7+-digit run (separators… → Detect request sentences in agent turns… → Mark strategies on each request… → …
  • SKILL.md covers When To Use, When Not To Use, Verdicts and How It Works, plus 4 more sections
  • Runs Python scripts from its folder; calls python and python3

What it does

Call Politeness Strategy Auditor is an agent skill from CALLE-AI/awesome-phone-call-agents. Offline experimental CALL-E helper that grades agent request phrasing against Stanford-politeness strategy markers, flagging bald imperatives ("Give me your date of birth.") and condescension markers, with a softened-request goal template; not a rudeness judgment, a content-scope check, or authorization to act.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including scripts and reference files (for example `references/example-call-result-bald.json`, `references/example-call-result-mixed.json` and `references/example-call-result-no-requests.json`).

The repository describes itself as: Portable phone-call Agent Skills, apps, examples, adapters, and scheduler recipes for AI agents. The licence is MIT.

Example prompts

  • “Give me your date of birth.”
  • “/call-politeness-strategy-auditor”

Requirements

  • Python 3

Workflow steps

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

  1. Mask first. Any 7+-digit run (separators included) is masked in
  2. Detect request sentences in agent turns only: imperatives
  3. Mark strategies on each request sentence: modal, please (incl.
  4. Grade. Question-form and implicit-form requests are structurally
  5. Condescension markers (dear, honey, sweetie, sugar, sonny,

What it can do on your machine

Read from SKILL.md and the folder at commit 38d4118. 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

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python
    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • cambridge.org
    • aclanthology.org

    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

Call Politeness Strategy Auditor loads about 2k tokens when it runs, and up to ~4.7k if it reads all its reference files. Until then it costs about 86 tokens; SKILL.md has 982 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~86
When it runs · the whole SKILL.md, loaded when a task matches
~2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.7k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from CALLE-AI/awesome-phone-call-agents at commit 38d4118, republished under its MIT licence (© CALLE-AI). 982 words, ~2,043 tokens.

Download SKILL.mdSave it as .claude/skills/call-politeness-strategy-auditor/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.
name
call-politeness-strategy-auditor
description
Offline experimental CALL-E helper that grades agent request phrasing against Stanford-politeness strategy markers, flagging bald imperatives ("Give me your date of birth.") and condescension markers, with a softened-request goal template; not a rudeness judgment, a content-scope check, or authorization to act.
license
MIT
research
arXiv:1306.6078, arXiv:2407.12814

call-politeness-strategy-auditor

Every data request is a small face-threatening act. Some agents pay the courtesy toll; some just grab.

Phone agents live on requests: date of birth, spelling, confirmations, read-backs. Conversation-analytic politeness theory treats every such request as a face-threatening act that speakers normally redress - a modal, a "please", a hedge, an apology for the intrusion. A voice agent that fires bare imperatives ("Give me your date of birth." "Spell your last name.") gets the same data with more friction, and reads as a robot that grabs. No sibling skill audits request phrasing: call-agent-certainty-calibrator grades epistemic wording, call-leading-question-guard grades coercive question form, call-data-minization-auditor grades whether the collection was in goal scope. This skill grades the FORM of the ask - and only the form: a polite request for out-of-scope data still fails the minimization auditor.

When To Use

  • after any collection or confirmation call, to review how the agent asked for what it needed - before reusing the goal template on the next campaign
  • when iterating on goal wording to reduce caller friction and hang-ups
  • alongside call-data-minimization-auditor (scope) and call-agent-certainty-calibrator (epistemics) for a full conduct pass

When Not To Use

  • as a content-scope check - politeness never sanitizes WHAT is asked (that is call-data-minimization-auditor's object)
  • to judge sincerity, warmth, or cultural style; the markers are lexical and English-only, and directness norms vary across cultures
  • as a rudeness verdict on humans - this grades the agent's request design

Verdicts

VerdictMeaningSuggested routing
COURTEOUSzero bald requests; every request carries >= 1 strategy markerproceed; keep the card with the call record
MIXED1-2 bald requests below the detection thresholdsreview flagged turns; soften the goal template
BALD_REQUESTS_DETECTED>= 3 bald requests, or a majority (with >= 3 requests)rework the goal template before the next campaign
NO_AGENT_REQUESTSno request sentences found (informational call)nothing to grade

How It Works

Deterministic, offline, no LLM:

  1. Mask first. Any 7+-digit run (separators included) is masked in every turn and the post_summary before analysis, keeping the last two characters. Masked digits never affect classification. This heuristic is not complete anonymization; other identifiers and phone formats may remain.
  2. Detect request sentences in agent turns only: imperatives (verb-initial: give/spell/repeat/confirm/read/write down/enter/press/ bring/hand/state/list/say/tell/provide, plus "hold the line"), questions (sentence ends "?"), and implicit redressed forms ("If you would...", "I was wondering if...", "Would you mind...", "Might I ask...", "I'm afraid...", "I'd like you to...", "Sorry to trouble you...", "Forgive me/the...", "My apologies..."). Transitional waits ("Hold on", "One moment", "Bear with me", "Give me a second") and agent-self-directed sentences ("Let me check", "I'll be right back") are not requests. "Say, ..." / "Tell me, ..." exclamatory openers (comma right after the verb) are guarded out; such a sentence still counts as a question when it ends with "?".
  3. Mark strategies on each request sentence: modal, please (incl. "kindly"), gratitude, apologizing, hedging, deference, counterfactual, indirect, formal address (honorific + surname). The sentence split is honorific-safe ("Mr. Nguyen, please confirm..." stays one sentence).
  4. Grade. Question-form and implicit-form requests are structurally never BALD - the interrogative and the indirect stem are themselves redress. An imperative with zero strategy markers is BALD.
  5. Condescension markers (dear, honey, sweetie, sugar, sonny, good girl/boy) are flagged per agent turn as advisories - they never change the verdict by themselves.

The card reports per-request evidence (sentence, form, strategies, grade), counts, the verdict, and a fixed honesty disclaimer.

Show full SKILL.md (433 more words)Show less

Foundation

FunctionCitation
Theory anchor (face-threatening acts; bald-on-record vs redress)Brown, P., & Levinson, S. C. (1987). Politeness: Some Universals in Language Usage. Cambridge University Press. ISBN 978-0521313551. https://www.cambridge.org/highereducation/books/politeness/89113EE2FB4A1D254D4A8D2011E542E4
Method anchor (strategy taxonomy + lexicon we operationalize)Danescu-Niculescu-Mizil, C., Sudhof, M., Jurafsky, D., Leskovec, J., & Potts, C. (2013). "A computational approach to politeness with application to social factors." ACL 2013 (Vol. 1: Long Papers), pp. 250-259. https://aclanthology.org/P13-1025/, arXiv:1306.6078. NOTE: no DOI listed on the anthology page - cite by URL + arXiv only
Computational feasibility (tag strategies, then act on them)Madaan, A., Setlur, A., Parekh, T., Poczos, B., Neubig, G., Yang, Y., Salakhutdinov, R., Black, A. W., & Prabhumoye, S. (2020). "Politeness Transfer: A Tag and Generate Approach." ACL 2020, pp. 1869-1881. DOI 10.18653/v1/2020.acl-main.169. CORRECTION: 9th author is Shrimai Prabhumoye - web-search snippets misattribute "Norman Jouppi" (TPU-hardware researcher, unrelated)
Recency (field survey, published)Priya, P., Firdaus, M., & Ekbal, A. (2024). "Computational Politeness in Natural Language Processing: A Survey." ACM Computing Surveys 56(9), Article 241 (May 2024). DOI 10.1145/3654660, arXiv:2407.12814
Domain evidence, agent direction (users significantly prefer polite AI)O'Driscoll, A., & Blackwell, A. F. (2025). "Social Norms, Social AI: Investigating the Effects of AI (Im)politeness and Gender on User Perception." BCS HCI 2025 (HCI4AI). DOI 10.14236/ewic/BCSHCI2025.66. CORRECTION: full title begins "Social Norms, Social AI:" - truncated snippet titles omit the prefix

Boundaries

  • call-data-minimization-auditor grades WHETHER a data request was in goal scope; this skill grades HOW it was phrased. Politeness grading never sanitizes content - run both for a full collection review.
  • call-leading-question-guard grades coercive question FORM (tag, negative interrogative, presupposition); a soft modal question can still be leading in content, and a bald imperative is never a question.
  • call-agent-certainty-calibrator grades epistemic wording (record-shows / I-believe / hedged); politeness markers are not epistemic claims.

Limitations

Stated, not hidden:

  • English lexicons only; directness norms vary across cultures - a terse request is not necessarily rude, and this skill does not judge culture.
  • Strategy marking is sentence-local: a "please" in an adjacent sentence does not soften a bald imperative in its own sentence.
  • Implicit requests are recognized only for the listed redressed stems; other paraphrases ("I still need you to...") are ungraded unless they match an imperative or question form.
  • Condescension markers are advisory only; "dear" is endearment in some registers and diminutive in others - a human decides.
  • Masked digit runs cannot carry strategies and are never graded.
  • The interrogative-is-redress rule means a question can never grade BALD by construction, even a brusque one ("Date of birth?"). Documented trade-off of the deterministic rule.

Testing

Run the suite from the skill root:

bash
python -m pytest scripts/test_politeness_strategy_auditor.py -q
python3 scripts/test_politeness_strategy_auditor.py

Both commands require pytest to be installed; the second invokes the test file's runner directly.

© CALLE-AI, 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 9 other files (scripts, references) in skills/call-politeness-strategy-auditor of CALLE-AI/awesome-phone-call-agents.

  • SKILL.md
  • references/example-call-result-bald.json
  • references/example-call-result-mixed.json
  • references/example-call-result-no-requests.json
  • references/example-call-result.json
  • references/example-goal.txt
  • references/examples.md
  • references/safety.md
  • scripts/politeness_strategy_auditor.py
  • scripts/test_politeness_strategy_auditor.py

Open the folder on GitHubat commit 38d4118

Compare with similar skills

Call Politeness Strategy Auditor 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.

Call Politeness Strategy Auditor compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Call Politeness Strategy Auditor this skillCALLE-AI/awesome-phone-call-agents107—~2kAutomated safety check: PassMIT
Grade Iteratealirezarezvani/claude-skills28k—~1kAutomated safety check: PassMIT
Experimental Designaiming-lab/AutoResearchClaw15k—~286Automated safety check: PassMIT
Pua Offlinetanweai/pua20k—~143Automated safety check: PassMIT
Indexing Issue Auditorsickn33/agentic-awesome-skills47k1 repos~1.5kAutomated safety check: PassMIT
Offline Fallbackthedaviddias/Front-End-Checklist74k—~488Automated safety check: PassMIT

Similar skills

  • Grade Iterate

    alirezarezvani/claude-skills

    Phase 3 of building a Claude Managed Agent — the bounded grade→iterate loop.

    28k GitHub stars~1k tokensUpdated 1 mo ago
    EducationAuto-check passed

  • Experimental Design

    aiming-lab/AutoResearchClaw

    Best practices for designing reproducible ML experiments. An agent skill from aiming-lab/AutoResearchClaw.

    15k GitHub stars~286 tokensUpdated 1 mo ago
    Research & ScienceAuto-check passed

  • Pua Offline

    tanweai/pua

    PUA offline alias for Codex. An agent skill from tanweai/pua.

    20k GitHub stars~143 tokensUpdated 1 mo ago
    Auto-check passed

  • Indexing Issue Auditor

    sickn33/agentic-awesome-skills

    High-level technical SEO and site architecture auditor. An agent skill from sickn33/agentic-awesome-skills.

    47k GitHub starsUsed in 1 repo~1.5k tokens
    Marketing & SEOAuto-check passed

  • Offline Fallback

    thedaviddias/Front-End-Checklist

    A skill your agent uses when adding PWA capabilities, implementing a service worker, or improving the experience for users on unreliable network connections.

    74k GitHub stars~488 tokensUpdated 4 days ago
    Auto-check passed

  • Analyzing Offline Evaluations

    PostHog/posthog

    Official

    Inspect and compare offline AI evaluation experiments, diagnose case-level regressions, and follow scorer history across application, model, or prompt changes.

    40k GitHub stars~1.6k tokensUpdated yesterday
    Auto-check passed

More from CALLE-AI/awesome-phone-call-agents

All 101 skills in this repo

  • Accessible Outing Verifier

    CALLE-AI/awesome-phone-call-agents

    Demonstrates advisory accessibility-planning checks with offline fixtures and a proposed bounded CALL-E workflow; use for exploring unknown or qualified venue claims without making calls.

    107 GitHub stars~1.4k tokensUpdated yesterday
    Auto-check passed

  • Ground Truth Gate

    CALLE-AI/awesome-phone-call-agents

    A skill your agent uses when an agent holds some evidence for a physical-world claim but the evidence is broader, narrower, or older than the exact question asked, and it must first decide whether a…

    107 GitHub stars~3.3k tokensUpdated yesterday
    Auto-check passed

  • Is It Accessible

    CALLE-AI/awesome-phone-call-agents

    Call a venue and ask the accessibility questions that matter to one specific person — step-free entry, hearing loop, guide dogs, quiet hours, changing places — then return a per-need verdict backed…

    107 GitHub stars~4.3k tokensUpdated yesterday
    Auto-check passed

  • Landmark Navigation Assist

    CALLE-AI/awesome-phone-call-agents

    Turns a pre-written, building-level location config into a CALL-E outbound phone-call task that guides a delivery driver through the last few hundred metres to a specific building using landmarks…

    107 GitHub stars~1.6k tokensUpdated yesterday
    Auto-check passed

  • Research Gap Call Verifier

    CALLE-AI/awesome-phone-call-agents

    Turn cited business research into a bounded, approval-gated phone-call plan that asks only unresolved factual questions, then reconcile CALL-E-compatible results without treating voicemail, refusal…

    107 GitHub stars~1.7k tokensUpdated yesterday
    Auto-check passed

  • Structured Outcome Followup Call

    CALLE-AI/awesome-phone-call-agents

    Place a goal-driven CALL-E call that collects specific structured answers, score those answers against a deterministic rubric you supply, and conditionally trigger a follow-up action — all runnable…

    107 GitHub stars~1.4k tokensUpdated yesterday
    Auto-check passed

Questions about Call Politeness Strategy Auditor

What does Call Politeness Strategy Auditor do?

Offline experimental CALL-E helper that grades agent request phrasing against Stanford-politeness strategy markers, flagging bald imperatives ("Give me your date of birth.") and condescension…. Call Politeness Strategy Auditor is an agent skill from CALLE-AI/awesome-phone-call-agents.") and condescension markers, with a softened-request goal template; not a rudeness judgment, a content-scope check, or authorization to act.

How do I install Call Politeness Strategy Auditor in Claude Code?

Run `npx skills add CALLE-AI/awesome-phone-call-agents --skill call-politeness-strategy-auditor -a claude-code`. Or copy the skill folder (skills/call-politeness-strategy-auditor in CALLE-AI/awesome-phone-call-agents) into .claude/skills/call-politeness-strategy-auditor in your project. Claude Code loads it when a task matches its description.

How do I install Call Politeness Strategy Auditor in Codex?

Run `npx skills add CALLE-AI/awesome-phone-call-agents --skill call-politeness-strategy-auditor -a codex`. Or copy the skill folder (skills/call-politeness-strategy-auditor in CALLE-AI/awesome-phone-call-agents) into .agents/skills/call-politeness-strategy-auditor in your project. Codex loads it when a task matches its description.

Can I use Call Politeness Strategy Auditor 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 CALLE-AI/awesome-phone-call-agents --skill call-politeness-strategy-auditor -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/call-politeness-strategy-auditor, .gemini/skills/call-politeness-strategy-auditor, .github/skills/call-politeness-strategy-auditor and .opencode/skills/call-politeness-strategy-auditor in your project.

What does Call Politeness Strategy Auditor need to run?

Going by SKILL.md and its folder, Call Politeness Strategy Auditor needs Python for the scripts in its folder and the command-line tools its instructions call (python and python3). Our summary lists: Python 3.

Does Call Politeness Strategy Auditor access the network?

SKILL.md names 2 domains. As links in the text: cambridge.org and aclanthology.org. This is read from the text; nothing was executed.

Is Call Politeness Strategy Auditor 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Call Politeness Strategy Auditor use?

Call Politeness Strategy Auditor is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Call Politeness Strategy Auditor use?

About 2k tokens (SKILL.md is roughly 8.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.7k tokens, read only when the agent opens those files.

What are the alternatives to Call Politeness Strategy Auditor?

Skills that share tags, products or a category with Call Politeness Strategy Auditor: Grade Iterate (alirezarezvani/claude-skills, 28k stars), Experimental Design (aiming-lab/AutoResearchClaw, 15k stars), Pua Offline (tanweai/pua, 20k stars) and Indexing Issue Auditor (sickn33/agentic-awesome-skills, 47k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Call Politeness Strategy Auditor?

CALLE-AI (a GitHub organization) maintains it in CALLE-AI/awesome-phone-call-agents, which has 107 GitHub stars. The repository holds 101 skills in this directory. The repository was last updated on October 10, 2026.

Source: CALLE-AI/awesome-phone-call-agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.