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Offline experimental CALL-E transcript helper that flags agent statements of specific values absent from the goal facts and callee turns (over-assertion) and goal facts spoken with hedges but no…
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Install Call Agent Certainty Calibrator
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill call-agent-certainty-calibrator -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install CALLE-AI/awesome-phone-call-agents call-agent-certainty-calibrator --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-agent-certainty-calibrator .claude/skills/call-agent-certainty-calibrator && rm -rf skills-srcUse ~/.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/
Install the "call-agent-certainty-calibrator" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/call-agent-certainty-calibrator into .claude/skills/call-agent-certainty-calibrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "call-agent-certainty-calibrator", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/call-agent-certainty-calibratorType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill call-agent-certainty-calibrator -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install CALLE-AI/awesome-phone-call-agents call-agent-certainty-calibrator --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/call-agent-certainty-calibrator .agents/skills/call-agent-certainty-calibrator && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "call-agent-certainty-calibrator" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/call-agent-certainty-calibrator into .agents/skills/call-agent-certainty-calibrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "call-agent-certainty-calibrator", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill call-agent-certainty-calibrator -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install CALLE-AI/awesome-phone-call-agents call-agent-certainty-calibrator --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/call-agent-certainty-calibrator .cursor/skills/call-agent-certainty-calibrator && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "call-agent-certainty-calibrator" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/call-agent-certainty-calibrator into .cursor/skills/call-agent-certainty-calibrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "call-agent-certainty-calibrator", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/CALLE-AI/awesome-phone-call-agents.git --path skills/call-agent-certainty-calibrator--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill call-agent-certainty-calibrator -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install CALLE-AI/awesome-phone-call-agents call-agent-certainty-calibrator --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/call-agent-certainty-calibrator .gemini/skills/call-agent-certainty-calibrator && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "call-agent-certainty-calibrator" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/call-agent-certainty-calibrator into .gemini/skills/call-agent-certainty-calibrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "call-agent-certainty-calibrator", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install CALLE-AI/awesome-phone-call-agents call-agent-certainty-calibratorInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill call-agent-certainty-calibrator -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/call-agent-certainty-calibrator .github/skills/call-agent-certainty-calibrator && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "call-agent-certainty-calibrator" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/call-agent-certainty-calibrator into .github/skills/call-agent-certainty-calibrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "call-agent-certainty-calibrator", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill call-agent-certainty-calibrator -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install CALLE-AI/awesome-phone-call-agents call-agent-certainty-calibrator --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/call-agent-certainty-calibrator .opencode/skills/call-agent-certainty-calibrator && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "call-agent-certainty-calibrator" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/call-agent-certainty-calibrator into .opencode/skills/call-agent-certainty-calibrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "call-agent-certainty-calibrator", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
call-agent-certainty-calibrator- GitHub stars
- 107
- Token cost
- ~1.2k tokens
- SKILL.md length
- 552 words
- Files
- 9 (incl. scripts, references)
- Skills in repo
- 101
- Repo updated
- First seen
- Licence
- MIT
At a glance
Offline experimental CALL-E transcript helper that flags agent statements of specific values absent from the goal facts and callee turns (over-assertion) and goal facts spoken with hedges but no…
- SKILL.md covers When To Use, When Not To Use, Workflow and Scientific Foundation, plus 1 more section
- Runs Python scripts from its folder; calls python3
What it does
Call Agent Certainty Calibrator is an agent skill from CALLE-AI/awesome-phone-call-agents. Offline experimental CALL-E transcript helper that flags agent statements of specific values absent from the goal facts and callee turns (over-assertion) and goal facts spoken with hedges but no source attribution (over-hedging), plus a three-tier calibrated-wording goal template. It is not proof the agent invented anything, a measure of internal confidence, or authorization to act.
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts and reference files (for example `references/example-transcript-calibrated.json`, `references/example-transcript-overassertive.json` and `references/example-transcript.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
- “/call-agent-certainty-calibrator”
Requirements
- Python 3
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:
python3
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
Call Agent Certainty Calibrator loads about 1.2k tokens when it runs, and up to ~4k if it reads all its reference files. Until then it costs about 104 tokens; SKILL.md has 552 words of instructions outside code blocks.
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). 552 words, ~1,160 tokens.
Download SKILL.mdSave it as
.claude/skills/call-agent-certainty-calibrator/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.- name
- call-agent-certainty-calibrator
- description
- Offline experimental CALL-E transcript helper that flags agent statements of specific values absent from the goal facts and callee turns (over-assertion) and goal facts spoken with hedges but no source attribution (over-hedging), plus a three-tier calibrated-wording goal template. It is not proof the agent invented anything, a measure of internal confidence, or authorization to act.
- license
- MIT
call-agent-certainty-calibrator
An agent that invents specifics is worse than one that says 'I don't know'. An agent that hedges its own record is worse than useless.
Language models can express calibrated confidence in words - and, left unchecked, they drift both ways: asserting specifics nobody gave them ("free delivery on Friday!") and softening facts they were told to state ("I think it might be $45?"). On a phone call both failures are audible, and both end up in the outcome. This skill grades the agent's stated values against the goal it was given.
When To Use
- after any fact-bearing CALL-E call, together with the goal text (or plan JSON) the call was built from
- when an outcome contains a value nobody remembers putting in the goal
- before placing calls, to craft wording that speaks record facts with authority and admits gaps honestly
When Not To Use
- without the goal file; calibration is graded against the goal's facts and the CLI requires it
- to prove the agent fabricated a value; an OVER-ASSERTED value may be true and simply missing from the goal text - the card routes to verification, always
- on the callee's certainty;
provenance-gradegrades how the callee knew what they said - during a call; strictly post-call analysis plus pre-call goal crafting
Workflow
Audit a finished call
python3 scripts/agent_certainty_calibrator.py analyze \
--transcript path/to/call-result.json --goal-file path/to/goal.txtReads the real get_call_run result shape ({status, result: {transcript}})
or the flat fixture shape used by sibling skill fixtures; the goal file is
plain text or a JSON with a goal field. Emits a card:
goal_facts: amounts, dates, times extracted from the goal textover_assertions[]: agent-stated values in neither the goal facts nor any callee turn - unsourced specificsover_hedges[]: goal facts spoken in a sentence with hedge words ("i think", "might be", "around", ...) and no source markercalibrated_statements: goal facts stated plainly or attributed ("our records show...") - source attribution overrides a hedgeverdict:CALIBRATED/OVERASSERTIVE/OVERHEDGED/MIXED, plusunclearpaths (empty transcript, no agent turns, goal without extractable facts)
Values a CALLEE introduced and the agent merely confirmed are never over-assertions: repeating the person's own value back is confirmation, not invention.
Show full SKILL.md (206 more words)Show less
Craft the calibrated goal
python3 scripts/agent_certainty_calibrator.py craft --scenario calibrated-fact-statingEmits the plan_call inputs JSON whose goal implements three tiers of verbalized confidence: record facts stated with attribution, estimates labeled as estimates, and gaps admitted exactly.
Scientific Foundation
| Research | Relevance |
|---|---|
| Teaching Models to Express Their Uncertainty in Words (Lin, Hilton, Evans, TMLR 2022, arXiv 2205.14334) | Established verbalized confidence: models can and should express calibrated uncertainty in language - our three-tier wording operationalizes it for calls |
| Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs (Xiong et al., ICLR 2024, arXiv 2306.13063) | Empirical demonstration that verbalized confidence is often miscalibrated - the failure this skill audits on the transcript axis |
Citation notes recorded during verification: Lin et al. appeared in TMLR
2022 (arXiv 2205.14334); Xiong et al. at ICLR 2024 (arXiv 2306.13063).
This skill compares lexical value statements only, has no access to model
internals, and labels every output analysis_mode: "heuristic".
Differences from sibling skills
call-sycophancy-guardcatches the agent folding under the person's pushback; this skill catches unsolicited drift - invention and groundless hedging with nobody pushing.provenance-gradegrades the callee's epistemic state; this skill grades the agent's stated certainty against the goal record.call-cross-call-consistency-checkercompares the organization across calls; this skill compares the agent against its own instructions within one call.
© 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 8 other files (scripts, references) in skills/call-agent-certainty-calibrator of CALLE-AI/awesome-phone-call-agents.
- SKILL.md
- references/example-goal.txt
- references/example-transcript-calibrated.json
- references/example-transcript-overassertive.json
- references/example-transcript.json
- references/examples.md
- references/safety.md
- scripts/agent_certainty_calibrator.py
- scripts/test_agent_certainty_calibrator.py
Open the folder on GitHubat commit 38d4118
Compare with similar skills
Call Agent Certainty Calibrator 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Call Agent Certainty Calibrator this skillCALLE-AI/awesome-phone-call-agents | 107 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Flagsvercel/next.js | 143k | — | ~746 | Automated safety check: Pass | MIT | |
| Baoyu Youtube TranscriptJimLiu/baoyu-skills | 27k | 1 repos | ~2.4k | Automated safety check: Pass | MIT | |
| Youtube Transcriptbrowser-act/skills | 6.1k | — | ~2.1k | Automated safety check: Pass | MIT | |
| Youtube Transcriptsickn33/agentic-awesome-skills | 47k | 1 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Transcription0xsline/OpenChatCut | 2.2k | 1 repos | ~1.1k | Automated safety check: Pass | AGPL-3.0 |
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Questions about Call Agent Certainty Calibrator
What does Call Agent Certainty Calibrator do?
Offline experimental CALL-E transcript helper that flags agent statements of specific values absent from the goal facts and callee turns (over-assertion) and goal facts spoken with hedges but no…. Call Agent Certainty Calibrator is an agent skill from CALLE-AI/awesome-phone-call-agents. Offline experimental CALL-E transcript helper that flags agent statements of specific values absent from the goal facts and callee turns (over-assertion) and goal facts spoken with hedges but no source attribution (over-hedging), plus a three-tier calibrated-wording goal template.
How do I install Call Agent Certainty Calibrator in Claude Code?
Run `npx skills add CALLE-AI/awesome-phone-call-agents --skill call-agent-certainty-calibrator -a claude-code`. Or copy the skill folder (skills/call-agent-certainty-calibrator in CALLE-AI/awesome-phone-call-agents) into .claude/skills/call-agent-certainty-calibrator in your project. Claude Code loads it when a task matches its description.
How do I install Call Agent Certainty Calibrator in Codex?
Run `npx skills add CALLE-AI/awesome-phone-call-agents --skill call-agent-certainty-calibrator -a codex`. Or copy the skill folder (skills/call-agent-certainty-calibrator in CALLE-AI/awesome-phone-call-agents) into .agents/skills/call-agent-certainty-calibrator in your project. Codex loads it when a task matches its description.
Can I use Call Agent Certainty Calibrator 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-agent-certainty-calibrator -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-agent-certainty-calibrator, .gemini/skills/call-agent-certainty-calibrator, .github/skills/call-agent-certainty-calibrator and .opencode/skills/call-agent-certainty-calibrator in your project.
What does Call Agent Certainty Calibrator need to run?
Going by SKILL.md and its folder, Call Agent Certainty Calibrator needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.
Does Call Agent Certainty Calibrator 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 Call Agent Certainty Calibrator 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 Agent Certainty Calibrator use?
Call Agent Certainty Calibrator 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 Agent Certainty Calibrator use?
About 1.2k tokens (SKILL.md is roughly 4.6k 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 2.8k tokens, read only when the agent opens those files.
What are the alternatives to Call Agent Certainty Calibrator?
Skills that share tags, products or a category with Call Agent Certainty Calibrator: Flags (vercel/next.js, 143k stars), Baoyu Youtube Transcript (JimLiu/baoyu-skills, 27k stars), Youtube Transcript (browser-act/skills, 6.1k stars) and Youtube Transcript (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 Agent Certainty Calibrator?
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.