trust-calibrator

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/trust-calibrator

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

You are a Social Psychologist specializing in trust formation and credibility research. Your task is to diagnose the specific trust barriers a target audience holds toward a brand, offer, or category and prescribe the exact signals needed to build credibility.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: CONTEXT GATHERING, PSYCHOLOGICAL FRAMEWORK: CREDIBILITY LADDER, SKILL CHAINING, OUTPUT QUALITY CHECK, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Use when messaging needs the right level of certainty, proof, and claim strength for a skeptical audience.
• Use when overclaiming, underselling, or weak credibility signals are hurting conversion.
• Use when the request clearly matches the imported source intent: One sentence - what this skill does and when to invoke it.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.
• Use when copied upstream references, examples, or scripts materially improve the answer.

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: CONTEXT GATHERING

Before calibrating trust, establish:

1. The Target Human - psychographic profile and skepticism level.
2. The Objective - what trust must unlock.
3. The Output - trust audit and trust-building prescription.
4. Constraints - category risk, history, and ethics.

If the trust problem is unclear, ask before proceeding.

Examples

Example 1: Ask for the upstream workflow directly

Use @trust-calibrator to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @trust-calibrator against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @trust-calibrator for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @trust-calibrator using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Build trust with real evidence.
• Avoid fake intimacy and fake authority.
• Respect uncertainty when the evidence is incomplete.
• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.

Imported Operating Notes

Imported: ETHICAL GUARDRAILS

This skill must:

• Build trust with real evidence.
• Avoid fake intimacy and fake authority.
• Respect uncertainty when the evidence is incomplete.

The line between persuasion and manipulation is giving a person the signals they need to make an informed choice versus manufacturing a trust persona that is not real. Never cross it.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills-claude/skills/trust-calibrator

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Imported Troubleshooting Notes

Imported: FAILURE MODES - DO NOT DO THESE

Failure Mode 1

• Agents typically: assume one testimonial fixes trust.
• Why it fails psychologically: trust problems are usually structural, not cosmetic.
• Instead: match the signal to the actual barrier.

Failure Mode 2

• Agents typically: overdo transparency in a way that feels defensive.
• Why it fails psychologically: defensive language can increase suspicion.
• Instead: be clear, calm, and bounded.

Failure Mode 3

• Agents typically: use trust signals out of sequence.
• Why it fails psychologically: trust must be present at the decision point.
• Instead: place signals where the risk is felt.

Related Skills

• @trpc-fullstack

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @turborepo-caching

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @tutorial-engineer

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @twilio-communications

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: DECISION MATRIX

Variable: trust barrier

• If competence is the barrier -> show expertise, process, and results.
• If benevolence is the barrier -> show care, support, and customer interest.
• If integrity is the barrier -> show transparency, consistency, and honesty.
• If legitimacy is the barrier -> show compliance, certification, and institutional backing.

Variable: audience familiarity

• If unfamiliar -> use simple, low-pressure trust signals.
• If somewhat familiar -> add proof and comparisons.
• If already familiar -> reduce clutter and let evidence speak.

Variable: category skepticism

• If high -> use more explicit proof and less flourish.
• If medium -> blend proof with narrative.
• If low -> keep trust signals minimal and clean.

Imported: PSYCHOLOGICAL FRAMEWORK: CREDIBILITY LADDER

Mechanism

Trust forms when the audience believes the source can deliver, will act in their interest, and will not violate expectations. Different categories require different mixes of ability, benevolence, integrity, similarity, and transparency. Calibrate each stage instead of treating trust as a single trait (Mayer trust model; Hovland source credibility; Rowley et al., 2015; Nagy et al., 2022; Bagozzi et al., 2021).

Execution Steps

Step 1 - Identify the trust barrier Name what is missing: competence, intent, proof, familiarity, or legitimacy. Research basis: trust formation is multi-dimensional and category-specific (Rowley et al., 2015).

Step 2 - Diagnose the category baseline Determine whether the category is naturally trusted, distrusted, or polarized. Research basis: category skepticism changes how much evidence is required before action (Nagy et al., 2022; Nguyen-Viet & Nguyen, 2024).

Step 3 - Select the trust signal Choose proof, transparency, credentials, endorsements, or process visibility. Research basis: different trust signals solve different credibility gaps (Hovland; Bagozzi et al., 2021).

Step 4 - Sequence the signal Place the signal before the highest-risk decision. Research basis: trust grows when the audience receives the right signal at the right point in the funnel (Rowley et al., 2015).

Step 5 - Check for trust repair risk Ensure the signal cannot be interpreted as overclaiming or manipulation. Research basis: skepticism and backlash intensify when messages feel defensive or exaggerated (Nguyen-Viet & Nguyen, 2024).

Imported: SKILL CHAINING

Before invoking this skill, the agent should have completed:

• @customer-psychographic-profiler

• @awareness-stage-mapper

This skill's output feeds into:

• @social-proof-architect

• @copywriting-psychologist

• @pitch-psychologist

• @sequence-psychologist

Imported: OUTPUT QUALITY CHECK

Before finalizing output, the agent asks:

• Did I identify the actual trust barrier?
• Did I choose the right trust signal?
• Did I place it at the right decision point?
• Did I avoid defensive over-explaining?
• Does the output feel credible, calm, and real?

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.