Turn ordinary self-talk into identity votes, pattern reads, and one high-ROI next action.
SaferSkills independently audited identity-votes (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Use this skill for an explicit identity review or a multi-day trajectory analysis.
Core principle: behavior disappears, identity accumulates. Every repeated action is a vote for a kind of person.
This is inspired by James Clear's identity-based habits framework from Atomic Habits. Do not summarize the book unless asked; use the framework as practical grounding for the skill.
The agent can turn several behaviors into an evidence ledger, compare competing identity trajectories, and identify the next action that would change the pattern.
High ROI: replace vague personality language with a behavior-grounded trajectory and one next action.
Use this for people who want behavior-level self-awareness without vague personality analysis. Especially useful for users with ADHD or scattered attention: they can talk naturally, and the agent extracts the identity signal.
The skill should be usable by any person. Do not assume Michelle's private ontology unless someone provides it.
Also use this skill when the user asks for analysis of:
Do not invoke this full review for every casual behavior description. Use identity-vote-translator for a lightweight end-of-conversation read.
Accept any of:
Do not require rigid formatting. If duration is available, use it. If duration is missing, weigh by intentionality, effort, emotional salience, and repetition.
Treat one day or one week as limited evidence. Use conditional trajectory language and never present an identity inference as a fixed trait.
During conversation, maintain a temporary ledger:
Behavior observed:
Identity vote:
Direction: reinforces | weakens | redirects
Strength: low | medium | high
Evidence:
Next vote candidate:Only present the ledger if useful. Usually summarize the top 2-3 decision-relevant votes instead of showing everything.
Prefer grounded identity types over generic traits. Examples:
Avoid praise. Avoid productivity scores. Avoid inventing a flattering identity when the evidence is mixed.
Name danger identities when evidence supports them, but do not shame the user.
These are not labels for the person. They are trajectories being reinforced by repeated behaviors.
Prefer avoidance trajectory or confrontation trajectory in the output. Use the capitalized Avoider or Confronter label only when the user already uses that ontology or repeated evidence across several contexts makes the shorthand useful.
When a danger identity appears, give one concrete "next vote" that redirects it. Keep the tone direct: identify the pattern, name the cost, and prescribe the smallest corrective action.
For each strong pattern:
If the user has named desired or danger identities, use those terms first. If not, infer identities from evidence.
Return:
Read: one sentence naming the strongest identity signal.
High-ROI next vote: the one action most likely to shift the trajectory.
Identity votes
- Identity: someone who ...
Evidence strength: LOW | MEDIUM | HIGH
Evidence: concrete behaviors from the input
Trajectory: where this converges if repeated
Alignment: aligned | mixed | misaligned
Next vote: one practical behavior to reinforce or redirect
Main tension: the strongest conflict between identities
Risk to watch: Avoider | Confronter | other, if presentIf the input covers more than one week, include trajectory movement: rising, falling, or stable.
Never assign decimal weights unless the user already has a defined scoring model that gives those numbers meaning.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.