ArthurZakirov Impact Bullets Skill
Use this when
Use this skill to create or revise:
- resume bullets
- LinkedIn Experience bullets
- project-impact bullets
- performance-review summaries
- portfolio case-study proof points
Core principle
A bullet should prove value, not merely list responsibilities.
Strong bullet formula:
Action + measurable result + context/constraint + mechanism + business/user impact
Not every bullet needs all parts, but the strongest bullets include several.
Step 1 — Extract raw material
From the user's notes, extract:
- outcome achieved
- metric or scale
- time period
- starting constraint
- technical mechanism
- tools/tech stack
- audience/users affected
- business risk or deadline
- adoption evidence
- seniority/visibility signals
Do not invent metrics or tools.
Step 2 — Choose bullet type
Use a mix of bullet types.
Outcome bullet
Focuses on business result.
• Helped secure [business-critical outcome] by [action] in [timeframe], after [constraint].
Leverage bullet
Shows before/after prioritization or efficiency.
• Shifted [roadmap/process] from [before metric] to [after metric] — [ratio] more leverage — by [mechanism].
Production system bullet
Shows real deployment and stack.
• Built and deployed [system] with [tech stack], automating [workflow steps].
Adoption bullet
Shows the work landed with users.
• Equipped [number/type of users] with [capability] through [workshops/onboarding/docs/tools].
Scope-expansion bullet
Shows responsibility beyond role.
• Became a technical contact for [stakeholders] on [topics], supporting [decisions/outcomes].
Ramp-up bullet
Shows learning speed.
• Exceeded [expected level/timeframe] in [domain] despite [starting constraint], contributing to [critical effort].
Step 3 — Add tech stack without bloating
Tech stack should support credibility, not drown the bullet.
Good:
• Built and deployed [system] with LangChain, Pydantic, Databricks, Delta Lake, and MLflow, automating [workflow].
Too much:
• Built with Tool A, Tool B, Tool C, Tool D, Tool E, Tool F, Tool G, Tool H, Tool I, Tool J...
For long stacks, add a final line:
Tech stack: Python, Java, Databricks, Delta Lake, MLflow, AWS, Docker, Jira API, Slack API.
Step 4 — Avoid internal jargon
Translate internal names into external language.
Examples:
- internal request channel → operational request channel
- team-specific acronym → manual operational workflow
- service name → legacy Java service
- internal ticket code → automation roadmap item
Keep private/company-specific details out unless the user explicitly wants them included.
Step 5 — Tune for platform
Resume
More formal, achievement-oriented, concise.
LinkedIn Experience
Can be slightly more narrative and include adoption/visibility signals.
Performance review
Can include more context, constraints, and leadership appreciation.
Character-limit compression order
When over limit, cut in this order:
- adjectives and filler
- repeated tech stack mentions
- excessive internal context
- secondary examples
- redundant bullets
- less measurable claims
Protect:
- strongest metrics
- business-critical outcomes
- deployed/adopted evidence
- senior stakeholder signals
- unique mechanisms
Strong verbs
Use:
- built
- deployed
- shifted
- secured
- automated
- equipped
- reduced
- maintained
- enabled
- analyzed
- visualized
- onboarded
- contributed
- converted
- hardened
- scaled
Avoid:
- helped with
- worked on
- involved in
- participated in
- responsible for
Use “helped” only when the user wants careful attribution or did not own the whole outcome.
Output format
Return:
## Full version
[bullets]
## LinkedIn version under [limit]
[bullets]
## Resume version
[bullets]
## Notes / assumptions
- [items that need user verification]
Quality bar
Every bullet should answer at least two of:
- What changed?
- How much?
- How fast?
- For whom?
- Under what constraint?
- Using what mechanism?
- Why did it matter?