This skill turns implicit intent into an explicit, trackable artifact at the
Works with
moment execution starts. It creates a lightweight intent contract, watches for
scope drift while work is in progress, and closes each intent with a short
resolution record.
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionintent-framed-agentExecute the skills CLI command in your project's root directory to begin installation:
Fetches intent-framed-agent from pskoett/pskoett-ai-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate intent-framed-agent. Access via /intent-framed-agent in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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npx skills add pskoett/pskoett-ai-skills
npx skills add pskoett/pskoett-ai-skills/skills/intent-framed-agent
This skill turns implicit intent into an explicit, trackable artifact at the moment execution starts. It creates a lightweight intent contract, watches for scope drift while work is in progress, and closes each intent with a short resolution record.
Use this skill for coding tasks only. It is designed for implementation work that changes executable code.
Do not use it for general-agent activities such as:
For trivial edits (for example, simple renames or typo fixes), skip the full intent frame.
Activate at the planning-to-execution transition for non-trivial coding work.
Common cues:
At execution start, emit:
## Intent Frame #N
**Outcome:** [One sentence. What does done look like?]
**Approach:** [How we will implement it. Key decisions.]
**Constraints:** [Out-of-scope boundaries.]
**Success criteria:** [How we verify completion.]
**Estimated complexity:** [Small / Medium / Large]
Rules:
Does this capture what we are doing? Anything to adjust before I start?During execution, monitor for drift at natural boundaries:
Drift examples:
When detected, emit:
## Intent Check #N
This looks like it may be moving outside the stated intent.
**Stated outcome:** [From active frame]
**Current action:** [What is happening]
**Question:** Is this a deliberate pivot or accidental scope creep?
If pivot is intentional, update the active intent frame and continue. If not, return to the original scope.
When work under the active intent ends, emit:
## Intent Resolution #N
**Outcome:** [Fulfilled / Partially fulfilled / Pivoted / Abandoned]
**What was delivered:** [Brief actual output]
**Pivots:** [Any acknowledged changes, or None]
**Open items:** [Remaining in-scope items, or None]
Resolution is preferred but optional if the session ends abruptly.
One session can contain multiple intent frames.
Rules:
Abandoned or Pivoted, then open a new frame.#1, #2, ...).Entire CLI: https://github.com/entireio/cli
When tool access is available, detect Entire at activation:
entire status 2>/dev/null
Copilot/chat fallback:
Use this skill as the front-door alignment layer for non-trivial coding work:
plan-interview (optional, for requirement shaping)intent-framed-agent (execution contract + scope drift monitoring)context-surfing (context quality monitoring — runs concurrently with intent-framed-agent during execution)simplify-and-harden (post-completion quality/security pass)self-improvement (capture recurring patterns and promote durable rules)Both skills are live during execution. They monitor different failure modes:
They are complementary, not redundant. An agent can be perfectly on-scope while its context quality degrades. Conversely, scope drift can happen with perfect context quality. Intent Checks continue firing alongside context-surfing's wave monitoring.
Precedence rule: If both skills fire simultaneously (an Intent Check and a context-surfing drift exit at the same time), the drift exit takes precedence. Degraded context makes scope checks unreliable — resolve the context issue first, then resume scope monitoring in the next session.
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
intent-framed-agent is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in intent-framed-agent — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
intent-framed-agent fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: intent-framed-agent is focused, and the summary matches what you get after install.
We added intent-framed-agent from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added intent-framed-agent from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: intent-framed-agent is the kind of skill you can hand to a new teammate without a long onboarding doc.
intent-framed-agent fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for intent-framed-agent matched our evaluation — installs cleanly and behaves as described in the markdown.
intent-framed-agent is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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