intent-framed-agent
Frames coding-agent work sessions with explicit intent capture and drift monitoring. Use when a session transitions from planning/Q&A to implementation for coding tasks, refactors, feature builds, bug fixes, or other multi-step execution where scope drift is a risk.
Best use case
intent-framed-agent is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Frames coding-agent work sessions with explicit intent capture and drift monitoring. Use when a session transitions from planning/Q&A to implementation for coding tasks, refactors, feature builds, bug fixes, or other multi-step execution where scope drift is a risk.
Teams using intent-framed-agent should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/intent-framed-agent/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How intent-framed-agent Compares
| Feature / Agent | intent-framed-agent | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Frames coding-agent work sessions with explicit intent capture and drift monitoring. Use when a session transitions from planning/Q&A to implementation for coding tasks, refactors, feature builds, bug fixes, or other multi-step execution where scope drift is a risk.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Intent Framed Agent ## Install ```bash npx skills add pskoett/pskoett-ai-skills ``` ```bash npx skills add pskoett/pskoett-ai-skills/skills/intent-framed-agent ``` ## Purpose 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. ## Scope (Important) 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: - broad research - planning-only conversations - documentation-only work - operational/admin tasks with no coding implementation For trivial edits (for example, simple renames or typo fixes), skip the full intent frame. ## Trigger Activate at the planning-to-execution transition for non-trivial coding work. Common cues: - User says: "go ahead", "implement this", "let's start building" - Agent is about to move from discussion into code changes ## Workflow ### Phase 1: Intent Capture At execution start, emit: ```markdown ## 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: - Keep each field to 1-2 sentences. - Ask for confirmation before coding: - `Does this capture what we are doing? Anything to adjust before I start?` - Do not proceed until the user confirms or adjusts. ### Phase 2: Intent Monitor During execution, monitor for drift at natural boundaries: - before touching a new area/file - before starting a new logical work unit - when current action feels tangential Drift examples: - work outside stated scope - approach changes with no explicit pivot - new features/refactors outside constraints - solving a different problem than the stated outcome When detected, emit: ```markdown ## 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. ### Phase 3: Intent Resolution When work under the active intent ends, emit: ```markdown ## 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. ## Multi-Intent Sessions One session can contain multiple intent frames. Rules: 1. Resolve current intent before opening the next. 2. If user changes direction mid-task, resolve current intent as `Abandoned` or `Pivoted`, then open a new frame. 3. Drift checks always target the currently active frame. 4. Number frames sequentially within the session (`#1`, `#2`, ...). 5. Constraints do not carry forward unless explicitly restated. ## Entire CLI Integration Entire CLI: https://github.com/entireio/cli When tool access is available, detect Entire at activation: ```bash entire status 2>/dev/null ``` - If it succeeds, mention that intent records will be captured in the session transcript on the checkpoint branch. This enables `learning-aggregator --deep` to later mine intent frames and drift events for cross-session scope-drift patterns. - If unavailable/failing, continue silently. Do not block execution and do not nag about installation. Copilot/chat fallback: - If command execution is unavailable, skip detection and continue with the same intent workflow in chat output. ### How intent frames become learning signals Each Intent Frame and Intent Check you emit is captured verbatim in Entire's session transcript. At cadence, `learning-aggregator --deep` reads those transcripts and extracts: - Frames that were resolved as `Abandoned` or `Pivoted` → potential planning gaps - Drift signals that repeatedly fire in similar contexts → potential scope definition issues - Constraint violations detected by drift checks → patterns for promotion to project instruction files You do not need to do anything special for this — the intent blocks are structured (`## Intent Frame #N`, `## Intent Check`, `## Intent Resolution`), which makes them parseable from the transcript. ## Guardrails - Keep it lightweight; avoid long prose. - Do not over-trigger on trivial tasks. - Do not interrupt on every small step. - Treat acknowledged pivots as valid. - Preserve exact structured block headers/fields for parseability. ## Interoperability with Other Skills Use this skill as the front-door alignment layer for non-trivial coding work: 1. `plan-interview` (optional, for requirement shaping) 2. `intent-framed-agent` (execution contract + scope drift monitoring) 3. `context-surfing` (context quality monitoring — runs concurrently with intent-framed-agent during execution) 4. `simplify-and-harden` (post-completion quality/security pass) 5. `self-improvement` (capture recurring patterns and promote durable rules) ### Relationship with context-surfing Both skills are live during execution. They monitor different failure modes: - **intent-framed-agent** monitors *scope* drift — is the agent doing the right thing? It fires structured Intent Checks when work moves outside the stated outcome. - **context-surfing** monitors *context quality* drift — is the agent still capable of doing it well? It fires when the agent's own coherence degrades (hallucination, contradiction, hedging). 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. ### What this skill produces - **Intent frame artifact** — consumed by context-surfing as part of the wave anchor and copied verbatim into handoff files on drift exit. - **Intent resolution** — signals task completion, which triggers simplify-and-harden. - **Drift observations** — scope drift patterns can be logged to self-improvement as learnings if they recur.
Related Skills
verify-gate
Runs project compile, test, and lint commands between implementation and quality review. Gates simplify-and-harden behind machine verification. If checks fail, routes back to implementation with diagnostics for a fix loop. If checks pass, signals ready for the quality pass. Use after any implementation work completes and before simplify-and-harden. Essential for the inner loop's verify step.
use-agent-factory
How to drive the 14-workflow agent factory in this repo from a Claude session. Covers: when to use the factory vs. direct edits, how to start the chain, where the human gates are, how to pick an implementer, how to recover from stuck PRs, and all the failure modes learned to date. Use this skill when the user asks you to ship a feature, fix, or refactor through the factory; when they reference an existing issue or PR in the factory chain; when a workflow is stuck or misbehaving; or when you need to file issues or plan files that the factory will pick up. Do NOT use this skill for: single-file scratch edits on an untracked branch, research questions, one-shot script runs, or any work that does not produce a PR to main.
simplify-and-harden
Post-completion self-review for coding agents that runs simplify, harden, and micro-documentation passes on non-trivial code changes. Use when: a coding task is complete in a general agent session and you want a bounded quality and security sweep before signaling done. For CI pipeline execution, use simplify-and-harden-ci.
pre-flight-check
[Beta] Session-start scan that surfaces relevant learnings, recent errors, and eval status before work begins. Bridges the outer loop back into the inner loop by making accumulated knowledge visible at task start. Activated via SessionStart hook or manually before major tasks.
plan-interview
Ensures alignment between user and Claude during feature/spec planning through a structured interview process. Use this skill when the user invokes /plan-interview before implementing a new feature, refactoring, or any non-trivial implementation task. The skill runs an upfront interview to gather requirements across technical constraints, scope boundaries, risk tolerance, and success criteria before any codebase exploration. Do NOT use this skill for: pure research/exploration tasks, simple bug fixes, or when the user just wants standard planning without the interview process.
measure-ai-proficiency
Assess and improve repository AI coding proficiency and context engineering maturity. Use when users ask about: (1) AI readiness or AI maturity assessment, (2) context engineering quality or improvement, (3) CLAUDE.md, .cursorrules, or copilot-instructions files, (4) measuring how well a repo is prepared for AI coding assistants, (5) recommendations for improving AI collaboration, (6) what context files to add, or (7) comparing their repo to AI proficiency best practices.
learning-aggregator
[Beta] Cross-session analysis of accumulated .learnings/ files. Reads all entries, groups by pattern_key, computes recurrence across sessions, and outputs ranked promotion candidates. This is the outer loop's inspect step — it turns raw learning data into actionable gap reports. Use on a regular cadence (weekly, before major tasks, or at session start for critical projects). Can be invoked manually or scheduled.
eval-creator
[Beta] Creates permanent eval cases from promoted learnings and runs regression checks against them. Turns failures into test cases that prevent silent regression. This is the outer loop's regress-test step. Use when a learning is promoted and has a clear pass/fail condition, or on cadence to verify promoted rules still hold.
customize-measurement
Customize AI proficiency measurement for your specific repository through a guided interview. Use when: setting up measure-ai-proficiency for a new repo, adjusting thresholds for your team's size, hiding irrelevant recommendations, or mapping custom file names to standard patterns.
context-surfing
Monitors context window health throughout a session and rides peak context quality for maximum output fidelity. Activates automatically after plan-interview and intent-framed-agent. Stays active through execution and hands off cleanly to simplify-and-harden and self-improvement when the wave completes naturally or exits via handoff. Use this skill whenever a multi-step agent task is underway and session continuity or context drift is a concern. Especially important for long-running tasks, complex refactors, or any work where degraded context would silently corrupt the output. Trigger even if the user doesn't say "context surfing" — if an agent task is running across multiple steps with intent and a plan already established, this skill is live.
Agentic Workflow Creator
Create natural language GitHub Actions workflows using the agentic workflows pattern from GitHub Next.
testing-android-intents-for-vulnerabilities
Tests Android inter-process communication (IPC) through intents for vulnerabilities including intent injection, unauthorized component access, broadcast sniffing, pending intent hijacking, and content provider data leakage. Use when assessing Android app attack surface through exported components, testing intent-based data flows, or evaluating IPC security. Activates for requests involving Android intent security, IPC testing, exported component analysis, or Drozer assessment.