ie-from-pr-learnings

ie-from-pr-learnings is a skill for Claude Code from davidteren/intent-engineering. It costs 74 tokens per session (1,476 once invoked), scanned A, original, MIT.

A tool that turns pull-request review comments, triage documents, or GitHub pull-request links into Intent Engineering configuration improvements. Pull requests are proposed code changes reviewed before they are merged.

In plain words
What is it for?
Extracting guardrails from review feedback, updating .intense/ conventions and severity settings, and optionally defining design-pattern policies based on past review problems.
Why use it?
It preserves lessons from code review as project rules, so future reviews and audits can check them automatically. It also produces a short summary of the findings.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: mentions CLAUDE.md; mentions AGENTS.md.

Runs only inside its plugin — its command needs a path that Claude Code sets for a plugin’s own hooks and for nothing else. Install the plugin, not this.

Part of the intent-engineering plugin — 6 skills, 5 agents shipped together

Good fit Extracting guardrails from review feedback, updating .intense/ conventions and severity settings, and optionally defining design-pattern policies based on past review problems.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.

Claude Code
/plugin marketplace add davidteren/intent-engineering
Claude Code
/plugin install intent-engineering

Made for: Claude Code.

Or install intent-engineering, the plugin that ships this one along with the rest of its 6 skills, 5 agents.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for ie-from-pr-learnings

README.md
[![agentmods](https://agentmods.dev/badge/skills/davidteren/intent-engineering/ie-from-pr-learnings/github.svg)](https://agentmods.dev/skills/davidteren/intent-engineering/ie-from-pr-learnings)
Your own site
<a href="https://agentmods.dev/skills/davidteren/intent-engineering/ie-from-pr-learnings"><img src="https://agentmods.dev/badge/skills/davidteren/intent-engineering/ie-from-pr-learnings/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for ie-from-pr-learnings

Your own site · 80×15
<a href="https://agentmods.dev/skills/davidteren/intent-engineering/ie-from-pr-learnings"><img src="https://agentmods.dev/badge/skills/davidteren/intent-engineering/ie-from-pr-learnings.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 74 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,476 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00074 $0.01476
Opus 5 $0.00037 $0.00738
Sonnet 5 $0.00015 $0.00295
Haiku 4.5 $0.00007 $0.00148

Measured 9d ago against content hash 6f64b23c99b1, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

ie-from-pr-learnings scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/intent-engineering/skills/ie-from-pr-learnings/SKILL.md · 133 lines

How it starts

The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Intent Engineering — From PR learnings

Mines review learnings (PR threads, triage tables, or human-written guardrail lists) into project .intense/ config so later /ie-review and /ie-audit runs enforce what humans already fought for in review.

This skill writes only under .intense/ (and optional report under the resolved artifacts.report_dir). It never pushes, never opens PRs, and never clobbers existing YAML without confirmation.

When to use

  • User has a triage doc (e.g. PR-COMMENTS-TRIAGE-*.md with G1…Gn guardrails).
  • User points at one or more GitHub PRs after review.
  • User wants workspace-level config for a multi-repo stack (BE + FE) via walk-up.

Argument parsing

Token Effect
path ending in .md Treat as triage / learnings document (primary).
pr:<url|number> Fetch review threads with gh (read-only). Repeatable.
stack / blank with multi-repo cwd Prefer nearest .intense/ walk-up; detect BE/FE siblings.
config:<path> Override project config dir (same rules as config-resolution).
out:<dir> Write the summary report under this dir (default: resolved artifacts.report_dir).

Procedure

1. Resolve project base + existing config

Per ${CLAUDE_PLUGIN_ROOT}/references/config-resolution.md:

  1. Discover PROJECT_INTENSE (walk-up / config: / INTENSE_CONFIG_DIR).
  2. If none, create .intense/ at the workspace root (for multi-repo stacks, the parent that contains backend/frontend-style siblings, not deep inside one app) after confirming with the user.
  3. Load existing ways-of-working.yaml / patterns.yaml / thresholds.yaml if present.
  4. Always record Config source for the report.

2. Collect learnings

From a triage markdown (preferred when available):

  • Extract tables of guardrails (IDs like G1…, priority P0–P3, state).
  • Prefer rows marked P0/P1 and cross-stack guardrails.
  • Capture “Wontfix + rationale” as advisory notes (do not invent severity P0).

Read the full file on GitHub · 133 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 133 lines · 74 tokens per session scan A 6f64b23c99b1

Subscribe to this mod's changes

ie-from-pr-learnings is a skill published in the GitHub repository davidteren/intent-engineering (3 stars, last pushed yesterday), licensed MIT. It adds 74 tokens to every session and 1,476 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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