Opportunity Scan

Opportunity Scan — find what to change, from what actually happened

Reads your agent's own capabilities plus one target you choose, and recommends which primitive each finding should become. Agent-agnostic (Claude Code, Codex, PI, …). It maps what it finds to the full primitive palette (rules · skill · hook · subagent · MCP · automation/workflow), and it works for any agent because it learns that agent's capabilities first.

Two targets, one skill — this is the whole design:

• A run → the REACTIVE loop. Something just went sideways in a loop you ran. Point the scan at that run's

artifacts and ask "what in the AI layer would have prevented this?" You fix the system, not the code.

• A window of logs → the PROACTIVE scan. Nothing is broken. Point it at weeks of sessions and ask *"what do I

keep doing by hand that should be encoded?"*

Same skill, same output shape — you're just changing what it reads.

This is a discovery tool — what to change — NOT a quality eval (whether a built thing is good). Keep the two separate.

Inputs — required first, then optional

Read $ARGUMENTS as prose, not as positional slots. Only input 1 is required. Input 2 is free-form and will contain spaces, so never split arguments on whitespace and never bind them by position — a steer typed without quotes is still one steer. If something is missing, ask for it once, in a single message, not one question at a time.

1. What to scan (required) — exactly one of:

• A RUN (reactive) — the artifacts one loop left behind: the plan, the implementation report, an RCA, the PR

body, the review output, the commits/diff. Add that run's session log too if you can point at it. These are already scoped to the run, so there's no session-hunting to do.

• A WINDOW OF LOGS (proactive) — where your agent keeps session logs, plus how far back. Examples: Claude

Code → ~/.claude/projects/ + ~/.claude/history.jsonl; Codex → ~/.codex/sessions/; PI → your extension's log dir. Default window: the last 2 weeks.

This choice is what makes the scan reactive or proactive. Nothing else changes.

1. Your steer (optional — ask once, accept "nothing specific") — one input, whose meaning follows the

target:

• Scanning a RUN: the symptom you noticed, in your words — what the agent got wrong, what you had to

correct, what annoyed you. You were there; don't make the scan re-derive from the artifacts what you can just say. (This is the "you just did X" of the one-sentence outer loop — the tooled version accepts the same X.) Keep it to the observation; working out why it happened is the scan's job, not yours.

• Scanning LOGS: the theme you care about — the kind of work you want to stop doing by hand, a

quality bar you keep enforcing, a part of the loop that keeps costing you. Without it the scan just ranks by frequency, which is not the same as ranking by what matters to you.

Either way: you supply the steer, the target supplies the evidence.

1. The agent's own capability docs (optional — normally resolve this yourself) — so the scan knows what your

agent can become (its real extension points). Work it out; do not ask first. Identify which agent you are running as and find its own extensibility docs (for Claude Code that is code.claude.com/docs). Only ask if you genuinely cannot determine it, or if the user wants you pointed somewhere specific. Never assume a fixed set of extension points: read them from whatever docs you actually find.

Steps — keep them literal; this is the fragile part (meta-prompting)

1. Learn your own capabilities. Determine which agent you are running as and read its capability docs (input

3, which you normally resolve yourself). Write a short internal list of this agent's extension points (rules, skill, hook, subagent, MCP/tool, automation/workflow, whatever the docs describe). Use what the docs say — do not assume a fixed set.

1. Read the target — branch on what input 1 was:

• A RUN: read the artifacts in full — they're small, and the detail is the point. If input 2 named a

symptom, start there: find it in the artifacts and verify it against what actually happened, rather than re-deriving from scratch what the user already told you. Then reconstruct the rest of the run: what was asked, what the agent did, where it went wrong, where it had to be corrected, what it assumed, what it skipped — the named symptom is the entry point, not a blinder. Read the diff last, as evidence rather than as the subject.

• A WINDOW OF LOGS: pull out what you actually did — recurring commands, repeated multi-step sequences,

repeated instructions/corrections, tools reached for, friction/retries. Aggregate, don't ingest: logs can be huge — prefer the prompt/command-history file over raw transcripts, and reduce with shell tools (jq/grep/sort | uniq -c) so only frequencies and representative samples enter your context, never whole log files.

Either way: if you can't locate or parse the target, ask the user rather than invent.

1. Find what to change — the question differs by target:

• RUN (reactive): for each thing that went wrong or needed correcting, ask **"what in the AI layer would

have prevented this?" Name the smallest durable change that would have caught it — a line in a rule, a step added to a skill, a hook, a tighter tool scope — specific enough to apply today. Fix the system, not the code: do not propose the code fix, propose the thing that would have made the code fix unnecessary or automatic. ⚠️ Not every failure is a system gap** — if something went wrong that no durable change would have prevented, say so plainly instead of inventing a rule for a one-off. One honest "nothing to change here" is worth more than five speculative rules.

• LOGS (proactive): for each recurring pattern, ask "what should this become?" Rank by roughly (how

often it occurs × how much encoding it would save).

Both targets: pick the best-fit primitive (from step 1's list) and say why. If input 2 was given, weight it — on a run, the named symptom's prevention leads the report (and if the evidence says the symptom was actually something else, say so plainly); on logs, surface what the user said they care about even when it isn't the most frequent pattern, and say plainly when a high-frequency pattern is not worth encoding. And propose each change in the house style of the artifacts that already exist: skim a couple of the project's current rules/skills/agents first and shape the recommendation to look like them, so what it suggests is something the user would actually build. Examples of the mapping:

• a rule you keep restating → rules (CLAUDE.md / AGENTS.md)
• a repeated multi-step workflow → a skill
• a must-never / must-always you keep enforcing by hand → a hook
• a specialized recurring delegation → a subagent
• a clean end-to-end hand-off you do often → an automation (later: an Archon workflow)

1. Write the report as a single self-contained HTML file (see the contract below), then tell the user where it

is.

Report contract — prescribe HOW to render, never WHAT to include

Let the analysis drive the report. Which sections exist, what goes in them, how deep each goes, how many opportunities, which quotes or numbers are worth pulling out — all of that comes from what you actually found, NOT from this skill. Do not box the report into a fixed set of sections, do not seed findings, do not tell it what to conclude. If the data is rich, the report is rich; if a single finding deserves its own deep section, give it one; if something surprising turns up, surface it.

Only the rendering is prescribed:

• One self-contained .html file (inline CSS), opens in any browser.
• Visually clean, scannable, and generous — let the findings breathe; use whatever layout, sections, real

quotes, stats, or visuals best fit what was actually found.

The rule: this skill governs how to put it on the page, never what goes on the page.

Keep it light

Do exactly these four steps, clearly. Don't add scoring frameworks, config, or extra passes it doesn't need. If a step can't complete (docs or the target not found), stop and ask — never fabricate the analysis. Scan one target per run. If someone wants both the reactive and the proactive view, that's two runs and two reports — mixing "what broke in this loop" with "what I keep repeating across a month" produces a report that answers neither question well.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: coleam00
• Source: coleam00/skills
• License: MIT

Install and usage instructions live in the source repository linked above.