Claude Code Thinking Skills is a catalogue of 28 portable skills that give coding agents structured procedures for reasoning about decisions, diagnosis, risk, strategy, and related problems. It is intended for Claude Code, GitHub Copilot, Codex, Cursor, and other tools that support Agent Skills.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add tjboudreaux/cc-thinking-skills --skill thinking-map-territorygit clone --depth 1 https://github.com/tjboudreaux/cc-thinking-skillsWrote 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.
[](https://agentmods.dev/skills/tjboudreaux/cc-thinking-skills/thinking-map-territory)<a href="https://agentmods.dev/skills/tjboudreaux/cc-thinking-skills/thinking-map-territory"><img src="https://agentmods.dev/badge/skills/tjboudreaux/cc-thinking-skills/thinking-map-territory/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.
<a href="https://agentmods.dev/skills/tjboudreaux/cc-thinking-skills/thinking-map-territory"><img src="https://agentmods.dev/badge/skills/tjboudreaux/cc-thinking-skills/thinking-map-territory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 51 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00042 | $0.00715 |
| Opus 5 | $0.00021 | $0.00358 |
| Sonnet 5 | $0.00008 | $0.00143 |
| Haiku 4.5 | $0.00004 | $0.00072 |
Grade A, and why
thinking-map-territory 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Map Territory
Docs, tests, diagrams, metrics, comments, and mental models are maps. Running code and actual data are the territory. When they disagree, verify territory and update the model — never force reality to match the description.
When to Use
- Observed behavior contradicts a doc, test expectation, diagram, comment, dashboard, or prior assumption.
- A claim about the system rests on a secondary source rather than current code or data.
- Tests pass but production or manual behavior is wrong.
- You are about to theorize why something happens before inspecting what happens.
- A decision depends on whether a model, schema, or metric is still current.
When NOT to Use
- The map is the artifact you are asked to edit (doc, diagram, spec) — that artifact is the task territory.
- Same path already verified this session — reuse that observation.
- Map is authoritative and generative (codegen types, derived schema) with no claimed drift.
- The mismatch cannot change the decision — note and move on.
- Competing causal hypotheses after territory is confirmed — switch to scientific-method.
- Security exploit construction — use red-team; this skill only settles model-versus-observation.
Procedure
- Name the map. State the exact representation trusted: which doc, test, metric, diagram, comment, or assumption. Quote the claim, not a paraphrase.
- Name the territory check. Specify the observation that would prove or disprove the claim: code path, runtime value, query, reproduction, recent change, or live metric.
- Verify source and freshness. Confirm origin of the map (author, generator, last update) and whether it could be stale relative to deploy, config, or data change. Prefer primary current sources over summaries.
- Observe the territory. Read the real code path, run or instrument it, query real data, or reproduce the behavior. Do not predict from signatures or names alone.
- Record the delta and update. If territory contradicts the map, territory wins for shipped behavior. Document the gap, revise the model, then decide the fix (code, map, or both). Note unmapped paths — likely next failure sites.
- Stop when settled. Once the claim is confirmed or overturned with a concrete observation, stop re-checking the same path.
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.
- 12d ago First seen · 54 lines · 42 tokens per session scan A 49692f7757be
thinking-map-territory is a skill published in the GitHub repository tjboudreaux/cc-thinking-skills (1,300 stars, last pushed 1mo ago), licensed MIT. It adds 42 tokens to every session and 715 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
swiftui-debugging
Diagnose SwiftUI performance issues including unnecessary re-renders, view identity problems, and slow body evaluations. Use when SwiftUI views are slow, janky, or re-rendering too often.
performance-profiling
Guide performance profiling with Instruments, diagnose hangs, memory issues, slow launches, and energy drain. Use when reviewing app performance or investigating specific bottlenecks.
debug-menu
Generates a developer debug menu with feature flag toggles, environment switching, network log viewer, cache clearing, crash trigger, and diagnostic info export. Only included in DEBUG builds. Use when user wants a debug panel, dev tools menu, or shake-to-debug functionality.
error-monitoring
Generates protocol-based error/crash monitoring with swappable providers (Sentry, Crashlytics). Use when user wants to add crash reporting, error tracking, or production monitoring.
logging-setup
Generates structured logging infrastructure using os.log/Logger to replace print() statements. Use when user wants to add proper logging, replace print statements, or set up app logging.
dead-code-detector
Detect unused/unreachable code in polyglot codebases (Python, TypeScript, Rust). TRIGGERS - dead code, unused functions, unused imports.