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 agentmods add commands/bitflight-devops/skilllint/map-codebasegit clone --depth 1 https://github.com/bitflight-devops/skilllintWhat 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 | $0.00022 | $0.00625 |
| Opus 5 | $0.00011 | $0.00313 |
| Sonnet 5 | $0.00004 | $0.00125 |
| Haiku 4.5 | $0.00002 | $0.00063 |
Grade A, and why
gsd:map-codebase 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 yesterday.
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.
This is a copy
100% identical to gsd:map-codebase — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Each mapper agent explores a focus area and writes documents directly to .planning/codebase/. The orchestrator only receives confirmations, keeping context usage minimal.
Output: .planning/codebase/ folder with 7 structured documents about the codebase state.
<execution_context> @./.claude/get-shit-done/workflows/map-codebase.md </execution_context>
Load project state if exists: Check for .planning/STATE.md - loads context if project already initialized
This command can run:
- Before /gsd:new-project (brownfield codebases) - creates codebase map first
- After /gsd:new-project (greenfield codebases) - updates codebase map as code evolves
- Anytime to refresh codebase understanding
<when_to_use> Use map-codebase for:
- Brownfield projects before initialization (understand existing code first)
- Refreshing codebase map after significant changes
- Onboarding to an unfamiliar codebase
- Before major refactoring (understand current state)
- When STATE.md references outdated codebase info
Skip map-codebase for:
- Greenfield projects with no code yet (nothing to map)
- Trivial codebases (<5 files) </when_to_use>
<success_criteria>
- .planning/codebase/ directory created
- All 7 codebase documents written by mapper agents
- Documents follow template structure
- Parallel agents completed without errors
- User knows next steps </success_criteria>
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.
- yesterday First seen · 72 lines · 22 tokens per session scan A 7cb8cc0ce5c6
gsd:map-codebase is a command published in the GitHub repository bitflight-devops/skilllint (6 stars, last pushed yesterday), licensed MIT. It adds 22 tokens to every session and 625 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to gsd:map-codebase, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.