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 skills/linkpranay-ai/context-engineering-protocol/ult-codegraphnpx skills add linkpranay-ai/context-engineering-protocol --skill ult-codegraphgit clone --depth 1 https://github.com/linkpranay-ai/context-engineering-protocolWhat 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 | $0.00040 | $0.04020 |
| Opus 5 | $0.00020 | $0.02010 |
| Sonnet 5 | $0.00008 | $0.00804 |
| Haiku 4.5 | $0.00004 | $0.00402 |
Grade A, and why
codegraph 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 2d 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 — 315 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codebase Graph (graphify wrapper)
Wraps the external graphify CLI (https://github.com/safishamsi/graphify, MIT
license, distributed as the PyPI package graphifyy) to build a knowledge
graph of a codebase — files, functions, classes, and their
calls/imports/uses/inherits relationships — at graphify's own fixed
output location, graphify-out/, where other skills query it directly.
Status: piloting. Validated on a real ~40 KSLOC FastAPI codebase (a structural lead —
webhook_dispatcher.py → RetryPolicy, the bridge between two otherwise-separate subsystems — that a textualgrepcould not find, since the bridging file never mentions the target term) before this migration. Now rolling out to a small set of engineering volunteers piloting it on substantially larger codebases (500 KSLOC+), wheregraph.jsonitself can run to tens of MB and the scoped-query pattern below stops being optional and starts being the only thing that scales. Report findings (works well / doesn't / surprises) as an issue in this repo so this can graduate out of pilot status or be reworked.
How to generate / refresh the graph
Step 0 — scope the index (first run only)
Before running graphify update on the whole repo, take a quick look at
what's actually there:
# Top-level dirs by file count — surfaces generated/vendored/other-language subtrees
for d in */; do n=$(find "$d" -type f | wc -l); echo "$n $d"; done | sort -rn | head -10
Then ask: is there one directory that holds essentially all the source you care about, separate from generated code, vendored third-party libraries, or bindings for other languages?
- Yes — point
graphify updateat that directory instead of.(e.g.graphify update src/ --no-cluster). Smaller, cleaner graph; less cross-language noise inaffected/explainresults. - No (everything's genuinely mixed) — run
graphify update . --no-clusteron the repo root. Validated on a real ~300 KSLOC, 8-language, 2425-file monorepo (google/protobuf): completed in 5m28s, 73K nodes / 254K edges, withaffected/explainqueries returning in ~3s — an unscoped run on a messy repo is workable, just expect occasional cross-language results. - No, but multiple genuinely independent roots (e.g. sibling repos or workspace directories with no shared parent worth scoping to) — index each root separately, then combine:
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.
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.
- 2d ago First seen · 315 lines · 40 tokens per session scan A b7534f2bb814
codegraph is a skill published in the GitHub repository linkpranay-ai/context-engineering-protocol (8 stars, last pushed 2d ago), licensed Apache-2.0. It adds 40 tokens to every session and 4,020 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 2026-08-31.
Other skills, from other repositories
sdd
Execute the Liatrio Spec-Driven Development (SDD) workflow when explicitly invoked by the user. NOTE: this skill is NOT intended to be dynamically loaded or automatically triggered; it should only ever be explicitly called by the user.
bcc-plan-spar
BCC align+lock+review PLAN.md for one slice (no product code). Slash: /bcc-plan-spar · chat: bcc:plan-spar · "lock PLAN" · spar the plan. Args: rounds=N (auto-review cap), review=self|subagent|cli|auto|off. Grill until clear enough (no default Q&A quota). Hand off to bcc-clean-cut after human APPROVE.
bcc-throughline
BCC global progress cockpit (plans.md/progress.md/findings.md). Slash: /bcc-throughline · chat: bcc:throughline · "where are we" · reprioritize · resume after /clear. Not for coding or full PLAN grill.
handoff
Guides the Claude Code agent through completing a task, selecting a handoff tier, building a valid completion-card, and performing a clean task handoff.
x-harness-admission
Teach the x-harness admission workflow and completion-card discipline.
bcc-breaking-coding-chaos
BCC main skill: dual-loop coding workflow (throughline → plan-spar → clean-cut) or quick status+next. Slash: /bcc-breaking-coding-chaos · chat: bcc:breaking-coding-chaos · "run BCC" · "BCC status" · "what next BCC". Needs a real idea (1:1 implement). Args: goal text, or status. May pass plan-spar review budget as…